reorganize

src/generate_queries_alternative.py

@@ -1,565 +0,0 @@
-from src.use_llm import main_generate, get_embeddings
-
-from src.questions_queries import *
-import time
-import uuid
-import chromadb
-import spacy
-import numpy as np
-import os
-from src.sparql_query_wikibase import wikibase_properties_id, classes_wikibase
-#os.environ["TOKENIZERS_PARALLELISM"] = "false"
-import pandas as pd
-import spacy
-
-def get_nlp():
-    try:
-        return spacy.load("en_core_web_sm")
-    except OSError:
-        from spacy.cli import download
-        download("en_core_web_sm")
-        return spacy.load("en_core_web_sm")
-nlp = get_nlp()
-
-
-
-# questions_queries_all = [{ "question": education_most_popular_question, "query": education_most_popular_query},
-#                           { "question": how_many_designers_per_fashion_house_question, "query": how_many_designers_per_fashion_house_query},
-#                            {"question": how_many_directors_per_fashion_house_question, "query": how_many_directors_per_fashion_house_query}, 
-#                            {"question": designers_multiple_houses_question, "query":designers_multiple_houses_query }, 
-#                            {"question": award_question, "query": award_question},
-#                            {"question": fashion_houses_with_collections_question, "query": fashion_houses_with_collections_query},
-#                            {"question": popular_year_inception_question, "query": popular_year_inception_query},
-#                            {"question": longest_serving_director_question, "query": longest_serving_director_query},
-#                            {"question": houses_most_collections_question, "query": houses_most_collections_query},
-#                            {"question": collections_sustainability_theme_question, "query": collections_sustainability_theme_query},
-#                            {"question": collections_jeans_question, "query": collections_jeans_query},
-#                            {"question": creative_directors_school_question, "query": creative_directors_school_query}, 
-#                            {"question": fashion_houses_thematic_collection_question, "query": fashion_houses_thematic_collection_query},
-#                             {"question": fashion_house_directors_question, "query": fashion_house_directors_query},
-#                           { "question": designer_fashion_house_directors_question, "query": designer_fashion_house_directors_query},
-#                           { "question": country_designer_question, "query": country_designer_query},
-#                           { "question": designer_order_fashion_collection_question, "query": designer_order_fashion_collection_query},
-#                           { "question": designer_fashion_director_question2, "query": designer_fashion_director_query2},
-#                           { "question": year_designers_birth_question, "query": year_designers_birth_query}
-#                            ]
-questions_queries_all = [{ "question": education_most_popular_question, "query": education_most_popular_query},
-                          { "question": how_many_designers_per_fashion_house_question, "query": how_many_designers_per_fashion_house_query},
-                           {"question": how_many_directors_per_fashion_house_question, "query": how_many_directors_per_fashion_house_query}, 
-                           {"question": designers_multiple_houses_question, "query":designers_multiple_houses_query }, 
-                           {"question": award_question, "query": award_question},
-                           {"question": fashion_houses_with_collections_question, "query": fashion_houses_with_collections_query},
-                           {"question": popular_year_inception_question, "query": popular_year_inception_query},
-                           {"question": longest_serving_director_question, "query": longest_serving_director_query},
-                           {"question": houses_most_collections_question, "query": houses_most_collections_query},
-                           {"question": collections_sustainability_theme_question, "query": collections_sustainability_theme_query},
-                           {"question": collections_jeans_question, "query": collections_jeans_query},
-                           {"question": creative_directors_school_question, "query": creative_directors_school_query}, 
-                           {"question": fashion_houses_thematic_collection_question, "query": fashion_houses_thematic_collection_query},
-                           {"question": fashion_house_directors_question.substitute({ "x": f"{"Chanel"}"}), "query": fashion_house_directors_query.substitute({ "x": f"'{"Chanel"}'"})},
-                          { "question": designer_fashion_house_directors_question.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_fashion_house_directors_query.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          {"question": country_designer_question.substitute({ "x": f"{"Italy"}"}), "query": country_designer_query.substitute({ "x": f"'{"Italy"}'"})},
-                          {  "question": designer_order_fashion_collection_question.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_order_fashion_collection_query.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          {"question": designer_fashion_director_question2.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_fashion_director_query2.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          { "question": year_designers_birth_question.substitute({ "x": 1970}), "query": year_designers_birth_query.substitute({ "x": 1970})},
-                          { "question": fashion_collection_images_question.substitute({ "x": f"{"Chanel"}"}), "query": fashion_collection_images_query.substitute({ "x": f"'{"Chanel"}'"})}]
-
-                           
-
-if os.path.exists("web_app/query_log.json"):
-    other_pairs = pd.read_json("web_app/query_log.json")
-    other_pairs_success = other_pairs[(other_pairs["status"] == "Success") & (other_pairs["feedback"] == "good")]
-    other_pairs_success = other_pairs_success[["question", "query"]]
-    questions_queries_all = questions_queries_all + other_pairs_success.to_dict(orient='records')
-
-    #print only the questions
-    #print([q["question"] for q in questions_queries_all])
-    
-    
-
-
-
-def mask_entities(text, nlp):
-    doc = nlp(text)
-    masked_text = text
-    for ent in doc.ents:
-        masked_text = masked_text.replace(ent.text, "[ENTITY]")
-    return masked_text
-
-
-
-import re
-import spacy
-
-def replace_entity(original_question, to_do_question, query):
-    """
-    Replaces entities in the query using entities from to_do_question while preserving quotation marks.
-    Handles multiple entity replacements and numerical entity replacements.
-    """
-    nlp = spacy.load("en_core_web_sm")
-    
-    original_doc = nlp(original_question)
-    to_do_doc = nlp(to_do_question)
-    
-    # Extract entities from both questions
-    original_entities = [ent.text for ent in original_doc.ents]
-    to_do_entities = [ent.text for ent in to_do_doc.ents]
-    # print("Original Entities:", original_entities)
-    # print("To-Do Entities:", to_do_entities)
-
-    # Create entity mapping
-    entity_mapping = {}
-
-    for orig_ent, new_ent in zip(original_entities, to_do_entities):
-        # Find numbers in each entity
-        orig_numbers = re.findall(r"\d+", orig_ent)
-        new_numbers = re.findall(r"\d+", new_ent)
-
-        if orig_numbers and new_numbers and len(orig_numbers) == len(new_numbers):
-            # If multiple numbers, replace each one
-            for orig_num, new_num in zip(orig_numbers, new_numbers):
-                entity_mapping[orig_num] = new_num
-        else:
-            # Otherwise, replace entire entity
-            entity_mapping[orig_ent] = new_ent
-
-    #print("Entity Mapping:", entity_mapping)
-
-    # Replace entities in the query
-    for orig, new in entity_mapping.items():
-        query = re.sub(rf'("{orig}"|\b{re.escape(orig)}\b)', 
-                       lambda match: f'"{new}"' if match.group(0).startswith('"') else new, 
-                       query)
-
-    return query
-
-
-def capitalize_sentences(sentences):
-    """
-    Ensures that each sentence in a list starts with an uppercase letter.
-    """
-    capitalized_sentences = []
-    
-    for sentence in sentences:
-        sentence = sentence.strip()  # Remove leading/trailing spaces
-        if sentence:  # Check if the sentence is not empty
-            sentence = sentence[0].upper() + sentence[1:]  # Capitalize first letter
-        capitalized_sentences.append(sentence)
-    
-    return capitalized_sentences
-
-
-
-
-
-def similarity_question(question, questions_queries_dictionary, collection, n_results=5, threshold=0.15):
-    """
-    Removes duplicate embeddings and retrieves similar questions.
-    """
-    nlp = spacy.load("en_core_web_sm")  # Load spaCy model for entity recognition
-    
-    original_documents = [questions_queries_dictionary[i]["question"] for i in range(len(questions_queries_dictionary))]
-    masked_documents = [mask_entities(q, nlp) for q in original_documents]
-
-    # Dictionary to store unique embeddings
-    unique_embeddings = {}
-
-    # Store each unique document in the vector embedding database
-    for i, d in enumerate(masked_documents):
-        embedding = get_embeddings(d)[0]
-        
-        # Check if embedding is unique
-        is_duplicate = any(np.allclose(embedding, np.array(e), atol=1e-6) for e in unique_embeddings.values())
-        if not is_duplicate:
-            unique_embeddings[str(i)] = embedding  # Store unique embedding as a list
-            
-            collection.add(
-                ids=[str(i)],
-                embeddings=[embedding],  # Ensure this is a list of lists
-                documents=[d]
-            )
-    
-    # Compute the embedding for the input question
-    masked_question = mask_entities(question, nlp)
-    query_embedding = get_embeddings(d)[0]
-
-    results = collection.query(
-        query_embeddings=[query_embedding],  # Ensure correct format
-        n_results=n_results
-    )
-
-    triples = []
-    for i in range(len(results['documents'][0])):
-        masked_similar_question = results['documents'][0][i]
-        distance = results['distances'][0][i]
-        print(distance)
-        paraphrase = distance < threshold
-        
-        # Find the corresponding original question
-        index_similar_query = masked_documents.index(masked_similar_question)
-        original_similar_question = original_documents[index_similar_query]
-        similar_query = questions_queries_dictionary[index_similar_query]["query"]
-        
-        if paraphrase and "[ENTITY]" in masked_similar_question and "[ENTITY]" in masked_question:
-            to_do_query = replace_entity(original_similar_question, question, similar_query)
-        else:
-            to_do_query = None
-        
-        triples.append((original_similar_question, similar_query, to_do_query))
-    
-    return triples
-
-
-def similarity_question_no_masking(question, questions_queries_dictionary, collection, n_results=5, threshold=0.15):
-    """
-    Removes duplicate embeddings and retrieves similar questions.
-    """
-    
-    original_documents = [questions_queries_dictionary[i]["question"] for i in range(len(questions_queries_dictionary))]
- 
-
-    # Dictionary to store unique embeddings
-    unique_embeddings = {}
-
-    # Store each unique document in the vector embedding database
-    for i, d in enumerate(original_documents):
-        embedding = get_embeddings(d)[0]
-        # Check if embedding is unique
-        is_duplicate = any(np.allclose(embedding, np.array(e), atol=1e-6) for e in unique_embeddings.values())
-        if not is_duplicate:
-            unique_embeddings[str(i)] = embedding  # Store unique embedding as a list
-            
-            collection.add(
-                ids=[str(i)],
-                embeddings=[embedding],  # Ensure this is a list of lists
-                documents=[d]
-            )
-    
-    # Compute the embedding for the input question
-    
-    query_embedding = get_embeddings(question)[0]
-
-    results = collection.query(
-        query_embeddings=[query_embedding],  # Ensure correct format
-        n_results=n_results
-    )
-
-    triples = []
-    for i in range(len(results['documents'][0])):
-        similar_question = results['documents'][0][i]
-        distance = results['distances'][0][i]
-        print(distance)
-        paraphrase = distance < threshold
-        
-        # Find the corresponding original question
-        index_similar_query = original_documents.index(similar_question)
-        original_similar_question = original_documents[index_similar_query]
-        similar_query = questions_queries_dictionary[index_similar_query]["query"]
-        
-        to_do_query = similar_query if paraphrase else None
-        
-        triples.append((original_similar_question, similar_query, to_do_query))
-    
-    return triples
-
-def select_dict(dict, keys):
-    return {k: dict[k] for k in keys if k in dict}
-
-def prompt_template(to_do_question,triples_examples,wikibase_properties_id,how_many_examples = 1, ):
-
-    questions = [triples_examples[i][0] for i in range(len(triples_examples))][:how_many_examples]
-    print("EXAMPLE QUESTION(s): ",questions)
-
-    classes_wikibase_selection = select_dict(classes_wikibase, ["fashion house", "fashion designer"])
-    general_properties = select_dict(wikibase_properties_id, ["instance of", "reference URL", "start time", "end time", "occupation title", "point in time", "official website"])
-    general_properties["rdfs:label"] = "rdfs:label"
-    designer_properties = select_dict(wikibase_properties_id, ["employer", "educated at", "work location", "award received", "date of birth", "date of death", "place of birth",  "country of citizenship", "occupation", "sex or gender"])
-    fashion_house_properties = select_dict(wikibase_properties_id, ["inception","headquarters location", "parent organization", "founded by","owned by", "industry", "country", "total revenue", "designer employed", "fashion collection", "description of fashion collection","image of fashion collection"])
-    fashion_collection_properties = select_dict(wikibase_properties_id, ["fashion show category", "fashion show location", "fashion season"])
-    qualifier_properties = select_dict(wikibase_properties_id, ["start time", "end time", "occupation title", "point in time","description of fashion collection","image of fashion collection"])
-    
-    prompt = f"""You are an expert in translating natural language questions into SPARQL queries for FashionDB - a knwoledge graph about Fashion.
-    I provide you with the ontology of FashionDB. The properties are stored in a dictionary as property_label: property_id. The classes are stored in a dictionary as class_label: class_id.
-    General Properties: {general_properties}, Fashion Designer Properties: {designer_properties}, Fashion House Properties: {fashion_house_properties}, Fashion Collection Properties: {fashion_collection_properties}. 
-    In particular the following properties are always qualifiers thus their prefix is always pq: {qualifier_properties}.
-    Classes: {classes_wikibase_selection}.
-    Remember to use the entities presented in Natural language question to translate , when generating the corresponding SPARQL query.
-    
-    
-    I provide you with example."""
-    for i in range(len(questions)):
-        prompt += f""" Example question: {triples_examples[i][0]}
-                    Corresponding SPARQL query:{triples_examples[i][1]} """
-    prompt += f""" Question to translate to SPARQL: {to_do_question}
-    Remember that the use case is FASHION: if there is a mispelling of a fashion designer or house, you can adjust it according to your knowledge of fashion. Example: "balenciaho" should be "Balenciaga". 
-                Your generated corresponding SPARQL query: """
-
-    return prompt
-
-
-def prompt_template_gemma2(to_do_question, triples_examples, wikibase_properties_id, how_many_examples=1):
-    questions = [triples_examples[i][0] for i in range(len(triples_examples))][:how_many_examples]
-    print("EXAMPLE QUESTION(s): ",questions)
-    classes_wikibase_selection = select_dict(classes_wikibase, ["fashion house", "fashion designer"])
-    general_properties = select_dict(wikibase_properties_id, ["instance of", "reference URL", "start time", "end time", "occupation title", "point in time", "official website"])
-    general_properties["rdfs:label"] = "rdfs:label"
-    designer_properties = select_dict(wikibase_properties_id, ["employer", "educated at", "work location", "award received", "date of birth", "date of death", "place of birth", "country of citizenship", "occupation", "sex or gender"])
-    fashion_house_properties = select_dict(wikibase_properties_id, ["inception", "headquarters location", "parent organization", "founded by", "owned by", "industry", "country", "total revenue", "designer employed", "fashion collection", "description of fashion collection", "image of fashion collection"])
-    fashion_collection_properties = select_dict(wikibase_properties_id, ["fashion show category", "fashion show location", "fashion season"])
-    qualifier_properties = select_dict(wikibase_properties_id, ["start time", "end time", "occupation title", "point in time", "description of fashion collection", "image of fashion collection"])
-    
-    prompt = f"""
-    You are an expert in translating natural language fashion-related questions into **SPARQL queries** for **FashionDB**, a knowledge graph about fashion.
-
-    ---
-    ## **FashionDB Ontology**
-    - **Classes**: {classes_wikibase_selection}
-    - **General Properties**: {general_properties}
-    - **Fashion Designer Properties**: {designer_properties}
-    - **Fashion House Properties**: {fashion_house_properties}
-    - **Fashion Collection Properties**: {fashion_collection_properties}
-    - **Qualifier Properties** (always prefixed with `pq:`): {qualifier_properties}
-
-    ---
-    ## **Instructions**
-    - **Fix misspellings** of fashion brands and designers before generating the query.
-        - Example: "Guxci" → **"Gucci"**, "Balenciaho" → **"Balenciaga"**.
-    - If a brand or designer **isn't recognized**, **make a reasonable correction** based on common fashion knowledge.
-    - Handle **abstract or conceptual fashion questions**, such as:
-        - "Which fashion houses have had the most influence in the 20th century?"
-        - "What are the key design trends in haute couture from the 1990s?"
-    - **Always return a valid SPARQL query** using the provided ontology.
-
-    ---
-    ## **Example(s)**
-    """
-    
-    for i in range(len(questions)):
-        prompt += f"""
-        **Example {i+1}**  
-        - **Question**: {triples_examples[i][0]}  
-        - **SPARQL Query**:  
-        ```sparql
-        {triples_examples[i][1]}
-        ```
-        """
-
-    prompt += f"""
-    ---
-    ## **Your Task**
-    **Question**: {to_do_question}
-
-    **SPARQL Query:**  
-    ```sparql
-    """
-
-    return prompt
-
-def prompt_template_gpt4o_mini(to_do_question, triples_examples, wikibase_properties_id, how_many_examples=1):
-    questions = [triples_examples[i][0] for i in range(len(triples_examples))][:how_many_examples]
-    
-    classes_wikibase_selection = select_dict(classes_wikibase, ["fashion house", "fashion designer"])
-    general_properties = select_dict(wikibase_properties_id, ["instance of", "reference URL", "start time", "end time", "occupation title", "point in time", "official website"])
-    general_properties["rdfs:label"] = "rdfs:label"
-    designer_properties = select_dict(wikibase_properties_id, ["employer", "educated at", "work location", "award received", "date of birth", "date of death", "place of birth", "country of citizenship", "occupation", "sex or gender"])
-    fashion_house_properties = select_dict(wikibase_properties_id, ["inception", "headquarters location", "parent organization", "founded by", "owned by", "industry", "country", "total revenue", "designer employed", "fashion collection", "description of fashion collection", "image of fashion collection"])
-    fashion_collection_properties = select_dict(wikibase_properties_id, ["fashion show category", "fashion show location", "fashion season"])
-    qualifier_properties = select_dict(wikibase_properties_id, ["start time", "end time", "occupation title", "point in time", "description of fashion collection", "image of fashion collection"])
-    
-    prompt = f"""
-    You are a **SPARQL expert** specializing in **FashionDB**, a knowledge graph about fashion.
-
-    ### **Your Task**
-    - Translate the given **natural language question** into a **valid SPARQL query**.
-    - **Fix spelling mistakes** of fashion brands and designers.
-        - Example: "Guxci" → "Gucci", "Balenciaho" → "Balenciaga".
-    - If a brand or designer isn't recognized, **guess the correct name** based on fashion industry knowledge.
-    - Support **abstract fashion questions**, such as:
-        - "How did Dior's designs evolve over the decades?"
-        - "Which fashion houses had the biggest impact on 21st-century streetwear?"
-    - Your **SPARQL query must use the correct ontology**.
-
-    ---
-    ### **FashionDB Ontology**
-    - **Classes**: {classes_wikibase_selection}
-    - **General Properties**: {general_properties}
-    - **Fashion Designer Properties**: {designer_properties}
-    - **Fashion House Properties**: {fashion_house_properties}
-    - **Fashion Collection Properties**: {fashion_collection_properties}
-    - **Qualifier Properties (always prefixed with `pq:`)**: {qualifier_properties}
-
-    ---
-    ### **Example(s)**
-    """
-    
-    for i in range(len(questions)):
-        prompt += f"""
-        **Example {i+1}**  
-        - **Question**: {triples_examples[i][0]}  
-        - **SPARQL Query**:  
-        ```sparql
-        {triples_examples[i][1]}
-        ```
-        """
-
-    prompt += f"""
-    ---
-    ### **Now Translate This Question**
-    **Question**: {to_do_question}
-
-    **SPARQL Query:**  
-    ```sparql
-    """
-
-    return prompt
-
-
-
-#validate
-def replace_last_occurrence(s, pattern, replacement):
-    pos = s.rfind(pattern)  # Find the last occurrence of the pattern
-    if pos != -1:
-        return s[:pos] + s[pos:].replace(pattern, replacement, 1)
-        
-def validation_query(sparql_query):
-    if sparql_query.startswith("sparql"):
-        sparql_query = sparql_query[6:]
-    #if last character is \n remove it 
-    while sparql_query[-1] == "\n" or sparql_query[-1] == " ":
-        sparql_query = sparql_query[:-1]
-
-    if sparql_query[-1] == ".":
-        sparql_query = sparql_query[:-1]
-    sparql_query = sparql_query.encode().decode('unicode_escape')
-    sparql_query = sparql_query.replace("wdt", "wbt")
-    if "SERVICE" not in sparql_query:
-        sparql_query = replace_last_occurrence(sparql_query, "}", "SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } \n }")
-    
-    return sparql_query
-
-
-def safe_get_results(query, max_retries=3):
-    """
-    Safely executes a SPARQL query, handling HTTP errors gracefully.
-    
-    Parameters:
-    - query (str): The SPARQL query to execute.
-    - max_retries (int): Number of retries before failing.
-    
-    Returns:
-    - DataFrame: Query results, or an empty DataFrame if the query fails.
-    """
-    for attempt in range(max_retries):
-        try:
-            return get_results_to_df(query)  # Attempt to execute the query
-        except requests.exceptions.HTTPError as e:
-            print(f"Attempt {attempt + 1}: Query failed with HTTPError {e}")
-            time.sleep(2)  # Wait before retrying
-        except Exception as e:
-            print(f"Attempt {attempt + 1}: Unexpected error {e}")
-            time.sleep(2)
-
-    print("All attempts failed. Returning empty DataFrame.")
-    return pd.DataFrame()  # Return empty DataFrame if all retries fail
-
-
-
-def correction_question_prompt(to_do_question):
-    correction_prompt = f"""
-    You are an expert in **fashion brand and designer names**.
-    Your task is to **correct misspellings** in the given question while keeping its original meaning.
-    If you recognize a fashion-related name that is misspelled, **fix it**.
-    If nothing is wrong, generate the Question to Correct.
-    Don't generate **.
-
-    ### **Examples**
-    - "Who founded Guxci?" → "Who founded Gucci?"
-    - "What is balenciaho famous for?" → "What is Balenciaga famous for?"
-    - "Who is the head designer of gucxi?" → "Who is the head designer of Gucci?"
-
-    ### **Question to Correct**
-    {to_do_question}
-
-    ### **Corrected Version**
-    """
-    return correction_prompt
-
-def initialize_collection():
-    # Initialize ChromaDB client
-    client = chromadb.Client()
-    # If the collection already exists, delete it to start fresh.
-    try:
-        client.delete_collection(name="docs")  # Delete the existing collection
-    except:
-        pass  
-    # Re-create the collection for each query
-    collection = client.create_collection(name="docs")
-    return collection
-
-
-
-
-def main_generate_queries(to_do_question):
-    
-    # # Initialize ChromaDB client
-    # client = chromadb.Client()
-    # # If the collection already exists, delete it to start fresh.
-    # try:
-    #     client.delete_collection(name="docs")  # Delete the existing collection
-    # except:
-    #     pass  
-    # # Re-create the collection for each query
-    # collection = client.create_collection(name="docs")
-    collection = initialize_collection()
-    triples_examples = similarity_question(to_do_question, questions_queries_all, collection)
-    if triples_examples[0][2] is not None:
-        print("it's a paraphrase :)")
-        sparql_query = triples_examples[0][2]
-        print(triples_examples[0][0])
-        result_query = safe_get_results(sparql_query)
-        if result_query.empty:
-            to_do_question = main_generate(correction_question_prompt(to_do_question), "gemma2", "You have to fix the mispellings of the Question to Correct")
-            print(to_do_question)
-            sparql_query = replace_entity(triples_examples[0][0], to_do_question, triples_examples[0][1])
-            result_query = safe_get_results(sparql_query)
-        print(sparql_query)
-        if not result_query.empty:
-            return result_query.to_dict(orient='records'), sparql_query
-
-    prompt = prompt_template_gemma2(to_do_question, triples_examples, wikibase_properties_id, how_many_examples=1)
-
-    sparql_query = main_generate(prompt, "gemma2", "You are a natural language to SPARQL language translator. Do only generate the SPARQL query, nothing else.")
-    sparql_query = validation_query(sparql_query)
-    result_query = safe_get_results(sparql_query)
-
-    print(sparql_query)
-    if result_query.empty:
-        to_do_question = main_generate(correction_question_prompt(to_do_question), "gemma2", "You have to fix the mispellings of the Question to Correct")
-        print(to_do_question)
-        prompt = prompt_template_gemma2(to_do_question, triples_examples, wikibase_properties_id, how_many_examples=2)
-        sparql_query = main_generate(prompt, "gemma2", "You are a natural language to SPARQL language translator. Do only generate the SPARQL query, nothing else.")
-        sparql_query = validation_query(sparql_query)
-        result_query = safe_get_results(sparql_query)
-        if result_query.empty:
-            new_collection = initialize_collection()
-            triples_examples_no_masked = similarity_question_no_masking(to_do_question, questions_queries_all, new_collection)
-            prompt = prompt_template_gemma2(to_do_question, triples_examples_no_masked, wikibase_properties_id, how_many_examples=2)
-            sparql_query = main_generate(prompt, "gemma2", "You are a natural language to SPARQL language translator. Do only generate the SPARQL query, nothing else.")
-            sparql_query = validation_query(sparql_query)
-            result_query = safe_get_results(sparql_query)
-            print(sparql_query)
-            if result_query.empty:
-                text_generated =  main_generate(to_do_question, "gemma2", "You are an expert in fashion. Just provide the answer to the question.")
-                return text_generated, sparql_query
-
-    print(sparql_query)
-    print(result_query)
-    
-    return result_query.to_dict(orient='records'), sparql_query
-
-# #main("What is the inception of Chanel?")
-# if __name__ == "__main__":
-#     #main("Which fashion designers being creative directors were born in Italy?")
-#     #main_generate_queries("Which fashion houses had collections with jeans in their descriptions and how many of the collections have jeans?")
-#     main_generate_queries("Which designers were born in 1970?")
-

src/questions_queries.py

@@ -1,1594 +0,0 @@
-# import sys
-# import os
-# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-# from src.sparql_query_wikibase import *
-
-
-
-# fashion_collection_images_question = "Images of collections produced by Chanel"
-# fashion_collection_images_query = """
-# PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?statement ?fashion_collectionLabel ?image_url ?reference_URL 
-#   ?fashion_seasonLabel (YEAR(?fashion_year) AS ?year) ?categoryLabel WHERE {
-#     { ?fashion_house  rdfs:label "Chanel"@en.}  # Match label
-#   UNION
-#   { ?fashion_house  skos:altLabel "Chanel"@en. }  # Match alias   
-                                           
-  
-#   ?fashion_house p:P31 ?statement.
-#   ?statement ps:P31 ?fashion_collection.
-#   OPTIONAL { ?fashion_collection wbt:P32 ?fashion_season. }
-#   OPTIONAL {?fashion_collection wbt:P28 ?fashion_year.}
-#   OPTIONAL {?fashion_collection wbt:P38 ?category.}
-  
-#   OPTIONAL {
-#     ?statement pq:P35 ?image_url.
-#   }
-
-                                                
-#   OPTIONAL {
-#     ?statement prov:wasDerivedFrom ?reference.
-#     ?reference pr:P24 ?reference_URL. 
-#   }
-
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } }
-#                                            """
-
-# year_designers_birth_question = "Which designers were born in 1963?"
-# year_designers_birth_query = """
-# PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-# PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
-
-# SELECT ?designerLabel ?birthdate  WHERE {
-#   ?designer wbt:P3 ?birthdate .
-#   FILTER (YEAR(?birthdate) = 1963)
-
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# } """
-
-# fashion_house_directors_question = "Which designers have been creative directors of Chanel ?"
-# fashion_house_directors_query = """
-# PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT 
-# ?creative_directorLabel (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year)
-#   (YEAR(?end_time) AS ?end_year) ?title ?statement ?reference_URL_source
-# WHERE {
-#   # Fashion house identified by brand name
-#   ?fashion_house wbt:P2 wb:Q1.                                       
-#     { ?fashion_house rdfs:label "Chanel"@en.}  # Match label
-#   UNION
-#   { ?fashion_house skos:altLabel "Chanel"@en. }  # Match alias   
-  
-#   # Link through designer employed (P29) property for the fashion house  - the property for designer employment is P10
-#   {
-#     ?fashion_house p:P29 ?statement.
-#     ?statement ps:P29 ?creative_director.
-
-#     # Optional qualifiers for P29 regarding the time frame
-#     OPTIONAL { ?statement pq:P15 ?start_time. }
-#     OPTIONAL { ?statement pq:P28 ?point_time. } 
-#     OPTIONAL { ?statement pq:P16 ?end_time. }
-#     OPTIONAL { ?statement pq:P25 ?title. }
-#     OPTIONAL { ?fashion_house wbt:P11 ?inception. } 
-
-    
-#     # Retrieve references for P29
-#     OPTIONAL {
-#       ?statement prov:wasDerivedFrom ?reference.
-#       ?reference pr:P24 ?reference_URL.
-#       BIND(
-#         IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-#       )
-#       BIND(
-#         IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-#       )
-#     }
-#   }
-#   UNION
-#   # Link through founded_by (P14) property for the fashion house
-#   {
-#     ?fashion_house p:P14 ?statement.
-#     ?statement ps:P14 ?creative_director.
-
-#     # Optional qualifiers for P14 (start_year as inception)
-#     OPTIONAL { ?statement pq:P15 ?start_time. }
-#     OPTIONAL { ?statement pq:P28 ?point_time. } 
-#     OPTIONAL { ?statement pq:P16 ?end_time. }
-#     OPTIONAL { ?statement pq:P25 ?title. }
- 
-    
-#     # Retrieve references for P14
-#     OPTIONAL {
-#       ?statement prov:wasDerivedFrom ?reference.
-#       ?reference pr:P24 ?reference_URL.
-#       BIND(
-#         IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-#       )
-#       BIND(
-#         IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-#       )
-#     }
-#   }
-
-#   # Filter for relevant titles (creative director, founder, etc.) to retrieve relevant employment as creative director
-#   FILTER (
-#     REGEX(?title, "director", "i") || 
-#     REGEX(?title, "head designer", "i") ||
-#     REGEX(?title, "chief", "i") ||
-#     REGEX(?title, "founder", "i")                                   
-#   )
-#   FILTER(?reference_URL_source != "")
-#   # Retrieve labels
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }   ORDER BY ?start_year """
-
-
-
-# designer_fashion_house_directors_question = "In which fashion house did Alessandro Michele work as a creative director?"
-# designer_fashion_house_directors_query = """  
-# PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?fashion_houseLabel 
-#        (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year) 
-#        (YEAR(?end_time) AS ?end_year) 
-#        ?title ?reference_URL_source ?statement 
-# WHERE {
-#    # Designer linked to fashion house through roles or founded_by
-#     ?fashion_designer wbt:P2 wb:Q5.                                              
-#      { ?fashion_designer  rdfs:label "Alessandro Michele"@en.}  # Match label
-#   UNION
-#   { ?fashion_designer  skos:altLabel "Alessandro Michele"@en. }  # Match alias   
-   
-#    # Link through employer (P10)
-#    ?fashion_designer p:P10 ?statement.
-#    ?statement ps:P10 ?fashion_house.
-  
-#    # Optional start and end time qualifiers for roles
-#    OPTIONAL { ?statement pq:P15 ?start_time. }
-#    OPTIONAL { ?statement pq:P28 ?point_time. } 
-#    OPTIONAL { ?statement pq:P16 ?end_time. }
-#    OPTIONAL { ?statement pq:P25 ?title. }
-
-#    # Inception as fallback for start year
-#    OPTIONAL { ?fashion_house wbt:P11 ?inception. }
-
-#    # Filter for relevant roles (director, founder, etc.)
-#    FILTER (
-#        REGEX(?title, "director", "i") || 
-#        REGEX(?title, "head designer", "i") ||
-#        REGEX(?title, "chief","i") ||
-#        REGEX(?title, "founder","i")                                   
-#    )
-  
-#    # Retrieve references for the statement
-#     OPTIONAL {
-#       ?statement prov:wasDerivedFrom ?reference.
-#       ?reference pr:P24 ?reference_URL.
-#       BIND(
-#         IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-#       )
-#       BIND(
-#         IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-#       )
-#     }
-#   FILTER(?reference_URL_source != "")
-
-#    SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-# }
-# """
-
-
-# designer_fashion_director_question2 = "In which fashion house did Alessandro Michele work as a creative director?"
-# designer_fashion_director_query2 = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT 
-# ?fashion_houseLabel ?creative_directorLabel (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year)  (YEAR(?end_time) AS ?end_year) ?title ?statement ?reference_URL_source
-# WHERE {
-#   # Fashion designer identified by name
-#   ?creative_director wbt:P2 wb:Q5.                                       
-#     { ?creative_director  rdfs:label "Alessandro Michele"@en.}  # Match label
-#   UNION
-#   { ?creative_director  skos:altLabel "Alessandro Michele"@en. }  # Match alias   
-
-#   # Link through designer employed (P29) property for the fashion house  - the property for designer employment is P30
-#   {
-#     ?fashion_house p:P29 ?statement.
-#     ?statement ps:P29 ?creative_director.
-
-#     # Optional qualifiers for P29 regarding the time frame
-#     OPTIONAL { ?statement pq:P15 ?start_time. }
-#     OPTIONAL { ?statement pq:P28 ?point_time. } 
-#     OPTIONAL { ?statement pq:P16 ?end_time. }
-#     OPTIONAL { ?statement pq:P25 ?title. }
-#     OPTIONAL { ?fashion_house wbt:P11 ?inception. } 
-
-    
-#     # Retrieve references for P29
-#     OPTIONAL {
-#       ?statement prov:wasDerivedFrom ?reference.
-#       ?reference pr:P24 ?reference_URL.
-#       BIND(
-#         IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-#       )
-#       BIND(
-#         IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-#       )
-#     }
-#   }
-#   FILTER (
-#     REGEX(?title, "director", "i") || 
-#     REGEX(?title, "head designer", "i") ||
-#     REGEX(?title, "chief", "i") ||
-#     REGEX(?title, "founder", "i")                                   
-#   )
-#   FILTER(?reference_URL_source != "")
-
-#   # Retrieve labels
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }   ORDER BY ?start_year """
-
-# country_designer_question = "Which designers are from Italy ?"
-# country_designer_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?fashion_designerLabel   (SAMPLE(?statement) AS ?statement)  
-#   (SAMPLE(?reference_URL) AS ?reference_URL) WHERE {
-#   # Restrict to designers who are instances of fashion designer (Q5)
-
-#   ?fashion_designer wbt:P2 wb:Q5.
-
-#   # Access the statement for the country relationship
-#   ?fashion_designer p:P6 ?statement.
-#   ?statement ps:P6 ?country.
-#     { ?country  rdfs:label "Italy"@en.}  # Match label
-#   UNION
-#   { ?country skos:altLabel "Italy"@en. }  # Match alias   
-
-#   # Retrieve references from the statement
-#   OPTIONAL {
-#     ?statement prov:wasDerivedFrom ?reference.
-#     ?reference pr:P24 ?reference_URL.
-#   }
-
-#   # Retrieve labels for the fashion designer
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-# } 
-# GROUP BY ?fashion_designerLabel 
-# ORDER BY ?fashion_designerLabel """
-
-# country_designer_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?fashion_designerLabel (SAMPLE(?reference_URL) AS ?reference_URL) {
-#   # Restrict to designers who are instances of fashion designer (Q5)
-
-#   ?fashion_designer wbt:P2 wb:Q5.
-#   ?fashion_designer wbt:P6 ?country.
-#   ?fashion_designer p:P6 ?statement.
-#     { ?country  rdfs:label "Italy"@en.}  # Match label
-#   UNION
-#   { ?country  skos:altLabel "Italy"@en. }  # Match alias   
- 
-
-#   # Retrieve references from the statement
-#   OPTIONAL {
-#     ?statement prov:wasDerivedFrom ?reference.
-#     ?reference pr:P24 ?reference_URL.
-#   }
-
-#   # Retrieve labels for the fashion designer
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-# } 
-# GROUP BY ?fashion_designerLabel 
-# ORDER BY ?fashion_designerLabel  """
-
-
-# designer_order_fashion_collection_question = "Which fashion collections did Alessandro Michele work on?"
-# designer_order_fashion_collection_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?fashion_houseLabel 
-#        (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year) 
-#        (YEAR(?end_time) AS ?end_year) 
-#        ?title ?reference_URL_source ?statement ?fashion_collectionLabel ?image_url 
-#       (YEAR(?fashion_year) as ?year ) ?fashion_seasonLabel ?categoryLabel WHERE {
-#    # Designer linked to fashion house through roles or founded_by
-#    ?fashion_designer wbt:P2 wb:Q5. 
-#   { ?fashion_designer  rdfs:label "Alessandro Michele"@en.}  # Match label
-#   UNION
-#   { ?fashion_designer  skos:altLabel "Alessandro Michele"@en. }  # Match alias                                               
-                                            
-   
-#    # Link through "creative director" or other roles
-#    ?fashion_designer p:P10 ?statement_designer.
-#    ?statement_designer ps:P10 ?fashion_house.
-#    OPTIONAL { ?statement_designer pq:P15 ?start_time. }
-#    OPTIONAL { ?statement_designer pq:P28 ?point_time. } 
-#    OPTIONAL { ?statement_designer pq:P16 ?end_time. }
-#    OPTIONAL { ?statement_designer pq:P25 ?title. }   
-#     OPTIONAL { ?fashion_house wbt:P11 ?inception. }    
-#     FILTER (
-#        REGEX(?title, "director", "i") || 
-#        REGEX(?title, "head designer", "i") ||
-#        REGEX(?title, "chief","i") ||
-#        REGEX(?title, "founder","i")                                   
-#    )       
-  
-#     ?fashion_house p:P31 ?statement.
-#   ?statement ps:P31 ?fashion_collection.
-#   OPTIONAL {?fashion_collection wbt:P28 ?fashion_year.}
-#     OPTIONAL { ?fashion_collection wbt:P32 ?fashion_season. }
-#   OPTIONAL {?fashion_collection wbt:P38 ?category.}
-  
-#   OPTIONAL {?statement pq:P35 ?image_url.}
-#   OPTIONAL {?statement pq:P37 ?fashion_date.}
-  
- 
-#     # Use the adjusted fashion year in the FILTER condition
-# FILTER(
-#     (YEAR(?start_time) <= YEAR(?fashion_year) && (!BOUND(?end_time) || YEAR(?end_time) >= YEAR(?fashion_year))) ||
-#     (YEAR(?point_time) <= YEAR(?fashion_year)) ||
-#     (REGEX(?title, "founder", "i") && YEAR(?inception) <= YEAR(?fashion_year))
-# )
-
-#    # Retrieve references for the statement
-#    OPTIONAL {
-#      ?statement prov:wasDerivedFrom ?reference.
-#      ?reference pr:P24 ?reference_URL_source.
-#    }
-
-#    SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-# } ORDER BY ?year """
-
-
-# education_most_popular_question = "What are the most popular educational institutions for fashion designers?"
-# education_most_popular_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?schoolLabel (COUNT(?fashion_designer) AS ?designer_count) WHERE {
-#   # Retrieve all fashion designers and their schools
-#   ?fashion_designer wbt:P9 ?school.
-  
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }
-# GROUP BY ?schoolLabel
-# ORDER BY DESC(?designer_count) """
-
-# brands_founded_question = Template("Which fashion houses were founded in $x")
-# brands_founded_query = Template(""" PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-
-# SELECT ?fashion_houseLabel ?inception 
-# WHERE {
-#     ?fashion_house wbt:P2 wb:Q1.
-#     ?fashion_house wbt:P11 ?inception.
-
-    
-#     FILTER(YEAR(?inception) = $x)
-    
-#     SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }""")
-
-# how_many_directors_per_fashion_house_question = "How many designers have worked for each fashion house as creative directors?"
-# how_many_directors_per_fashion_house_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?fashion_houseLabel (COUNT(DISTINCT ?fashion_designer) AS ?creative_director_count) WHERE {
-#   # Match fashion houses linked to designers via employer property
-#   ?fashion_designer p:P10 ?statement.
-#   ?statement ps:P10 ?fashion_house.
-  
-#   # Filter for roles related to creative direction
-#   OPTIONAL { ?statement pq:P25 ?title. }
-#   FILTER (
-#     REGEX(?title, "creative director", "i") || 
-#     REGEX(?title, "head designer", "i") ||
-#     REGEX(?title, "chief", "i") ||
-#     REGEX(?title, "founder", "i")
-#   )
-  
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }
-# GROUP BY ?fashion_houseLabel
-# ORDER BY DESC(?creative_director_count)
-# """
-
-# how_many_designers_per_fashion_house_question = "How many designers have worked for each fashion house?"
-# how_many_designers_per_fashion_house_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?fashion_houseLabel (COUNT(?designer) AS ?designer_count) WHERE {
-#   ?fashion_house wbt:P29 ?designer .
-
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }
-# GROUP BY ?fashion_houseLabel
-# ORDER BY DESC(?designer_count) """
-
-
-# designers_multiple_houses_question = "Which designers have worked for more than one fashion house?"
-# designers_multiple_houses_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?designerLabel WHERE {
-#   ?designer wbt:P10 ?house .
-#   ?designer wbt:P10 ?anotherHouse .
-# SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } 
-#  }
-# GROUP BY ?designerLabel
-# HAVING (COUNT(?house) > 1)"""
-
-# award_question = "Which designers won which awards?"
-# award_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?designerLabel ?awardLabel WHERE {
-#   ?designer wbt:P18 ?award .
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }"""
-
-# fashion_houses_with_collections_question ="Which distinct fashion houses have at least a fashion collection?"
-# fashion_houses_with_collections_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT DISTINCT ?houseLabel WHERE {
-#   ?house wbt:P31 ?collection .
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# } """
-
-# popular_year_inception_question = "In which years were the most fashion houses founded?"
-# popular_year_inception_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-
-# SELECT ?year_inception (COUNT(DISTINCT ?fashion_house) AS ?count)
-# WHERE {
-#   ?fashion_house wbt:P11 ?year.
-#   BIND(YEAR(?year) AS ?year_inception)
-# }
-# GROUP BY ?year_inception
-# ORDER BY DESC(?count) """
-
-
-# longest_serving_director_question = "Which fashion houses have had the longest-serving creative director?"
-# longest_serving_director_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>
-# PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
-# PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
-
-# SELECT ?fashion_houseLabel ?duration ?designerLabel
-# WHERE {
-#   ?fashion_house p:P29 ?statement.
-#   ?statement ps:P29 ?designer.
-#   OPTIONAL { ?statement pq:P15 ?startTime. }
-#   OPTIONAL { ?statement pq:P16 ?endTime. }
-
-#   # Ensure we only calculate when both dates exist
-#   FILTER(BOUND(?startTime) && BOUND(?endTime))
-
-#   # Compute duration safely
-#   BIND(YEAR(?endTime) - YEAR(?startTime) AS ?duration)
-
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-# }
-# ORDER BY DESC(?duration)"""
-
-# houses_most_collections_question = "Which fashion houses have the most collections recorded in the database?"
-# houses_most_collections_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?fashionHouseLabel (COUNT(?collection) AS ?collection_count) WHERE {
-#   ?fashionHouse wbt:P31 ?collection .
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }
-# GROUP BY ?fashionHouseLabel
-# ORDER BY DESC(?collection_count)"""
-
-
-# collections_sustainability_theme_question = "Which collections include sustainability as a key theme?"
-# collections_sustainability_theme_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?collectionLabel ?fashion_houseLabel ?reference_URL WHERE {
-#   ?fashion_house p:P31 ?statement.
-#   ?statement ps:P31 ?collection.
-#   ?statement pq:P34 ?description .
-#   FILTER (regex(?description, "sustainability", "i"))
-#      # Retrieve references for the statement
-#    OPTIONAL {
-#      ?statement prov:wasDerivedFrom ?reference.
-#      ?reference pr:P24 ?reference_URL.
-#    }
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }  """ 
-
-
-# collections_jeans_question = "Which collections showcase products made with jeans?"
-# collections_jeans_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?collectionLabel ?fashion_houseLabel ?reference_URL WHERE {
-#   ?fashion_house p:P31 ?statement.
-#   ?statement ps:P31 ?collection.
-#   ?statement pq:P34 ?description .
-#   FILTER (regex(?description, "jeans", "i"))
-#      # Retrieve references for the statement
-#    OPTIONAL {
-#      ?statement prov:wasDerivedFrom ?reference.
-#      ?reference pr:P24 ?reference_URL.
-#    }
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# }  """ 
-
-
-# creative_directors_school_question = "To which school went most designers who later became creative directors of fashion houses?"
-# creative_directors_school_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?schoolLabel (COUNT(DISTINCT ?designer) AS ?designer_count) WHERE {
-#   ?designer p:P10 ?fashion_house .
-
-#      OPTIONAL { ?fashion_house pq:P25 ?title. }
-#   FILTER (
-#     REGEX(?title, "creative director", "i") || 
-#     REGEX(?title, "head designer", "i") ||
-#     REGEX(?title, "chief", "i") ||
-#     REGEX(?title, "founder", "i")
-#   )
-  
-#   OPTIONAL { ?designer wbt:P9 ?school . }
-# SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } 
-#  }
-# GROUP BY ?schoolLabel
-# ORDER BY DESC(?designer_count)"""
-
-# fashion_houses_thematic_collection_question = "Which fashion houses had collections with a sustainability theme, and how many of them?"
-# fashion_houses_thematic_collection_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-# PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-# PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-# PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-# PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-# PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-# PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-# SELECT ?fashion_houseLabel (COUNT(DISTINCT ?collection) AS ?collection_count) WHERE {
-#   ?fashion_house p:P31 ?statement.
-#   ?statement ps:P31 ?collection.
-#   ?statement pq:P34 ?description .
-#   FILTER (regex(?description, "sustainability", "i"))
-#      # Retrieve references for the statement
-#    OPTIONAL {
-#      ?statement prov:wasDerivedFrom ?reference.
-#      ?reference pr:P24 ?reference_URL.
-#    }
-#   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-# } GROUP BY ?fashion_houseLabel
-# ORDER BY DESC(?collection_count) """
-
-
-# questions_queries_dict = [ { "question": education_most_popular_question, "query": education_most_popular_query},
-#                           { "question": how_many_designers_per_fashion_house_question, "query": how_many_designers_per_fashion_house_query},
-#                            {"question": how_many_directors_per_fashion_house_question, "query": how_many_directors_per_fashion_house_query}, 
-#                            {"question": designers_multiple_houses_question, "query":designers_multiple_houses_query }, 
-#                            {"question": award_question, "query": award_question},
-#                            {"question": fashion_houses_with_collections_question, "query": fashion_houses_with_collections_query},
-#                            {"question": popular_year_inception_question, "query": popular_year_inception_query},
-#                            {"question": longest_serving_director_question, "query": longest_serving_director_query},
-#                            {"question": houses_most_collections_question, "query": houses_most_collections_query},
-#                            {"question": collections_sustainability_theme_question, "query": collections_sustainability_theme_query},
-#                            ]
-
-
-# questions_queries_dict_entity = [{"question": fashion_house_directors_question, "query": fashion_house_directors_query},
-#                           { "question": designer_fashion_house_directors_question, "query": designer_fashion_house_directors_query},
-#                           { "question": country_designer_question, "query": country_designer_query},
-#                           { "question": designer_order_fashion_collection_question, "query": designer_order_fashion_collection_query},
-#                           { "question": designer_fashion_director_question2, "query": designer_fashion_director_query2},
-#                           { "question": year_designers_birth_question, "query": year_designers_birth_query}]
-
-
-# # Designers
-# designers_born_in_year_question = "Which designers were born in year $x?"
-# creative_directors_of_house_question = "Which designers have been creative directors of $x?"
-# designers_multiple_houses_question = "Which designers have worked for more than one fashion house?"
-# educated_at_school_question = "Which designers were educated at $x fashion school?"
-# won_award_question = "Which designers have won the $x award?"
-# launched_own_label_question = "Which designers launched their own fashion label?"
-# worked_at_x_between_years_question = "Which designers worked at $x between year $y and $z?"
-# designers_multiple_collaborations_question = "Which designers have collaborated with multiple fashion houses?"
-# designers_lived_in_city_question = "Which designers have lived in $x city?"
-# designers_active_decade_question = "Which designers were active during the $x decade?"
-
-# # Fashion Houses
-# designers_worked_for_house_question = "Which designers have worked for fashion house $x?"
-# creative_directors_per_house_question = "How many designers have worked for each fashion house as creative directors?"
-# houses_founded_in_year_question = "Which fashion houses were founded in year $x?"
-# houses_most_director_changes_question = "Which fashion houses have undergone the most creative director changes?"
-# longest_serving_director_question = "Which fashion houses have had the longest-serving creative director?"
-# houses_collaborations_question = "Which fashion houses have collaborated with other fashion houses or brands?"
-# houses_haute_couture_question = "Which fashion houses have produced haute couture collections?"
-# houses_active_decade_question = "Which fashion houses were active during the $x decade?"
-# houses_most_collections_question = "Which fashion houses have the most collections recorded in the database?"
-
-# # Fashion Collections
-# designer_worked_on_collections_question = "Which fashion collections did $x work on?"
-# collections_inspired_by_theme_question = "Which fashion house produced a collection inspired by $x theme?"
-# collections_high_critical_acclaim_question = "Which collections received the highest critical acclaim?"
-# collections_historical_references_question = "Which collections feature references to historical fashion styles?"
-# collections_sustainability_theme_question = "Which collections include sustainability as a key theme?"
-# collections_per_house_per_year_question = "How many collections did each fashion house produce in year $x?"
-# collections_avant_garde_question = "Which collections have been described as avant-garde?"
-# collections_stylistic_similarity_question = "Which collections share stylistic similarities with $x?"
-# collections_evolution_over_time_question = "How has the style of $x fashion house evolved over the years?"
-# collections_shown_at_fashion_week_question = "Which collections were showcased at $x fashion week?"
-
-# # Designers & Fashion Houses
-# studied_then_director_question = "Which designers who studied at $x fashion school later became creative directors at $y fashion house?"
-# worked_at_both_houses_question = "Which designers have worked at both $x and $y fashion houses?"
-# launched_label_after_working_question = "Which designers who launched their own fashion labels had previously worked at $x fashion house?"
-# won_award_then_worked_question = "Which designers who won the $x award also worked for $y fashion house?"
-# lived_in_city_and_director_question = "Which designers who lived in $x city have worked as creative directors for $y fashion house?"
-# worked_for_three_houses_question = "Which designers have worked for at least three different fashion houses during their career?"
-# designed_for_two_brands_same_year_question = "Which designers who were creative directors at $x fashion house also designed collections for another brand in the same year?"
-# creative_director_and_label_founder_question = "Which designers were both fashion house creative directors and independent label founders at the same time?"
-# collaborated_with_two_houses_question = "Which designers have collaborated with both $x and $y fashion houses?"
-# worked_at_x_before_y_question = "Which designers worked at $x fashion house before moving to $y?"
-
-# # Designers & Collections
-# designed_for_multiple_houses_same_season_question = "Which designers have designed collections for multiple fashion houses in the same season?"
-# couture_and_ready_to_wear_question = "Which designers who specialized in haute couture have also designed ready-to-wear collections?"
-# similar_theme_collections_question = "Which designers who worked on $x collection had previously worked on a similar-themed collection for another brand?"
-# award_winner_inspired_by_history_question = "Which designers who have won an award have produced a collection inspired by $x historical period?"
-# alma_mater_referenced_in_collection_question = "Which designers who studied at $x school designed a collection that referenced their alma mater?"
-
-# # Fashion Houses & Collections
-# same_historical_period_collections_question = "Which fashion houses have produced collections inspired by the same historical period?"
-# collaborations_with_artists_question = "Which fashion houses have produced collections featuring collaborations with artists from $x field (e.g., photography, sculpture, film)?"
-# change_director_different_collection_question = "Which fashion houses have changed creative directors and immediately released a significantly different collection?"
-# avant_garde_and_minimalist_question = "Which fashion houses have produced both avant-garde and minimalist collections?"
-# inspired_by_other_brands_question = "Which fashion houses have produced a collection inspired by a previous designer’s work from another brand?"
-
-# # Multi-Category (Designers, Houses, Collections)
-# designer_worked_house_then_influenced_question = "Which designers who worked for $x fashion house also designed a collection influenced by $y movement?"
-# designer_lived_in_city_referenced_collection_question = "Which designers who lived in $x city during year $y produced a collection with references to that place?"
-# multiple_designers_same_collection_question = "Which fashion houses have had at least three designers contribute to the same collection?"
-# worked_elsewhere_during_collection_question = "Which collections were created by a designer who was also working at another fashion house at the time?"
-# education_influenced_collection_question = "Which fashion collections were influenced by a designer’s education at $x fashion school?"
-
-# # Designers & Fashion Houses
-# studied_then_director_question = "Which designers who studied at $x fashion school later became creative directors at $y fashion house?"
-# worked_at_both_houses_question = "Which designers have worked at both $x and $y fashion houses?"
-# launched_label_after_working_question = "Which designers who launched their own fashion labels had previously worked at $x fashion house?"
-# won_award_then_worked_question = "Which designers who won the $x award also worked for $y fashion house?"
-# lived_in_city_and_director_question = "Which designers who lived in $x city have worked as creative directors for $y fashion house?"
-# worked_for_three_houses_question = "Which designers have worked for at least three different fashion houses during their career?"
-# designed_for_two_brands_same_year_question = "Which designers who were creative directors at $x fashion house also designed collections for another brand in the same year?"
-# creative_director_and_label_founder_question = "Which designers were both fashion house creative directors and independent label founders at the same time?"
-# collaborated_with_two_houses_question = "Which designers have collaborated with both $x and $y fashion houses?"
-# worked_at_x_before_y_question = "Which designers worked at $x fashion house before moving to $y?"
-
-# # Designers & Collections
-# designed_for_multiple_houses_same_season_question = "Which designers have designed collections for multiple fashion houses in the same season?"
-# couture_and_ready_to_wear_question = "Which designers who specialized in haute couture have also designed ready-to-wear collections?"
-# similar_theme_collections_question = "Which designers who worked on $x collection had previously worked on a similar-themed collection for another brand?"
-# award_winner_inspired_by_history_question = "Which designers who have won an award have produced a collection inspired by $x historical period?"
-# alma_mater_referenced_in_collection_question = "Which designers who studied at $x school designed a collection that referenced their alma mater?"
-
-# # Fashion Houses & Collections
-# same_historical_period_collections_question = "Which fashion houses have produced collections inspired by the same historical period?"
-# collaborations_with_artists_question = "Which fashion houses have produced collections featuring collaborations with artists from $x field (e.g., photography, sculpture, film)?"
-# change_director_different_collection_question = "Which fashion houses have changed creative directors and immediately released a significantly different collection?"
-# avant_garde_and_minimalist_question = "Which fashion houses have produced both avant-garde and minimalist collections?"
-# inspired_by_other_brands_question = "Which fashion houses have produced a collection inspired by a previous designer’s work from another brand?"
-
-# # Multi-Category (Designers, Houses, Collections)
-# designer_worked_house_then_influenced_question = "Which designers who worked for $x fashion house also designed a collection influenced by $y movement?"
-# designer_lived_in_city_referenced_collection_question = "Which designers who lived in $x city during year $y produced a collection with references to that place?"
-# multiple_designers_same_collection_question = "Which fashion houses have had at least three designers contribute to the same collection?"
-# worked_elsewhere_during_collection_question = "Which collections were created by a designer who was also working at another fashion house at the time?"
-# education_influenced_collection_question = "Which fashion collections were influenced by a designer’s education at $x fashion school?"
-
-
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from src.sparql_query_wikibase import *
-
-
-
-fashion_collection_images_question = Template("Images of collections produced by $x")
-fashion_collection_images_query = Template("""
-PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?statement ?fashion_collectionLabel ?image_url ?reference_URL 
-  ?fashion_seasonLabel (YEAR(?fashion_year) AS ?year) ?categoryLabel WHERE {
-    { ?fashion_house  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?fashion_house  skos:altLabel $x@en. }  # Match alias   
-                                           
-  
-  ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?fashion_collection.
-  OPTIONAL { ?fashion_collection wbt:P32 ?fashion_season. }
-  OPTIONAL {?fashion_collection wbt:P28 ?fashion_year.}
-  OPTIONAL {?fashion_collection wbt:P38 ?category.}
-  
-  OPTIONAL {
-    ?statement pq:P35 ?image_url.
-  }
-
-                                                
-  OPTIONAL {
-    ?statement prov:wasDerivedFrom ?reference.
-    ?reference pr:P24 ?reference_URL. 
-  }
-
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } }
-                                           """)
-
-year_designers_birth_question = Template("Which designers were born in $x?")
-year_designers_birth_query = Template("""
-PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
-
-SELECT ?designerLabel ?birthdate  WHERE {
-  ?designer wbt:P3 ?birthdate .
-  FILTER (YEAR(?birthdate) = $x)
-
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-} """)
-
-fashion_house_directors_question = Template("Which designers have been creative directors of $x ?")
-fashion_house_directors_query = Template("""
-PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT 
-?creative_directorLabel (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year)
-  (YEAR(?end_time) AS ?end_year) ?title ?statement ?reference_URL_source
-WHERE {
-  # Fashion house identified by brand name
-  ?fashion_house wbt:P2 wb:Q1.                                       
-    { ?fashion_house rdfs:label $x@en.}  # Match label
-  UNION
-  { ?fashion_house skos:altLabel $x@en. }  # Match alias   
-  
-  # Link through designer employed (P29) property for the fashion house  - the property for designer employment is P10
-  {
-    ?fashion_house p:P29 ?statement.
-    ?statement ps:P29 ?creative_director.
-
-    # Optional qualifiers for P29 regarding the time frame
-    OPTIONAL { ?statement pq:P15 ?start_time. }
-    OPTIONAL { ?statement pq:P28 ?point_time. } 
-    OPTIONAL { ?statement pq:P16 ?end_time. }
-    OPTIONAL { ?statement pq:P25 ?title. }
-    OPTIONAL { ?fashion_house wbt:P11 ?inception. } 
-
-    
-    # Retrieve references for P29
-    OPTIONAL {
-      ?statement prov:wasDerivedFrom ?reference.
-      ?reference pr:P24 ?reference_URL.
-      BIND(
-        IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-      )
-      BIND(
-        IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-      )
-    }
-  }
-  UNION
-  # Link through founded_by (P14) property for the fashion house
-  {
-    ?fashion_house p:P14 ?statement.
-    ?statement ps:P14 ?creative_director.
-
-    # Optional qualifiers for P14 (start_year as inception)
-    OPTIONAL { ?statement pq:P15 ?start_time. }
-    OPTIONAL { ?statement pq:P28 ?point_time. } 
-    OPTIONAL { ?statement pq:P16 ?end_time. }
-    OPTIONAL { ?statement pq:P25 ?title. }
- 
-    
-    # Retrieve references for P14
-    OPTIONAL {
-      ?statement prov:wasDerivedFrom ?reference.
-      ?reference pr:P24 ?reference_URL.
-      BIND(
-        IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-      )
-      BIND(
-        IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-      )
-    }
-  }
-
-  # Filter for relevant titles (creative director, founder, etc.) to retrieve relevant employment as creative director
-  FILTER (
-    REGEX(?title, "director", "i") || 
-    REGEX(?title, "head designer", "i") ||
-    REGEX(?title, "chief", "i") ||
-    REGEX(?title, "founder", "i")                                   
-  )
-  FILTER(?reference_URL_source != "")
-  # Retrieve labels
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}   ORDER BY ?start_year """)
-
-
-
-designer_fashion_house_directors_question = Template("In which fashion house did $x work as a creative director?")
-designer_fashion_house_directors_query = Template("""  
-PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel 
-       (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year) 
-       (YEAR(?end_time) AS ?end_year) 
-       ?title ?reference_URL_source ?statement 
-WHERE {
-   # Designer linked to fashion house through roles or founded_by
-    ?fashion_designer wbt:P2 wb:Q5.                                              
-     { ?fashion_designer  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?fashion_designer  skos:altLabel $x@en. }  # Match alias   
-   
-   # Link through employer (P10)
-   ?fashion_designer p:P10 ?statement.
-   ?statement ps:P10 ?fashion_house.
-  
-   # Optional start and end time qualifiers for roles
-   OPTIONAL { ?statement pq:P15 ?start_time. }
-   OPTIONAL { ?statement pq:P28 ?point_time. } 
-   OPTIONAL { ?statement pq:P16 ?end_time. }
-   OPTIONAL { ?statement pq:P25 ?title. }
-
-   # Inception as fallback for start year
-   OPTIONAL { ?fashion_house wbt:P11 ?inception. }
-
-   # Filter for relevant roles (director, founder, etc.)
-   FILTER (
-       REGEX(?title, "director", "i") || 
-       REGEX(?title, "head designer", "i") ||
-       REGEX(?title, "chief","i") ||
-       REGEX(?title, "founder","i")                                   
-   )
-  
-   # Retrieve references for the statement
-    OPTIONAL {
-      ?statement prov:wasDerivedFrom ?reference.
-      ?reference pr:P24 ?reference_URL.
-      BIND(
-        IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-      )
-      BIND(
-        IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-      )
-    }
-  FILTER(?reference_URL_source != "")
-
-   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-}
-""")
-
-
-designer_fashion_director_question2 = Template("In which fashion house did $x work as a creative director?")
-designer_fashion_director_query2 = Template("""PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT 
-?fashion_houseLabel ?creative_directorLabel (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year)  (YEAR(?end_time) AS ?end_year) ?title ?statement ?reference_URL_source
-WHERE {
-  # Fashion designer identified by name
-  ?creative_director wbt:P2 wb:Q5.                                       
-    { ?creative_director  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?creative_director  skos:altLabel $x@en. }  # Match alias   
-
-  # Link through designer employed (P29) property for the fashion house  - the property for designer employment is P30
-  {
-    ?fashion_house p:P29 ?statement.
-    ?statement ps:P29 ?creative_director.
-
-    # Optional qualifiers for P29 regarding the time frame
-    OPTIONAL { ?statement pq:P15 ?start_time. }
-    OPTIONAL { ?statement pq:P28 ?point_time. } 
-    OPTIONAL { ?statement pq:P16 ?end_time. }
-    OPTIONAL { ?statement pq:P25 ?title. }
-    OPTIONAL { ?fashion_house wbt:P11 ?inception. } 
-
-    
-    # Retrieve references for P29
-    OPTIONAL {
-      ?statement prov:wasDerivedFrom ?reference.
-      ?reference pr:P24 ?reference_URL.
-      BIND(
-        IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-      )
-      BIND(
-        IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-      )
-    }
-  }
-  FILTER (
-    REGEX(?title, "director", "i") || 
-    REGEX(?title, "head designer", "i") ||
-    REGEX(?title, "chief", "i") ||
-    REGEX(?title, "founder", "i")                                   
-  )
-  FILTER(?reference_URL_source != "")
-
-  # Retrieve labels
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}   ORDER BY ?start_year """)
-
-country_designer_question = Template("Which designers are from $x ?")
-country_designer_query = Template("""PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_designerLabel   (SAMPLE(?statement) AS ?statement)  
-  (SAMPLE(?reference_URL) AS ?reference_URL) WHERE {
-  # Restrict to designers who are instances of fashion designer (Q5)
-
-  ?fashion_designer wbt:P2 wb:Q5.
-
-  # Access the statement for the country relationship
-  ?fashion_designer p:P6 ?statement.
-  ?statement ps:P6 ?country.
-    { ?country  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?country skos:altLabel $x@en. }  # Match alias   
-
-  # Retrieve references from the statement
-  OPTIONAL {
-    ?statement prov:wasDerivedFrom ?reference.
-    ?reference pr:P24 ?reference_URL.
-  }
-
-  # Retrieve labels for the fashion designer
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-} 
-GROUP BY ?fashion_designerLabel 
-ORDER BY ?fashion_designerLabel """)
-
-country_designer_query = Template("""PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_designerLabel (SAMPLE(?reference_URL) AS ?reference_URL) {
-  # Restrict to designers who are instances of fashion designer (Q5)
-
-  ?fashion_designer wbt:P2 wb:Q5.
-  ?fashion_designer wbt:P6 ?country.
-  ?fashion_designer p:P6 ?statement.
-    { ?country  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?country  skos:altLabel $x@en. }  # Match alias   
- 
-
-  # Retrieve references from the statement
-  OPTIONAL {
-    ?statement prov:wasDerivedFrom ?reference.
-    ?reference pr:P24 ?reference_URL.
-  }
-
-  # Retrieve labels for the fashion designer
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-} 
-GROUP BY ?fashion_designerLabel 
-ORDER BY ?fashion_designerLabel  """)
-
-
-designer_order_fashion_collection_question = Template("Which fashion collections did $x work on?")
-designer_order_fashion_collection_query = Template("""PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel 
-       (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year) 
-       (YEAR(?end_time) AS ?end_year) 
-       ?title ?reference_URL_source ?statement ?fashion_collectionLabel ?image_url 
-      (YEAR(?fashion_year) as ?year ) ?fashion_seasonLabel ?categoryLabel WHERE {
-   # Designer linked to fashion house through roles or founded_by
-   ?fashion_designer wbt:P2 wb:Q5. 
-  { ?fashion_designer  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?fashion_designer  skos:altLabel $x@en. }  # Match alias                                               
-                                            
-   
-   # Link through "creative director" or other roles
-   ?fashion_designer p:P10 ?statement_designer.
-   ?statement_designer ps:P10 ?fashion_house.
-   OPTIONAL { ?statement_designer pq:P15 ?start_time. }
-   OPTIONAL { ?statement_designer pq:P28 ?point_time. } 
-   OPTIONAL { ?statement_designer pq:P16 ?end_time. }
-   OPTIONAL { ?statement_designer pq:P25 ?title. }   
-    OPTIONAL { ?fashion_house wbt:P11 ?inception. }    
-    FILTER (
-       REGEX(?title, "director", "i") || 
-       REGEX(?title, "head designer", "i") ||
-       REGEX(?title, "chief","i") ||
-       REGEX(?title, "founder","i")                                   
-   )       
-  
-    ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?fashion_collection.
-  OPTIONAL {?fashion_collection wbt:P28 ?fashion_year.}
-    OPTIONAL { ?fashion_collection wbt:P32 ?fashion_season. }
-  OPTIONAL {?fashion_collection wbt:P38 ?category.}
-  
-  OPTIONAL {?statement pq:P35 ?image_url.}
-  OPTIONAL {?statement pq:P37 ?fashion_date.}
-  
- 
-    # Use the adjusted fashion year in the FILTER condition
-FILTER(
-    (YEAR(?start_time) <= YEAR(?fashion_year) && (!BOUND(?end_time) || YEAR(?end_time) >= YEAR(?fashion_year))) ||
-    (YEAR(?point_time) <= YEAR(?fashion_year)) ||
-    (REGEX(?title, "founder", "i") && YEAR(?inception) <= YEAR(?fashion_year))
-)
-
-   # Retrieve references for the statement
-   OPTIONAL {
-     ?statement prov:wasDerivedFrom ?reference.
-     ?reference pr:P24 ?reference_URL_source.
-   }
-
-   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-} ORDER BY ?year """)
-
-
-education_most_popular_question = "What are the most popular educational institutions for fashion designers?"
-education_most_popular_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?schoolLabel (COUNT(?fashion_designer) AS ?designer_count) WHERE {
-  # Retrieve all fashion designers and their schools
-  ?fashion_designer wbt:P9 ?school.
-  
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}
-GROUP BY ?schoolLabel
-ORDER BY DESC(?designer_count) """
-
-brands_founded_question = Template("Which fashion houses were founded in $x")
-brands_founded_query = Template(""" PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-
-SELECT ?fashion_houseLabel ?inception 
-WHERE {
-    ?fashion_house wbt:P2 wb:Q1.
-    ?fashion_house wbt:P11 ?inception.
-
-    
-    FILTER(YEAR(?inception) = $x)
-    
-    SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}""")
-
-how_many_directors_per_fashion_house_question = "How many designers have worked for each fashion house as creative directors?"
-how_many_directors_per_fashion_house_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel (COUNT(DISTINCT ?fashion_designer) AS ?creative_director_count) WHERE {
-  # Match fashion houses linked to designers via employer property
-  ?fashion_designer p:P10 ?statement.
-  ?statement ps:P10 ?fashion_house.
-  
-  # Filter for roles related to creative direction
-  OPTIONAL { ?statement pq:P25 ?title. }
-  FILTER (
-    REGEX(?title, "creative director", "i") || 
-    REGEX(?title, "head designer", "i") ||
-    REGEX(?title, "chief", "i") ||
-    REGEX(?title, "founder", "i")
-  )
-  
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}
-GROUP BY ?fashion_houseLabel
-ORDER BY DESC(?creative_director_count)
-"""
-
-how_many_designers_per_fashion_house_question = "How many designers have worked for each fashion house?"
-how_many_designers_per_fashion_house_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel (COUNT(?designer) AS ?designer_count) WHERE {
-  ?fashion_house wbt:P29 ?designer .
-
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}
-GROUP BY ?fashion_houseLabel
-ORDER BY DESC(?designer_count) """
-
-
-designers_multiple_houses_question = "Which designers have worked for more than one fashion house?"
-designers_multiple_houses_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?designerLabel WHERE {
-  ?designer wbt:P10 ?house .
-  ?designer wbt:P10 ?anotherHouse .
-SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } 
- }
-GROUP BY ?designerLabel
-HAVING (COUNT(?house) > 1)"""
-
-award_question = "Which designers won which awards?"
-award_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?designerLabel ?awardLabel WHERE {
-  ?designer wbt:P18 ?award .
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}"""
-
-fashion_houses_with_collections_question ="Which distinct fashion houses have at least a fashion collection?"
-fashion_houses_with_collections_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT DISTINCT ?houseLabel WHERE {
-  ?house wbt:P31 ?collection .
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-} """
-
-popular_year_inception_question = "In which years were the most fashion houses founded?"
-popular_year_inception_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-
-SELECT ?year_inception (COUNT(DISTINCT ?fashion_house) AS ?count)
-WHERE {
-  ?fashion_house wbt:P11 ?year.
-  BIND(YEAR(?year) AS ?year_inception)
-}
-GROUP BY ?year_inception
-ORDER BY DESC(?count) """
-
-
-longest_serving_director_question = "Which fashion houses have had the longest-serving creative director?"
-longest_serving_director_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>
-PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
-PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
-
-SELECT ?fashion_houseLabel ?duration ?designerLabel
-WHERE {
-  ?fashion_house p:P29 ?statement.
-  ?statement ps:P29 ?designer.
-  OPTIONAL { ?statement pq:P15 ?startTime. }
-  OPTIONAL { ?statement pq:P16 ?endTime. }
-
-  # Ensure we only calculate when both dates exist
-  FILTER(BOUND(?startTime) && BOUND(?endTime))
-
-  # Compute duration safely
-  BIND(YEAR(?endTime) - YEAR(?startTime) AS ?duration)
-
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-}
-ORDER BY DESC(?duration)"""
-
-houses_most_collections_question = "Which fashion houses have the most collections recorded in the database?"
-houses_most_collections_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashionHouseLabel (COUNT(?collection) AS ?collection_count) WHERE {
-  ?fashionHouse wbt:P31 ?collection .
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}
-GROUP BY ?fashionHouseLabel
-ORDER BY DESC(?collection_count)"""
-
-
-collections_sustainability_theme_question = "Which collections include sustainability as a key theme?"
-collections_sustainability_theme_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?collectionLabel ?fashion_houseLabel ?reference_URL WHERE {
-  ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?collection.
-  ?statement pq:P34 ?description .
-  FILTER (regex(?description, "sustainability", "i"))
-     # Retrieve references for the statement
-   OPTIONAL {
-     ?statement prov:wasDerivedFrom ?reference.
-     ?reference pr:P24 ?reference_URL.
-   }
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}  """ 
-
-
-collections_jeans_question = "Which collections showcase products made with jeans?"
-collections_jeans_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?collectionLabel ?fashion_houseLabel ?reference_URL WHERE {
-  ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?collection.
-  ?statement pq:P34 ?description .
-  FILTER (regex(?description, "jeans", "i"))
-     # Retrieve references for the statement
-   OPTIONAL {
-     ?statement prov:wasDerivedFrom ?reference.
-     ?reference pr:P24 ?reference_URL.
-   }
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}  """ 
-
-
-creative_directors_school_question = "To which school went most designers who later became creative directors of fashion houses?"
-creative_directors_school_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?schoolLabel (COUNT(DISTINCT ?designer) AS ?designer_count) WHERE {
-  ?designer p:P10 ?fashion_house .
-
-     OPTIONAL { ?fashion_house pq:P25 ?title. }
-  FILTER (
-    REGEX(?title, "creative director", "i") || 
-    REGEX(?title, "head designer", "i") ||
-    REGEX(?title, "chief", "i") ||
-    REGEX(?title, "founder", "i")
-  )
-  
-  OPTIONAL { ?designer wbt:P9 ?school . }
-SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } 
- }
-GROUP BY ?schoolLabel
-ORDER BY DESC(?designer_count)"""
-
-fashion_houses_thematic_collection_question = "Which fashion houses had collections with a sustainability theme, and how many of them?"
-fashion_houses_thematic_collection_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel (COUNT(DISTINCT ?collection) AS ?collection_count) WHERE {
-  ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?collection.
-  ?statement pq:P34 ?description .
-  FILTER (regex(?description, "sustainability", "i"))
-     # Retrieve references for the statement
-   OPTIONAL {
-     ?statement prov:wasDerivedFrom ?reference.
-     ?reference pr:P24 ?reference_URL.
-   }
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-} GROUP BY ?fashion_houseLabel
-ORDER BY DESC(?collection_count) """
-
-
-questions_queries_dict = [ { "question": education_most_popular_question, "query": education_most_popular_query},
-                          { "question": how_many_designers_per_fashion_house_question, "query": how_many_designers_per_fashion_house_query},
-                           {"question": how_many_directors_per_fashion_house_question, "query": how_many_directors_per_fashion_house_query}, 
-                           {"question": designers_multiple_houses_question, "query":designers_multiple_houses_query }, 
-                           {"question": award_question, "query": award_question},
-                           {"question": fashion_houses_with_collections_question, "query": fashion_houses_with_collections_query},
-                           {"question": popular_year_inception_question, "query": popular_year_inception_query},
-                           {"question": longest_serving_director_question, "query": longest_serving_director_query},
-                           {"question": houses_most_collections_question, "query": houses_most_collections_query},
-                           {"question": collections_sustainability_theme_question, "query": collections_sustainability_theme_query},
-                           ]
-
-questions_queries_dict_entity = [                            {"question": fashion_house_directors_question.substitute({ "x": f"{"Chanel"}"}), "query": fashion_house_directors_query.substitute({ "x": f"'{"Chanel"}'"})},
-                          { "question": designer_fashion_house_directors_question.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_fashion_house_directors_query.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          {"question": country_designer_question.substitute({ "x": f"{"Italy"}"}), "query": country_designer_query.substitute({ "x": f"'{"Italy"}'"})},
-                          {  "question": designer_order_fashion_collection_question.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_order_fashion_collection_query.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          {"question": designer_fashion_director_question2.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_fashion_director_query2.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          { "question": year_designers_birth_question.substitute({ "x": 1970}), "query": year_designers_birth_query.substitute({ "x": 1970})},
-                          { "question": fashion_collection_images_question.substitute({ "x": f"{"Chanel"}"}), "query": fashion_collection_images_query.substitute({ "x": f"'{"Chanel"}'"})}]
-
-
-# Designers
-designers_born_in_year_question = "Which designers were born in year $x?"
-creative_directors_of_house_question = "Which designers have been creative directors of $x?"
-designers_multiple_houses_question = "Which designers have worked for more than one fashion house?"
-educated_at_school_question = "Which designers were educated at $x fashion school?"
-won_award_question = "Which designers have won the $x award?"
-launched_own_label_question = "Which designers launched their own fashion label?"
-worked_at_x_between_years_question = "Which designers worked at $x between year $y and $z?"
-designers_multiple_collaborations_question = "Which designers have collaborated with multiple fashion houses?"
-designers_lived_in_city_question = "Which designers have lived in $x city?"
-designers_active_decade_question = "Which designers were active during the $x decade?"
-
-# Fashion Houses
-designers_worked_for_house_question = "Which designers have worked for fashion house $x?"
-creative_directors_per_house_question = "How many designers have worked for each fashion house as creative directors?"
-houses_founded_in_year_question = "Which fashion houses were founded in year $x?"
-houses_most_director_changes_question = "Which fashion houses have undergone the most creative director changes?"
-longest_serving_director_question = "Which fashion houses have had the longest-serving creative director?"
-houses_collaborations_question = "Which fashion houses have collaborated with other fashion houses or brands?"
-houses_haute_couture_question = "Which fashion houses have produced haute couture collections?"
-houses_active_decade_question = "Which fashion houses were active during the $x decade?"
-houses_most_collections_question = "Which fashion houses have the most collections recorded in the database?"
-
-# Fashion Collections
-designer_worked_on_collections_question = "Which fashion collections did $x work on?"
-collections_inspired_by_theme_question = "Which fashion house produced a collection inspired by $x theme?"
-collections_high_critical_acclaim_question = "Which collections received the highest critical acclaim?"
-collections_historical_references_question = "Which collections feature references to historical fashion styles?"
-collections_sustainability_theme_question = "Which collections include sustainability as a key theme?"
-collections_per_house_per_year_question = "How many collections did each fashion house produce in year $x?"
-collections_avant_garde_question = "Which collections have been described as avant-garde?"
-collections_stylistic_similarity_question = "Which collections share stylistic similarities with $x?"
-collections_evolution_over_time_question = "How has the style of $x fashion house evolved over the years?"
-collections_shown_at_fashion_week_question = "Which collections were showcased at $x fashion week?"
-
-# Designers & Fashion Houses
-studied_then_director_question = "Which designers who studied at $x fashion school later became creative directors at $y fashion house?"
-worked_at_both_houses_question = "Which designers have worked at both $x and $y fashion houses?"
-launched_label_after_working_question = "Which designers who launched their own fashion labels had previously worked at $x fashion house?"
-won_award_then_worked_question = "Which designers who won the $x award also worked for $y fashion house?"
-lived_in_city_and_director_question = "Which designers who lived in $x city have worked as creative directors for $y fashion house?"
-worked_for_three_houses_question = "Which designers have worked for at least three different fashion houses during their career?"
-designed_for_two_brands_same_year_question = "Which designers who were creative directors at $x fashion house also designed collections for another brand in the same year?"
-creative_director_and_label_founder_question = "Which designers were both fashion house creative directors and independent label founders at the same time?"
-collaborated_with_two_houses_question = "Which designers have collaborated with both $x and $y fashion houses?"
-worked_at_x_before_y_question = "Which designers worked at $x fashion house before moving to $y?"
-
-# Designers & Collections
-designed_for_multiple_houses_same_season_question = "Which designers have designed collections for multiple fashion houses in the same season?"
-couture_and_ready_to_wear_question = "Which designers who specialized in haute couture have also designed ready-to-wear collections?"
-similar_theme_collections_question = "Which designers who worked on $x collection had previously worked on a similar-themed collection for another brand?"
-award_winner_inspired_by_history_question = "Which designers who have won an award have produced a collection inspired by $x historical period?"
-alma_mater_referenced_in_collection_question = "Which designers who studied at $x school designed a collection that referenced their alma mater?"
-
-# Fashion Houses & Collections
-same_historical_period_collections_question = "Which fashion houses have produced collections inspired by the same historical period?"
-collaborations_with_artists_question = "Which fashion houses have produced collections featuring collaborations with artists from $x field (e.g., photography, sculpture, film)?"
-change_director_different_collection_question = "Which fashion houses have changed creative directors and immediately released a significantly different collection?"
-avant_garde_and_minimalist_question = "Which fashion houses have produced both avant-garde and minimalist collections?"
-inspired_by_other_brands_question = "Which fashion houses have produced a collection inspired by a previous designer’s work from another brand?"
-
-# Multi-Category (Designers, Houses, Collections)
-designer_worked_house_then_influenced_question = "Which designers who worked for $x fashion house also designed a collection influenced by $y movement?"
-designer_lived_in_city_referenced_collection_question = "Which designers who lived in $x city during year $y produced a collection with references to that place?"
-multiple_designers_same_collection_question = "Which fashion houses have had at least three designers contribute to the same collection?"
-worked_elsewhere_during_collection_question = "Which collections were created by a designer who was also working at another fashion house at the time?"
-education_influenced_collection_question = "Which fashion collections were influenced by a designer’s education at $x fashion school?"
-
-# Designers & Fashion Houses
-studied_then_director_question = "Which designers who studied at $x fashion school later became creative directors at $y fashion house?"
-worked_at_both_houses_question = "Which designers have worked at both $x and $y fashion houses?"
-launched_label_after_working_question = "Which designers who launched their own fashion labels had previously worked at $x fashion house?"
-won_award_then_worked_question = "Which designers who won the $x award also worked for $y fashion house?"
-lived_in_city_and_director_question = "Which designers who lived in $x city have worked as creative directors for $y fashion house?"
-worked_for_three_houses_question = "Which designers have worked for at least three different fashion houses during their career?"
-designed_for_two_brands_same_year_question = "Which designers who were creative directors at $x fashion house also designed collections for another brand in the same year?"
-creative_director_and_label_founder_question = "Which designers were both fashion house creative directors and independent label founders at the same time?"
-collaborated_with_two_houses_question = "Which designers have collaborated with both $x and $y fashion houses?"
-worked_at_x_before_y_question = "Which designers worked at $x fashion house before moving to $y?"
-
-# Designers & Collections
-designed_for_multiple_houses_same_season_question = "Which designers have designed collections for multiple fashion houses in the same season?"
-couture_and_ready_to_wear_question = "Which designers who specialized in haute couture have also designed ready-to-wear collections?"
-similar_theme_collections_question = "Which designers who worked on $x collection had previously worked on a similar-themed collection for another brand?"
-award_winner_inspired_by_history_question = "Which designers who have won an award have produced a collection inspired by $x historical period?"
-alma_mater_referenced_in_collection_question = "Which designers who studied at $x school designed a collection that referenced their alma mater?"
-
-# Fashion Houses & Collections
-same_historical_period_collections_question = "Which fashion houses have produced collections inspired by the same historical period?"
-collaborations_with_artists_question = "Which fashion houses have produced collections featuring collaborations with artists from $x field (e.g., photography, sculpture, film)?"
-change_director_different_collection_question = "Which fashion houses have changed creative directors and immediately released a significantly different collection?"
-avant_garde_and_minimalist_question = "Which fashion houses have produced both avant-garde and minimalist collections?"
-inspired_by_other_brands_question = "Which fashion houses have produced a collection inspired by a previous designer’s work from another brand?"
-
-# Multi-Category (Designers, Houses, Collections)
-designer_worked_house_then_influenced_question = "Which designers who worked for $x fashion house also designed a collection influenced by $y movement?"
-designer_lived_in_city_referenced_collection_question = "Which designers who lived in $x city during year $y produced a collection with references to that place?"
-multiple_designers_same_collection_question = "Which fashion houses have had at least three designers contribute to the same collection?"
-worked_elsewhere_during_collection_question = "Which collections were created by a designer who was also working at another fashion house at the time?"
-education_influenced_collection_question = "Which fashion collections were influenced by a designer’s education at $x fashion school?"
-
-
-
-

src/sparql_query_wikibase.py

@@ -1,130 +0,0 @@
-
-
-from urllib.parse import urlparse
-import logging
-log = logging.getLogger(__name__)
-from time import sleep
-import requests
-helpers_session = requests.Session()
-from wikibaseintegrator.wbi_helpers import get_user_agent
-import pandas as pd
-from string import Template
-queries = False
-wikibase_api_url = 'https://fashionwiki.wikibase.cloud/w/api.php'
-config = {
-"SPARQL_ENDPOINT_URL": "https://fashionwiki.wikibase.cloud/query/sparql",
-'USER_AGENT':  'YourBotName/1.0 (https://yourwebsite.org/bot-info)',
-'WIKIBASE_URL': wikibase_api_url,
-}
-
-wikibase_properties_id = {'instance of': 'P2',
- 'reference URL': 'P24',
- 'start time': 'P15',
- 'end time': 'P16',
- 'occupation title': 'P25',
- 'educated at': 'P9',
- 'employer': 'P10',
- 'work location': 'P7',
- 'award received': 'P18',
- 'point in time': 'P28',
- 'exact match': 'P23',
- 'date of birth': 'P3',
- 'place of birth': 'P4',
- 'date of death': 'P5',
- 'country of citizenship': 'P6',
- 'occupation': 'P19',
- 'sex or gender': 'P8',
- 'official website': 'P17',
- 'perfumes': 'P27',
- 'who wears it': 'P26',
- 'inception': 'P11',
- 'headquarters location': 'P12',
- 'parent organization': 'P13',
- 'founded by': 'P14',
- 'owned by': 'P22',
- 'industry': 'P20',
- 'country': 'P30',
- 'total revenue': 'P21',
- 'designer employed': 'P29',
- 'country of origin': 'P30',
- 'fashion collection': 'P31',
- 'fashion season': 'P32',
- 'fashion show location': 'P33',
- 'description of fashion collection': 'P34',
- 'image of fashion collection': 'P35',
- 'editor of fashion collection description': 'P36',
- 'date of fashion collection': 'P37',
- 'fashion show category': 'P38',
- 'fashion house X fashion collection': 'P39'}
-
-classes_wikibase = {'fashion designer': 'Q5',
- 'fashion house': 'Q1',
- 'business': 'Q9',
- 'academic institution': 'Q2',
- 'geographic location': 'Q4',
- 'fashion award': 'Q8',
- 'gender': 'Q6',
- 'occupation': 'Q7',
- 'human': 'Q36',
- 'organization': 'Q3',
- 'brand': 'Q38',
- 'lifestyle brand': 'Q3417',
- 'privately held company': 'Q1729',
- 'fashion season': 'Q8199',
- 'fashion show category': 'Q8200',
- 'fashion season collection': 'Q8201',
- 'fashion journalist': 'Q8207'}
-
-
-def execute_sparql_query(query: str, prefix: str | None = None, endpoint: str | None = None, user_agent: str | None = None, max_retries: int = 1000, retry_after: int = 60) -> dict:
-    """
-    Execute any SPARQL query with the provided parameters.
-    """
-
-    sparql_endpoint_url = str(endpoint or config['SPARQL_ENDPOINT_URL'])
-    user_agent = user_agent or (str(config['USER_AGENT']) if config['USER_AGENT'] is not None else None)
-
-    hostname = urlparse(sparql_endpoint_url).hostname
-    if hostname and hostname.endswith(('wikidata.org', 'wikipedia.org', 'wikimedia.org')) and user_agent is None:
-        log.warning('WARNING: Please set a user agent if you interact with a Wikimedia Foundation instance.')
-
-    if prefix:
-        query = prefix + '\n' + query
-
-    headers = {
-        'Accept': 'application/sparql-results+json',
-        'User-Agent': get_user_agent(user_agent),
-        'Content-Type': 'application/sparql-query'  # Correct Content-Type
-    }
-
-    # Attempt to make the request
-    for _ in range(max_retries):
-        try:
-            # Use 'data' instead of 'params' for the POST request to SPARQL
-            response = helpers_session.post(sparql_endpoint_url, data=query, headers=headers)
-        except requests.exceptions.ConnectionError as e:
-            log.exception("Connection error: %s. Sleeping for %d seconds.", e, retry_after)
-            sleep(retry_after)
-            continue
-        if response.status_code in (500, 502, 503, 504):
-            log.error("Service unavailable (HTTP Code %d). Sleeping for %d seconds.", response.status_code, retry_after)
-            sleep(retry_after)
-            continue
-        if response.status_code == 429:
-            if 'retry-after' in response.headers:
-                retry_after = int(response.headers['retry-after'])
-            log.error("Too Many Requests (429). Sleeping for %d seconds", retry_after)
-            sleep(retry_after)
-            continue
-        response.raise_for_status()  # Raise any non-success status code
-        return response.json()  # Return the JSON result if successful
-
-    raise Exception(f"No result after {max_retries} retries.")
-
-
-def get_results_to_df( query):
-    results = execute_sparql_query(query)
-    df = pd.DataFrame(results["results"]["bindings"])
-    df = df.map(lambda x: x['value'] if pd.notnull(x) else None)
-    return df
-

src/use_llm.py

@@ -1,80 +0,0 @@
-
-# from huggingface_hub import InferenceClient
-# import os
-
-# # Use a Hugging Face inference endpoint like "google/gemma-1.1-7b-it"
-# # You must have access to this model (either public or via token)
-
-# HUGGINGFACE_API_TOKEN = os.getenv("HF_TOKEN")  # Add this in your HF Space's secret settings
-# DEFAULT_MODEL = "google/gemma-1.1-7b-it"
-
-# client = InferenceClient(DEFAULT_MODEL, token=HUGGINGFACE_API_TOKEN)
-
-# def send_chat_prompt(prompt: str, model: str, system_prompt: str) -> str:
-#     full_prompt = f"<|start_of_turn|>system\n{system_prompt}<|end_of_turn|>\n" \
-#                   f"<|start_of_turn|>user\n{prompt}<|end_of_turn|>\n" \
-#                   f"<|start_of_turn|>assistant\n"
-    
-#     response = client.text_generation(
-#         prompt=full_prompt,
-#         max_new_tokens=500,
-#         temperature=0.5,
-#         stop_sequences=["<|end_of_turn|>"]
-#     )
-    
-#     return response.strip()
-
-# def main_generate(prompt, model=DEFAULT_MODEL, system_prompt="You are a helpful assistant that generates SPARQL queries."):
-#     response = send_chat_prompt(prompt, model, system_prompt)
-#     response = response.replace('```', '').replace('json', '').strip()
-#     return response
-
-
-
-
-
-
-# from sentence_transformers import SentenceTransformer
-
-# model = SentenceTransformer("thenlper/gte-large")  # downloaded from Hugging Face
-
-# def get_embeddings(texts):
-#     if isinstance(texts, str):
-#         texts = [texts]
-#     embeddings = model.encode(texts, convert_to_numpy=True)
-#     return embeddings
-
-
-
-
-
-
-import ollama 
-import openai 
-
-def get_embeddings(texts):
-    response = ollama.embed(model="mxbai-embed-large", input=texts)
-    embeddings = response["embeddings"]
-    return embeddings
-
-
-openai_api_key = "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"
-#generate
-def send_chat_prompt(prompt, model, system_prompt ):
-    client = openai.OpenAI(
-            base_url="http://localhost:11434/v1" if not "gpt" in model else None,
-            api_key= "ollama" if not "gpt" in model else openai_api_key)
-    resp = client.chat.completions.create(
-        model=model,
-        temperature = 0.5 ,
-        messages=[
-                {"role": "system", "content": system_prompt},  
-                {"role": "user", "content": prompt}])
-    response = resp.choices[0].message.content
-    return response
-
-def main_generate(prompt,model, system_prompt):
-    response = send_chat_prompt(prompt,model, system_prompt)
-    response = response.replace('```','').replace('json','')
-    #print(f" {model} Response:", response)
-    return response

src/visual_qa.py

@@ -1,131 +0,0 @@
-
-import json
-import pandas as pd
-from transformers import CLIPProcessor, CLIPModel
-import torch
-import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-from datasets import load_dataset
-import chromadb
-from chromadb import Client
-from chromadb.config import Settings
-
-from datetime import datetime   
-
-def initialize_collection(collection_name="clip_image_embeddings"):
-    # Initialize ChromaDB client (PersistentClient stores embeddings between runs)
-    client = chromadb.PersistentClient(path="./chroma_db")  # Change path if needed
-    #client = chromadb.EphemeralClient()
-
-
-    # Get a list of existing collection names
-    existing_collections = [col for col in client.list_collections()]  # v0.6.0 change
-
-    if collection_name in existing_collections:
-        collection = client.get_collection(name=collection_name)
-        print(f"Using existing collection: {collection_name}")
-    else:
-        collection = client.create_collection(name=collection_name)
-
-    return collection
-
-
-
-def main_create_image_collection(df_emb, collection_name="clip_image_embeddings"):
-    documents = []
-    embeddings_all = df_emb["fashion_clip_image"].tolist()
-
-    for i in range(len(df_emb)):
-        if i >= len(df_emb["image_url"]):
-            print(f"Index {i} out of range for image_url column")
-            continue
-
-        image_url = df_emb["image_url"].iloc[i]
-        if image_url is not None:
-            documents.append(image_url)
-        else:
-            documents.append(None)
-
-    collection = initialize_collection(collection_name)
-
-    for i, d in enumerate(documents):
-        embeddings = embeddings_all[i]
-        embedding_id = str(i)  # Convert index to string
-
-        if embeddings is None:
-            continue  # Skip if no embedding
-
-        # Check if ID already exists
-        existing_entry = collection.get(ids=[embedding_id])
-
-        if existing_entry and existing_entry["ids"]:  # If the ID is found, skip adding
-            continue
-
-        collection.add(
-            ids=[embedding_id],
-            embeddings=embeddings,
-            documents=[d]
-        )
-
-    return collection
-
-
-
-model_name = "patrickjohncyh/fashion-clip"
-device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
-model = CLIPModel.from_pretrained(model_name).to(device)
-processor = CLIPProcessor.from_pretrained(model_name)
-
-def main_text_retrieve_images(text, result_query=None, n_retrieved=5):
-
-    
-
-    # Load the dataset (no split specified, so the whole dataset)
-    dataset = load_dataset("traopia/fashion_show_data_all_embeddings.json")
-    # This returns a DatasetDict with splits as keys (usually 'train' by default).
-    # To get the whole dataset, you can access the first split like this:
-    split_name = list(dataset.keys())[0]
-    full_dataset = dataset[split_name]
-
-    # Convert to pandas DataFrame
-    df_emb = full_dataset.to_pandas()
-    print(df_emb.head())  # Debugging statement
-    df_emb = df_emb.drop_duplicates(subset='image_urls')
-    df_emb['fashion_clip_image'] = df_emb['fashion_clip_image'].apply(lambda x: x[0] if type(x) == list else None)
-    df_emb['image_url'] = df_emb['image_urls'].apply(lambda x: x[0] if x else None)
-    df_emb = df_emb.drop_duplicates(subset='image_url')
-
-    #print("DataFrame head:", df_emb.head())  # Debugging statement
-
-    if result_query:
-        df_small = pd.DataFrame(result_query, columns=["image_url"])
-        df_filtered = df_emb.merge(df_small[['image_url']], on='image_url', how='inner')
-        df_emb = df_filtered
-        # Generate a collection name based on the current timestamp
-        collection_name = f"collection_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
-        collection = main_create_image_collection(df_emb, collection_name=collection_name)
-    else:
-        collection = main_create_image_collection(df_emb)
-
-    inputs = processor(text=[text], return_tensors="pt", padding=True, truncation=True, max_length=77).to(device)
-
-    with torch.no_grad():
-        text_features = model.get_text_features(**inputs).cpu().numpy()  # Should work without crashing
-
-    results = collection.query(
-        query_embeddings=text_features[0],
-        n_results=n_retrieved
-    )
-
-    result_doc = pd.DataFrame(results['documents'][0], columns=["image_url"])
-    df_result = df_emb.merge(result_doc[['image_url']], on='image_url', how='inner')
-    # Remove columns fashion_clip_image, image_urls, and description
-    df_result = df_result.drop(columns=['fashion_clip_image', 'description', 'editor', 'publish_date', 'image_urls'])
-    return df_result.to_dict(orient='records')
-
-# if __name__ == "__main__":
-#     text = "dress"
-#     result = main_text_retrieve_images(text)
-#     print(result)
-

src1/generate_queries_alternative.py

@@ -1,619 +0,0 @@
-from src1.questions_queries import *
-import time
-import ollama
-import uuid
-import chromadb
-import openai 
-openai_api_key = "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"
-
-import spacy
-nlp = spacy.load("en_core_web_sm")
-import os
-#os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-wikibase_properties_id = {'instance of': 'P2',
- 'reference URL': 'P24',
- 'start time': 'P15',
- 'end time': 'P16',
- 'occupation title': 'P25',
- 'educated at': 'P9',
- 'employer': 'P10',
- 'work location': 'P7',
- 'award received': 'P18',
- 'point in time': 'P28',
- 'exact match': 'P23',
- 'date of birth': 'P3',
- 'place of birth': 'P4',
- 'date of death': 'P5',
- 'country of citizenship': 'P6',
- 'occupation': 'P19',
- 'sex or gender': 'P8',
- 'official website': 'P17',
- 'perfumes': 'P27',
- 'who wears it': 'P26',
- 'inception': 'P11',
- 'headquarters location': 'P12',
- 'parent organization': 'P13',
- 'founded by': 'P14',
- 'owned by': 'P22',
- 'industry': 'P20',
- 'country': 'P30',
- 'total revenue': 'P21',
- 'designer employed': 'P29',
- 'country of origin': 'P30',
- 'fashion collection': 'P31',
- 'fashion season': 'P32',
- 'fashion show location': 'P33',
- 'description of fashion collection': 'P34',
- 'image of fashion collection': 'P35',
- 'editor of fashion collection description': 'P36',
- 'date of fashion collection': 'P37',
- 'fashion show category': 'P38',
- 'fashion house X fashion collection': 'P39'}
-
-classes_wikibase = {'fashion designer': 'Q5',
- 'fashion house': 'Q1',
- 'business': 'Q9',
- 'academic institution': 'Q2',
- 'geographic location': 'Q4',
- 'fashion award': 'Q8',
- 'gender': 'Q6',
- 'occupation': 'Q7',
- 'human': 'Q36',
- 'organization': 'Q3',
- 'brand': 'Q38',
- 'lifestyle brand': 'Q3417',
- 'privately held company': 'Q1729',
- 'fashion season': 'Q8199',
- 'fashion show category': 'Q8200',
- 'fashion season collection': 'Q8201',
- 'fashion journalist': 'Q8207'}
-
-questions_queries_all = [{ "question": education_most_popular_question, "query": education_most_popular_query},
-                          { "question": how_many_designers_per_fashion_house_question, "query": how_many_designers_per_fashion_house_query},
-                           {"question": how_many_directors_per_fashion_house_question, "query": how_many_directors_per_fashion_house_query}, 
-                           {"question": designers_multiple_houses_question, "query":designers_multiple_houses_query }, 
-                           {"question": award_question, "query": award_question},
-                           {"question": fashion_houses_with_collections_question, "query": fashion_houses_with_collections_query},
-                           {"question": popular_year_inception_question, "query": popular_year_inception_query},
-                           {"question": longest_serving_director_question, "query": longest_serving_director_query},
-                           {"question": houses_most_collections_question, "query": houses_most_collections_query},
-                           {"question": collections_sustainability_theme_question, "query": collections_sustainability_theme_query},
-                           {"question": collections_jeans_question, "query": collections_jeans_query},
-                           {"question": creative_directors_school_question, "query": creative_directors_school_query}, 
-                           {"question": fashion_houses_thematic_collection_question, "query": fashion_houses_thematic_collection_query},
-                           {"question": fashion_house_directors_question.substitute({ "x": f"{"Chanel"}"}), "query": fashion_house_directors_query.substitute({ "x": f"'{"Chanel"}'"})},
-                          { "question": designer_fashion_house_directors_question.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_fashion_house_directors_query.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          {"question": country_designer_question.substitute({ "x": f"{"Italy"}"}), "query": country_designer_query.substitute({ "x": f"'{"Italy"}'"})},
-                          {  "question": designer_order_fashion_collection_question.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_order_fashion_collection_query.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          {"question": designer_fashion_director_question2.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_fashion_director_query2.substitute({ "x": f"'{"Alexander McQueen"}'"})},
-                          { "question": year_designers_birth_question.substitute({ "x": 1970}), "query": year_designers_birth_query.substitute({ "x": 1970})},
-                          { "question": fashion_collection_images_question.substitute({ "x": f"{"Chanel"}"}), "query": fashion_collection_images_query.substitute({ "x": f"'{"Chanel"}'"})}
-                           ]
-
-if os.path.exists("web_app/query_log.json"):
-    other_pairs = pd.read_json("web_app/query_log.json")
-    other_pairs_success = other_pairs[(other_pairs["status"] == "Success") & (other_pairs["feedback"] == "good")]
-    other_pairs_success = other_pairs_success[["question", "query"]]
-    questions_queries_all = questions_queries_all + other_pairs_success.to_dict(orient='records')
-
-    #print only the questions
-    #print([q["question"] for q in questions_queries_all])
-    
-    
-
-
-
-def mask_entities(text, nlp):
-    doc = nlp(text)
-    masked_text = text
-    for ent in doc.ents:
-        masked_text = masked_text.replace(ent.text, "[ENTITY]")
-    return masked_text
-
-
-
-
-import re
-import spacy
-
-def replace_entity(original_question, to_do_question, query):
-    """
-    Replaces entities in the query using entities from to_do_question while preserving quotation marks.
-    Handles multiple entity replacements and numerical entity replacements.
-    """
-    nlp = spacy.load("en_core_web_sm")
-    
-    original_doc = nlp(original_question)
-    to_do_doc = nlp(to_do_question)
-    
-    # Extract entities from both questions
-    original_entities = [ent.text for ent in original_doc.ents]
-    to_do_entities = [ent.text for ent in to_do_doc.ents]
-    # print("Original Entities:", original_entities)
-    # print("To-Do Entities:", to_do_entities)
-
-    # Create entity mapping
-    entity_mapping = {}
-
-    for orig_ent, new_ent in zip(original_entities, to_do_entities):
-        # Find numbers in each entity
-        orig_numbers = re.findall(r"\d+", orig_ent)
-        new_numbers = re.findall(r"\d+", new_ent)
-
-        if orig_numbers and new_numbers and len(orig_numbers) == len(new_numbers):
-            # If multiple numbers, replace each one
-            for orig_num, new_num in zip(orig_numbers, new_numbers):
-                entity_mapping[orig_num] = new_num
-        else:
-            # Otherwise, replace entire entity
-            entity_mapping[orig_ent] = new_ent
-
-    #print("Entity Mapping:", entity_mapping)
-
-    # Replace entities in the query
-    for orig, new in entity_mapping.items():
-        query = re.sub(rf'("{orig}"|\b{re.escape(orig)}\b)', 
-                       lambda match: f'"{new}"' if match.group(0).startswith('"') else new, 
-                       query)
-
-    return query
-
-
-def capitalize_sentences(sentences):
-    """
-    Ensures that each sentence in a list starts with an uppercase letter.
-    """
-    capitalized_sentences = []
-    
-    for sentence in sentences:
-        sentence = sentence.strip()  # Remove leading/trailing spaces
-        if sentence:  # Check if the sentence is not empty
-            sentence = sentence[0].upper() + sentence[1:]  # Capitalize first letter
-        capitalized_sentences.append(sentence)
-    
-    return capitalized_sentences
-
-
-
-import numpy as np
-import spacy
-
-
-
-def similarity_question(question, questions_queries_dictionary, collection, n_results=5, threshold=0.15):
-    """
-    Removes duplicate embeddings and retrieves similar questions.
-    """
-    nlp = spacy.load("en_core_web_sm")  # Load spaCy model for entity recognition
-    
-    original_documents = [questions_queries_dictionary[i]["question"] for i in range(len(questions_queries_dictionary))]
-    masked_documents = [mask_entities(q, nlp) for q in original_documents]
-
-    # Dictionary to store unique embeddings
-    unique_embeddings = {}
-
-    # Store each unique document in the vector embedding database
-    for i, d in enumerate(masked_documents):
-        response = ollama.embed(model="mxbai-embed-large", input=d)
-        embedding = response["embeddings"][0]  # Extract the first (and only) embedding from the nested list
-        
-        # Check if embedding is unique
-        is_duplicate = any(np.allclose(embedding, np.array(e), atol=1e-6) for e in unique_embeddings.values())
-        if not is_duplicate:
-            unique_embeddings[str(i)] = embedding  # Store unique embedding as a list
-            
-            collection.add(
-                ids=[str(i)],
-                embeddings=[embedding],  # Ensure this is a list of lists
-                documents=[d]
-            )
-    
-    # Compute the embedding for the input question
-    masked_question = mask_entities(question, nlp)
-    response = ollama.embed(model="mxbai-embed-large", input=masked_question)
-    query_embedding = response["embeddings"][0]  # Extract embedding
-
-    results = collection.query(
-        query_embeddings=[query_embedding],  # Ensure correct format
-        n_results=n_results
-    )
-
-    triples = []
-    for i in range(len(results['documents'][0])):
-        masked_similar_question = results['documents'][0][i]
-        distance = results['distances'][0][i]
-        print(distance)
-        paraphrase = distance < threshold
-        
-        # Find the corresponding original question
-        index_similar_query = masked_documents.index(masked_similar_question)
-        original_similar_question = original_documents[index_similar_query]
-        similar_query = questions_queries_dictionary[index_similar_query]["query"]
-        
-        if paraphrase and "[ENTITY]" in masked_similar_question and "[ENTITY]" in masked_question:
-            to_do_query = replace_entity(original_similar_question, question, similar_query)
-        else:
-            to_do_query = None
-        
-        triples.append((original_similar_question, similar_query, to_do_query))
-    
-    return triples
-
-
-def similarity_question_no_masking(question, questions_queries_dictionary, collection, n_results=5, threshold=0.15):
-    """
-    Removes duplicate embeddings and retrieves similar questions.
-    """
-    
-    original_documents = [questions_queries_dictionary[i]["question"] for i in range(len(questions_queries_dictionary))]
- 
-
-    # Dictionary to store unique embeddings
-    unique_embeddings = {}
-
-    # Store each unique document in the vector embedding database
-    for i, d in enumerate(original_documents):
-        response = ollama.embed(model="mxbai-embed-large", input=d)
-        embedding = response["embeddings"][0]  # Extract the first (and only) embedding from the nested list
-        
-        # Check if embedding is unique
-        is_duplicate = any(np.allclose(embedding, np.array(e), atol=1e-6) for e in unique_embeddings.values())
-        if not is_duplicate:
-            unique_embeddings[str(i)] = embedding  # Store unique embedding as a list
-            
-            collection.add(
-                ids=[str(i)],
-                embeddings=[embedding],  # Ensure this is a list of lists
-                documents=[d]
-            )
-    
-    # Compute the embedding for the input question
-    
-    response = ollama.embed(model="mxbai-embed-large", input=question)
-    query_embedding = response["embeddings"][0]  # Extract embedding
-
-    results = collection.query(
-        query_embeddings=[query_embedding],  # Ensure correct format
-        n_results=n_results
-    )
-
-    triples = []
-    for i in range(len(results['documents'][0])):
-        similar_question = results['documents'][0][i]
-        distance = results['distances'][0][i]
-        print(distance)
-        paraphrase = distance < threshold
-        
-        # Find the corresponding original question
-        index_similar_query = original_documents.index(similar_question)
-        original_similar_question = original_documents[index_similar_query]
-        similar_query = questions_queries_dictionary[index_similar_query]["query"]
-        
-        to_do_query = similar_query if paraphrase else None
-        
-        triples.append((original_similar_question, similar_query, to_do_query))
-    
-    return triples
-
-def select_dict(dict, keys):
-    return {k: dict[k] for k in keys if k in dict}
-
-def prompt_template(to_do_question,triples_examples,wikibase_properties_id,how_many_examples = 1, ):
-
-    questions = [triples_examples[i][0] for i in range(len(triples_examples))][:how_many_examples]
-    print("EXAMPLE QUESTION(s): ",questions)
-
-    classes_wikibase_selection = select_dict(classes_wikibase, ["fashion house", "fashion designer"])
-    general_properties = select_dict(wikibase_properties_id, ["instance of", "reference URL", "start time", "end time", "occupation title", "point in time", "official website"])
-    general_properties["rdfs:label"] = "rdfs:label"
-    designer_properties = select_dict(wikibase_properties_id, ["employer", "educated at", "work location", "award received", "date of birth", "date of death", "place of birth",  "country of citizenship", "occupation", "sex or gender"])
-    fashion_house_properties = select_dict(wikibase_properties_id, ["inception","headquarters location", "parent organization", "founded by","owned by", "industry", "country", "total revenue", "designer employed", "fashion collection", "description of fashion collection","image of fashion collection"])
-    fashion_collection_properties = select_dict(wikibase_properties_id, ["fashion show category", "fashion show location", "fashion season"])
-    qualifier_properties = select_dict(wikibase_properties_id, ["start time", "end time", "occupation title", "point in time","description of fashion collection","image of fashion collection"])
-    
-    prompt = f"""You are an expert in translating natural language questions into SPARQL queries for FashionDB - a knwoledge graph about Fashion.
-    I provide you with the ontology of FashionDB. The properties are stored in a dictionary as property_label: property_id. The classes are stored in a dictionary as class_label: class_id.
-    General Properties: {general_properties}, Fashion Designer Properties: {designer_properties}, Fashion House Properties: {fashion_house_properties}, Fashion Collection Properties: {fashion_collection_properties}. 
-    In particular the following properties are always qualifiers thus their prefix is always pq: {qualifier_properties}.
-    Classes: {classes_wikibase_selection}.
-    Remember to use the entities presented in Natural language question to translate , when generating the corresponding SPARQL query.
-    
-    
-    I provide you with example."""
-    for i in range(len(questions)):
-        prompt += f""" Example question: {triples_examples[i][0]}
-                    Corresponding SPARQL query:{triples_examples[i][1]} """
-    prompt += f""" Question to translate to SPARQL: {to_do_question}
-    Remember that the use case is FASHION: if there is a mispelling of a fashion designer or house, you can adjust it according to your knowledge of fashion. Example: "balenciaho" should be "Balenciaga". 
-                Your generated corresponding SPARQL query: """
-
-    return prompt
-
-
-def prompt_template_gemma2(to_do_question, triples_examples, wikibase_properties_id, how_many_examples=1):
-    questions = [triples_examples[i][0] for i in range(len(triples_examples))][:how_many_examples]
-    print("EXAMPLE QUESTION(s): ",questions)
-    classes_wikibase_selection = select_dict(classes_wikibase, ["fashion house", "fashion designer"])
-    general_properties = select_dict(wikibase_properties_id, ["instance of", "reference URL", "start time", "end time", "occupation title", "point in time", "official website"])
-    general_properties["rdfs:label"] = "rdfs:label"
-    designer_properties = select_dict(wikibase_properties_id, ["employer", "educated at", "work location", "award received", "date of birth", "date of death", "place of birth", "country of citizenship", "occupation", "sex or gender"])
-    fashion_house_properties = select_dict(wikibase_properties_id, ["inception", "headquarters location", "parent organization", "founded by", "owned by", "industry", "country", "total revenue", "designer employed", "fashion collection", "description of fashion collection", "image of fashion collection"])
-    fashion_collection_properties = select_dict(wikibase_properties_id, ["fashion show category", "fashion show location", "fashion season"])
-    qualifier_properties = select_dict(wikibase_properties_id, ["start time", "end time", "occupation title", "point in time", "description of fashion collection", "image of fashion collection"])
-    
-    prompt = f"""
-    You are an expert in translating natural language fashion-related questions into **SPARQL queries** for **FashionDB**, a knowledge graph about fashion.
-
-    ---
-    ## **FashionDB Ontology**
-    - **Classes**: {classes_wikibase_selection}
-    - **General Properties**: {general_properties}
-    - **Fashion Designer Properties**: {designer_properties}
-    - **Fashion House Properties**: {fashion_house_properties}
-    - **Fashion Collection Properties**: {fashion_collection_properties}
-    - **Qualifier Properties** (always prefixed with `pq:`): {qualifier_properties}
-
-    ---
-    ## **Instructions**
-    - **Fix misspellings** of fashion brands and designers before generating the query.
-        - Example: "Guxci" → **"Gucci"**, "Balenciaho" → **"Balenciaga"**.
-    - If a brand or designer **isn't recognized**, **make a reasonable correction** based on common fashion knowledge.
-    - Handle **abstract or conceptual fashion questions**, such as:
-        - "Which fashion houses have had the most influence in the 20th century?"
-        - "What are the key design trends in haute couture from the 1990s?"
-    - **Always return a valid SPARQL query** using the provided ontology.
-
-    ---
-    ## **Example(s)**
-    """
-    
-    for i in range(len(questions)):
-        prompt += f"""
-        **Example {i+1}**  
-        - **Question**: {triples_examples[i][0]}  
-        - **SPARQL Query**:  
-        ```sparql
-        {triples_examples[i][1]}
-        ```
-        """
-
-    prompt += f"""
-    ---
-    ## **Your Task**
-    **Question**: {to_do_question}
-
-    **SPARQL Query:**  
-    ```sparql
-    """
-
-    return prompt
-
-def prompt_template_gpt4o_mini(to_do_question, triples_examples, wikibase_properties_id, how_many_examples=1):
-    questions = [triples_examples[i][0] for i in range(len(triples_examples))][:how_many_examples]
-    
-    classes_wikibase_selection = select_dict(classes_wikibase, ["fashion house", "fashion designer"])
-    general_properties = select_dict(wikibase_properties_id, ["instance of", "reference URL", "start time", "end time", "occupation title", "point in time", "official website"])
-    general_properties["rdfs:label"] = "rdfs:label"
-    designer_properties = select_dict(wikibase_properties_id, ["employer", "educated at", "work location", "award received", "date of birth", "date of death", "place of birth", "country of citizenship", "occupation", "sex or gender"])
-    fashion_house_properties = select_dict(wikibase_properties_id, ["inception", "headquarters location", "parent organization", "founded by", "owned by", "industry", "country", "total revenue", "designer employed", "fashion collection", "description of fashion collection", "image of fashion collection"])
-    fashion_collection_properties = select_dict(wikibase_properties_id, ["fashion show category", "fashion show location", "fashion season"])
-    qualifier_properties = select_dict(wikibase_properties_id, ["start time", "end time", "occupation title", "point in time", "description of fashion collection", "image of fashion collection"])
-    
-    prompt = f"""
-    You are a **SPARQL expert** specializing in **FashionDB**, a knowledge graph about fashion.
-
-    ### **Your Task**
-    - Translate the given **natural language question** into a **valid SPARQL query**.
-    - **Fix spelling mistakes** of fashion brands and designers.
-        - Example: "Guxci" → "Gucci", "Balenciaho" → "Balenciaga".
-    - If a brand or designer isn't recognized, **guess the correct name** based on fashion industry knowledge.
-    - Support **abstract fashion questions**, such as:
-        - "How did Dior's designs evolve over the decades?"
-        - "Which fashion houses had the biggest impact on 21st-century streetwear?"
-    - Your **SPARQL query must use the correct ontology**.
-
-    ---
-    ### **FashionDB Ontology**
-    - **Classes**: {classes_wikibase_selection}
-    - **General Properties**: {general_properties}
-    - **Fashion Designer Properties**: {designer_properties}
-    - **Fashion House Properties**: {fashion_house_properties}
-    - **Fashion Collection Properties**: {fashion_collection_properties}
-    - **Qualifier Properties (always prefixed with `pq:`)**: {qualifier_properties}
-
-    ---
-    ### **Example(s)**
-    """
-    
-    for i in range(len(questions)):
-        prompt += f"""
-        **Example {i+1}**  
-        - **Question**: {triples_examples[i][0]}  
-        - **SPARQL Query**:  
-        ```sparql
-        {triples_examples[i][1]}
-        ```
-        """
-
-    prompt += f"""
-    ---
-    ### **Now Translate This Question**
-    **Question**: {to_do_question}
-
-    **SPARQL Query:**  
-    ```sparql
-    """
-
-    return prompt
-
-#generate
-def send_chat_prompt(prompt, model, system_prompt ):
-    client = openai.OpenAI(
-            base_url="http://localhost:11434/v1" if not "gpt" in model else None,
-            api_key= "ollama" if not "gpt" in model else openai_api_key)
-    resp = client.chat.completions.create(
-        model=model,
-        temperature = 0.5 ,
-        messages=[
-                {"role": "system", "content": system_prompt},  
-                {"role": "user", "content": prompt}])
-    response = resp.choices[0].message.content
-    return response
-
-def main_generate(prompt,model, system_prompt):
-    response = send_chat_prompt(prompt,model, system_prompt)
-    response = response.replace('```','').replace('json','')
-    #print(f" {model} Response:", response)
-    return response
-
-
-#validate
-def replace_last_occurrence(s, pattern, replacement):
-    pos = s.rfind(pattern)  # Find the last occurrence of the pattern
-    if pos != -1:
-        return s[:pos] + s[pos:].replace(pattern, replacement, 1)
-        
-def validation_query(sparql_query):
-    if sparql_query.startswith("sparql"):
-        sparql_query = sparql_query[6:]
-    #if last character is \n remove it 
-    while sparql_query[-1] == "\n" or sparql_query[-1] == " ":
-        sparql_query = sparql_query[:-1]
-
-    if sparql_query[-1] == ".":
-        sparql_query = sparql_query[:-1]
-    sparql_query = sparql_query.encode().decode('unicode_escape')
-    sparql_query = sparql_query.replace("wdt", "wbt")
-    if "SERVICE" not in sparql_query:
-        sparql_query = replace_last_occurrence(sparql_query, "}", "SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } \n }")
-    
-    return sparql_query
-
-
-def safe_get_results(query, max_retries=3):
-    """
-    Safely executes a SPARQL query, handling HTTP errors gracefully.
-    
-    Parameters:
-    - query (str): The SPARQL query to execute.
-    - max_retries (int): Number of retries before failing.
-    
-    Returns:
-    - DataFrame: Query results, or an empty DataFrame if the query fails.
-    """
-    for attempt in range(max_retries):
-        try:
-            return get_results_to_df(query)  # Attempt to execute the query
-        except requests.exceptions.HTTPError as e:
-            print(f"Attempt {attempt + 1}: Query failed with HTTPError {e}")
-            time.sleep(2)  # Wait before retrying
-        except Exception as e:
-            print(f"Attempt {attempt + 1}: Unexpected error {e}")
-            time.sleep(2)
-
-    print("All attempts failed. Returning empty DataFrame.")
-    return pd.DataFrame()  # Return empty DataFrame if all retries fail
-
-
-
-def correction_question_prompt(to_do_question):
-    correction_prompt = f"""
-    You are an expert in **fashion brand and designer names**.
-    Your task is to **correct misspellings** in the given question while keeping its original meaning.
-    If you recognize a fashion-related name that is misspelled, **fix it**.
-    If nothing is wrong, generate the Question to Correct.
-    Don't generate **.
-
-    ### **Examples**
-    - "Who founded Guxci?" → "Who founded Gucci?"
-    - "What is balenciaho famous for?" → "What is Balenciaga famous for?"
-    - "Who is the head designer of gucxi?" → "Who is the head designer of Gucci?"
-
-    ### **Question to Correct**
-    {to_do_question}
-
-    ### **Corrected Version**
-    """
-    return correction_prompt
-
-def initialize_collection():
-    # Initialize ChromaDB client
-    client = chromadb.Client()
-    # If the collection already exists, delete it to start fresh.
-    try:
-        client.delete_collection(name="docs")  # Delete the existing collection
-    except:
-        pass  
-    # Re-create the collection for each query
-    collection = client.create_collection(name="docs")
-    return collection
-
-
-
-
-
-def main_generate_queries(to_do_question):
-    
-    # # Initialize ChromaDB client
-    # client = chromadb.Client()
-    # # If the collection already exists, delete it to start fresh.
-    # try:
-    #     client.delete_collection(name="docs")  # Delete the existing collection
-    # except:
-    #     pass  
-    # # Re-create the collection for each query
-    # collection = client.create_collection(name="docs")
-    collection = initialize_collection()
-    triples_examples = similarity_question(to_do_question, questions_queries_all, collection)
-    if triples_examples[0][2] is not None:
-        print("it's a paraphrase :)")
-        sparql_query = triples_examples[0][2]
-        print(triples_examples[0][0])
-        result_query = safe_get_results(sparql_query)
-        if result_query.empty:
-            to_do_question = main_generate(correction_question_prompt(to_do_question), "gemma2", "You have to fix the mispellings of the Question to Correct")
-            print(to_do_question)
-            sparql_query = replace_entity(triples_examples[0][0], to_do_question, triples_examples[0][1])
-            result_query = safe_get_results(sparql_query)
-        print(sparql_query)
-        if not result_query.empty:
-            return result_query.to_dict(orient='records'), sparql_query
-
-    prompt = prompt_template_gemma2(to_do_question, triples_examples, wikibase_properties_id, how_many_examples=1)
-
-    sparql_query = main_generate(prompt, "gemma2", "You are a natural language to SPARQL language translator. Do only generate the SPARQL query, nothing else.")
-    sparql_query = validation_query(sparql_query)
-    result_query = safe_get_results(sparql_query)
-
-    print(sparql_query)
-    if result_query.empty:
-        to_do_question = main_generate(correction_question_prompt(to_do_question), "gemma2", "You have to fix the mispellings of the Question to Correct")
-        print(to_do_question)
-        prompt = prompt_template_gemma2(to_do_question, triples_examples, wikibase_properties_id, how_many_examples=2)
-        sparql_query = main_generate(prompt, "gemma2", "You are a natural language to SPARQL language translator. Do only generate the SPARQL query, nothing else.")
-        sparql_query = validation_query(sparql_query)
-        result_query = safe_get_results(sparql_query)
-        if result_query.empty:
-            new_collection = initialize_collection()
-            triples_examples_no_masked = similarity_question_no_masking(to_do_question, questions_queries_all, new_collection)
-            prompt = prompt_template_gemma2(to_do_question, triples_examples_no_masked, wikibase_properties_id, how_many_examples=2)
-            sparql_query = main_generate(prompt, "gemma2", "You are a natural language to SPARQL language translator. Do only generate the SPARQL query, nothing else.")
-            sparql_query = validation_query(sparql_query)
-            result_query = safe_get_results(sparql_query)
-            print(sparql_query)
-            if result_query.empty:
-                text_generated =  main_generate(to_do_question, "gemma2", "You are an expert in fashion. Just provide the answer to the question.")
-                return text_generated, sparql_query
-
-    print(sparql_query)
-    print(result_query)
-    
-    return result_query.to_dict(orient='records'), sparql_query
-
-#main("What is the inception of Chanel?")
-if __name__ == "__main__":
-    #main("Which fashion designers being creative directors were born in Italy?")
-    #main_generate_queries("Which fashion houses had collections with jeans in their descriptions and how many of the collections have jeans?")
-    main_generate_queries("Which designers were born in 1970?")
-

src1/questions_queries.py

@@ -1,863 +0,0 @@
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-from urllib.parse import urlparse
-import logging
-log = logging.getLogger(__name__)
-from time import sleep
-import requests
-helpers_session = requests.Session()
-from wikibaseintegrator.wbi_helpers import get_user_agent
-import pandas as pd
-from string import Template
-
-
-wikibase_api_url = 'https://fashionwiki.wikibase.cloud/w/api.php'
-config = {
-  "SPARQL_ENDPOINT_URL": "https://fashionwiki.wikibase.cloud/query/sparql",
-  'USER_AGENT':  'YourBotName/1.0 (https://yourwebsite.org/bot-info)',#"Traopia",
-  'WIKIBASE_URL': wikibase_api_url,
-  }
-
-def execute_sparql_query(query: str, prefix: str | None = None, endpoint: str | None = None, user_agent: str | None = None, max_retries: int = 1000, retry_after: int = 60) -> dict:
-    """
-    Execute any SPARQL query with the provided parameters.
-    """
-
-    sparql_endpoint_url = str(endpoint or config['SPARQL_ENDPOINT_URL'])
-    user_agent = user_agent or (str(config['USER_AGENT']) if config['USER_AGENT'] is not None else None)
-
-    hostname = urlparse(sparql_endpoint_url).hostname
-    if hostname and hostname.endswith(('wikidata.org', 'wikipedia.org', 'wikimedia.org')) and user_agent is None:
-        log.warning('WARNING: Please set a user agent if you interact with a Wikimedia Foundation instance.')
-
-    if prefix:
-        query = prefix + '\n' + query
-
-    headers = {
-        'Accept': 'application/sparql-results+json',
-        'User-Agent': get_user_agent(user_agent),
-        'Content-Type': 'application/sparql-query'  # Correct Content-Type
-    }
-
-    # Attempt to make the request
-    for _ in range(max_retries):
-        try:
-            # Use 'data' instead of 'params' for the POST request to SPARQL
-            response = helpers_session.post(sparql_endpoint_url, data=query, headers=headers)
-        except requests.exceptions.ConnectionError as e:
-            log.exception("Connection error: %s. Sleeping for %d seconds.", e, retry_after)
-            sleep(retry_after)
-            continue
-        if response.status_code in (500, 502, 503, 504):
-            log.error("Service unavailable (HTTP Code %d). Sleeping for %d seconds.", response.status_code, retry_after)
-            sleep(retry_after)
-            continue
-        if response.status_code == 429:
-            if 'retry-after' in response.headers:
-                retry_after = int(response.headers['retry-after'])
-            log.error("Too Many Requests (429). Sleeping for %d seconds", retry_after)
-            sleep(retry_after)
-            continue
-        response.raise_for_status()  # Raise any non-success status code
-        return response.json()  # Return the JSON result if successful
-
-    raise Exception(f"No result after {max_retries} retries.")
-
-
-def get_results_to_df( query):
-    results = execute_sparql_query(query)
-    df = pd.DataFrame(results["results"]["bindings"])
-    df = df.map(lambda x: x['value'] if pd.notnull(x) else None)
-    return df
-
-fashion_collection_images_question = Template("Images of collections produced by $x")
-fashion_collection_images_query = Template("""
-PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?statement ?fashion_collectionLabel ?image_url ?reference_URL 
-  ?fashion_seasonLabel (YEAR(?fashion_year) AS ?year) ?categoryLabel WHERE {
-    { ?fashion_house  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?fashion_house  skos:altLabel $x@en. }  # Match alias   
-                                           
-  
-  ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?fashion_collection.
-  OPTIONAL { ?fashion_collection wbt:P32 ?fashion_season. }
-  OPTIONAL {?fashion_collection wbt:P28 ?fashion_year.}
-  OPTIONAL {?fashion_collection wbt:P38 ?category.}
-  
-  OPTIONAL {
-    ?statement pq:P35 ?image_url.
-  }
-
-                                                
-  OPTIONAL {
-    ?statement prov:wasDerivedFrom ?reference.
-    ?reference pr:P24 ?reference_URL. 
-  }
-
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } }
-                                           """)
-
-year_designers_birth_question = Template("Which designers were born in $x?")
-year_designers_birth_query = Template("""
-PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
-
-SELECT ?designerLabel ?birthdate  WHERE {
-  ?designer wbt:P3 ?birthdate .
-  FILTER (YEAR(?birthdate) = $x)
-
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-} """)
-
-fashion_house_directors_question = Template("Which designers have been creative directors of $x ?")
-fashion_house_directors_query = Template("""
-PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT 
-?creative_directorLabel (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year)
-  (YEAR(?end_time) AS ?end_year) ?title ?statement ?reference_URL_source
-WHERE {
-  # Fashion house identified by brand name
-  ?fashion_house wbt:P2 wb:Q1.                                       
-    { ?fashion_house rdfs:label $x@en.}  # Match label
-  UNION
-  { ?fashion_house skos:altLabel $x@en. }  # Match alias   
-  
-  # Link through designer employed (P29) property for the fashion house  - the property for designer employment is P10
-  {
-    ?fashion_house p:P29 ?statement.
-    ?statement ps:P29 ?creative_director.
-
-    # Optional qualifiers for P29 regarding the time frame
-    OPTIONAL { ?statement pq:P15 ?start_time. }
-    OPTIONAL { ?statement pq:P28 ?point_time. } 
-    OPTIONAL { ?statement pq:P16 ?end_time. }
-    OPTIONAL { ?statement pq:P25 ?title. }
-    OPTIONAL { ?fashion_house wbt:P11 ?inception. } 
-
-    
-    # Retrieve references for P29
-    OPTIONAL {
-      ?statement prov:wasDerivedFrom ?reference.
-      ?reference pr:P24 ?reference_URL.
-      BIND(
-        IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-      )
-      BIND(
-        IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-      )
-    }
-  }
-  UNION
-  # Link through founded_by (P14) property for the fashion house
-  {
-    ?fashion_house p:P14 ?statement.
-    ?statement ps:P14 ?creative_director.
-
-    # Optional qualifiers for P14 (start_year as inception)
-    OPTIONAL { ?statement pq:P15 ?start_time. }
-    OPTIONAL { ?statement pq:P28 ?point_time. } 
-    OPTIONAL { ?statement pq:P16 ?end_time. }
-    OPTIONAL { ?statement pq:P25 ?title. }
- 
-    
-    # Retrieve references for P14
-    OPTIONAL {
-      ?statement prov:wasDerivedFrom ?reference.
-      ?reference pr:P24 ?reference_URL.
-      BIND(
-        IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-      )
-      BIND(
-        IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-      )
-    }
-  }
-
-  # Filter for relevant titles (creative director, founder, etc.) to retrieve relevant employment as creative director
-  FILTER (
-    REGEX(?title, "director", "i") || 
-    REGEX(?title, "head designer", "i") ||
-    REGEX(?title, "chief", "i") ||
-    REGEX(?title, "founder", "i")                                   
-  )
-  FILTER(?reference_URL_source != "")
-  # Retrieve labels
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}   ORDER BY ?start_year """)
-
-
-
-designer_fashion_house_directors_question = Template("In which fashion house did $x work as a creative director?")
-designer_fashion_house_directors_query = Template("""  
-PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel 
-       (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year) 
-       (YEAR(?end_time) AS ?end_year) 
-       ?title ?reference_URL_source ?statement 
-WHERE {
-   # Designer linked to fashion house through roles or founded_by
-    ?fashion_designer wbt:P2 wb:Q5.                                              
-     { ?fashion_designer  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?fashion_designer  skos:altLabel $x@en. }  # Match alias   
-   
-   # Link through employer (P10)
-   ?fashion_designer p:P10 ?statement.
-   ?statement ps:P10 ?fashion_house.
-  
-   # Optional start and end time qualifiers for roles
-   OPTIONAL { ?statement pq:P15 ?start_time. }
-   OPTIONAL { ?statement pq:P28 ?point_time. } 
-   OPTIONAL { ?statement pq:P16 ?end_time. }
-   OPTIONAL { ?statement pq:P25 ?title. }
-
-   # Inception as fallback for start year
-   OPTIONAL { ?fashion_house wbt:P11 ?inception. }
-
-   # Filter for relevant roles (director, founder, etc.)
-   FILTER (
-       REGEX(?title, "director", "i") || 
-       REGEX(?title, "head designer", "i") ||
-       REGEX(?title, "chief","i") ||
-       REGEX(?title, "founder","i")                                   
-   )
-  
-   # Retrieve references for the statement
-    OPTIONAL {
-      ?statement prov:wasDerivedFrom ?reference.
-      ?reference pr:P24 ?reference_URL.
-      BIND(
-        IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-      )
-      BIND(
-        IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-      )
-    }
-  FILTER(?reference_URL_source != "")
-
-   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-}
-""")
-
-
-designer_fashion_director_question2 = Template("In which fashion house did $x work as a creative director?")
-designer_fashion_director_query2 = Template("""PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT 
-?fashion_houseLabel ?creative_directorLabel (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year)  (YEAR(?end_time) AS ?end_year) ?title ?statement ?reference_URL_source
-WHERE {
-  # Fashion designer identified by name
-  ?creative_director wbt:P2 wb:Q5.                                       
-    { ?creative_director  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?creative_director  skos:altLabel $x@en. }  # Match alias   
-
-  # Link through designer employed (P29) property for the fashion house  - the property for designer employment is P30
-  {
-    ?fashion_house p:P29 ?statement.
-    ?statement ps:P29 ?creative_director.
-
-    # Optional qualifiers for P29 regarding the time frame
-    OPTIONAL { ?statement pq:P15 ?start_time. }
-    OPTIONAL { ?statement pq:P28 ?point_time. } 
-    OPTIONAL { ?statement pq:P16 ?end_time. }
-    OPTIONAL { ?statement pq:P25 ?title. }
-    OPTIONAL { ?fashion_house wbt:P11 ?inception. } 
-
-    
-    # Retrieve references for P29
-    OPTIONAL {
-      ?statement prov:wasDerivedFrom ?reference.
-      ?reference pr:P24 ?reference_URL.
-      BIND(
-        IF(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt"), ?reference_URL, "") AS ?reference_URL_model
-      )
-      BIND(
-        IF(!(CONTAINS(STR(?reference_URL), "ollama") || CONTAINS(STR(?reference_URL), "gpt")), ?reference_URL, "") AS ?reference_URL_source
-      )
-    }
-  }
-  FILTER (
-    REGEX(?title, "director", "i") || 
-    REGEX(?title, "head designer", "i") ||
-    REGEX(?title, "chief", "i") ||
-    REGEX(?title, "founder", "i")                                   
-  )
-  FILTER(?reference_URL_source != "")
-
-  # Retrieve labels
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}   ORDER BY ?start_year """)
-
-country_designer_question = Template("Which designers are from $x ?")
-country_designer_query = Template("""PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_designerLabel   (SAMPLE(?statement) AS ?statement)  
-  (SAMPLE(?reference_URL) AS ?reference_URL) WHERE {
-  # Restrict to designers who are instances of fashion designer (Q5)
-
-  ?fashion_designer wbt:P2 wb:Q5.
-
-  # Access the statement for the country relationship
-  ?fashion_designer p:P6 ?statement.
-  ?statement ps:P6 ?country.
-    { ?country  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?country skos:altLabel $x@en. }  # Match alias   
-
-  # Retrieve references from the statement
-  OPTIONAL {
-    ?statement prov:wasDerivedFrom ?reference.
-    ?reference pr:P24 ?reference_URL.
-  }
-
-  # Retrieve labels for the fashion designer
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-} 
-GROUP BY ?fashion_designerLabel 
-ORDER BY ?fashion_designerLabel """)
-
-country_designer_query = Template("""PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_designerLabel (SAMPLE(?reference_URL) AS ?reference_URL) {
-  # Restrict to designers who are instances of fashion designer (Q5)
-
-  ?fashion_designer wbt:P2 wb:Q5.
-  ?fashion_designer wbt:P6 ?country.
-  ?fashion_designer p:P6 ?statement.
-    { ?country  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?country  skos:altLabel $x@en. }  # Match alias   
- 
-
-  # Retrieve references from the statement
-  OPTIONAL {
-    ?statement prov:wasDerivedFrom ?reference.
-    ?reference pr:P24 ?reference_URL.
-  }
-
-  # Retrieve labels for the fashion designer
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-} 
-GROUP BY ?fashion_designerLabel 
-ORDER BY ?fashion_designerLabel  """)
-
-
-designer_order_fashion_collection_question = Template("Which fashion collections did $x work on?")
-designer_order_fashion_collection_query = Template("""PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel 
-       (COALESCE(YEAR(?start_time), YEAR(?point_time), YEAR(?inception)) AS ?start_year) 
-       (YEAR(?end_time) AS ?end_year) 
-       ?title ?reference_URL_source ?statement ?fashion_collectionLabel ?image_url 
-      (YEAR(?fashion_year) as ?year ) ?fashion_seasonLabel ?categoryLabel WHERE {
-   # Designer linked to fashion house through roles or founded_by
-   ?fashion_designer wbt:P2 wb:Q5. 
-  { ?fashion_designer  rdfs:label $x@en.}  # Match label
-  UNION
-  { ?fashion_designer  skos:altLabel $x@en. }  # Match alias                                               
-                                            
-   
-   # Link through "creative director" or other roles
-   ?fashion_designer p:P10 ?statement_designer.
-   ?statement_designer ps:P10 ?fashion_house.
-   OPTIONAL { ?statement_designer pq:P15 ?start_time. }
-   OPTIONAL { ?statement_designer pq:P28 ?point_time. } 
-   OPTIONAL { ?statement_designer pq:P16 ?end_time. }
-   OPTIONAL { ?statement_designer pq:P25 ?title. }   
-    OPTIONAL { ?fashion_house wbt:P11 ?inception. }    
-    FILTER (
-       REGEX(?title, "director", "i") || 
-       REGEX(?title, "head designer", "i") ||
-       REGEX(?title, "chief","i") ||
-       REGEX(?title, "founder","i")                                   
-   )       
-  
-    ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?fashion_collection.
-  OPTIONAL {?fashion_collection wbt:P28 ?fashion_year.}
-    OPTIONAL { ?fashion_collection wbt:P32 ?fashion_season. }
-  OPTIONAL {?fashion_collection wbt:P38 ?category.}
-  
-  OPTIONAL {?statement pq:P35 ?image_url.}
-  OPTIONAL {?statement pq:P37 ?fashion_date.}
-  
- 
-    # Use the adjusted fashion year in the FILTER condition
-FILTER(
-    (YEAR(?start_time) <= YEAR(?fashion_year) && (!BOUND(?end_time) || YEAR(?end_time) >= YEAR(?fashion_year))) ||
-    (YEAR(?point_time) <= YEAR(?fashion_year)) ||
-    (REGEX(?title, "founder", "i") && YEAR(?inception) <= YEAR(?fashion_year))
-)
-
-   # Retrieve references for the statement
-   OPTIONAL {
-     ?statement prov:wasDerivedFrom ?reference.
-     ?reference pr:P24 ?reference_URL_source.
-   }
-
-   SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-} ORDER BY ?year """)
-
-
-education_most_popular_question = "What are the most popular educational institutions for fashion designers?"
-education_most_popular_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?schoolLabel (COUNT(?fashion_designer) AS ?designer_count) WHERE {
-  # Retrieve all fashion designers and their schools
-  ?fashion_designer wbt:P9 ?school.
-  
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}
-GROUP BY ?schoolLabel
-ORDER BY DESC(?designer_count) """
-
-brands_founded_question = Template("Which fashion houses were founded in $x")
-brands_founded_query = Template(""" PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-
-SELECT ?fashion_houseLabel ?inception 
-WHERE {
-    ?fashion_house wbt:P2 wb:Q1.
-    ?fashion_house wbt:P11 ?inception.
-
-    
-    FILTER(YEAR(?inception) = $x)
-    
-    SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}""")
-
-how_many_directors_per_fashion_house_question = "How many designers have worked for each fashion house as creative directors?"
-how_many_directors_per_fashion_house_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel (COUNT(DISTINCT ?fashion_designer) AS ?creative_director_count) WHERE {
-  # Match fashion houses linked to designers via employer property
-  ?fashion_designer p:P10 ?statement.
-  ?statement ps:P10 ?fashion_house.
-  
-  # Filter for roles related to creative direction
-  OPTIONAL { ?statement pq:P25 ?title. }
-  FILTER (
-    REGEX(?title, "creative director", "i") || 
-    REGEX(?title, "head designer", "i") ||
-    REGEX(?title, "chief", "i") ||
-    REGEX(?title, "founder", "i")
-  )
-  
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}
-GROUP BY ?fashion_houseLabel
-ORDER BY DESC(?creative_director_count)
-"""
-
-how_many_designers_per_fashion_house_question = "How many designers have worked for each fashion house?"
-how_many_designers_per_fashion_house_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel (COUNT(?designer) AS ?designer_count) WHERE {
-  ?fashion_house wbt:P29 ?designer .
-
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}
-GROUP BY ?fashion_houseLabel
-ORDER BY DESC(?designer_count) """
-
-
-designers_multiple_houses_question = "Which designers have worked for more than one fashion house?"
-designers_multiple_houses_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?designerLabel WHERE {
-  ?designer wbt:P10 ?house .
-  ?designer wbt:P10 ?anotherHouse .
-SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } 
- }
-GROUP BY ?designerLabel
-HAVING (COUNT(?house) > 1)"""
-
-award_question = "Which designers won which awards?"
-award_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?designerLabel ?awardLabel WHERE {
-  ?designer wbt:P18 ?award .
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}"""
-
-fashion_houses_with_collections_question ="Which distinct fashion houses have at least a fashion collection?"
-fashion_houses_with_collections_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT DISTINCT ?houseLabel WHERE {
-  ?house wbt:P31 ?collection .
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-} """
-
-popular_year_inception_question = "In which years were the most fashion houses founded?"
-popular_year_inception_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-
-SELECT ?year_inception (COUNT(DISTINCT ?fashion_house) AS ?count)
-WHERE {
-  ?fashion_house wbt:P11 ?year.
-  BIND(YEAR(?year) AS ?year_inception)
-}
-GROUP BY ?year_inception
-ORDER BY DESC(?count) """
-
-
-longest_serving_director_question = "Which fashion houses have had the longest-serving creative director?"
-longest_serving_director_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>
-PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
-PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
-
-SELECT ?fashion_houseLabel ?duration ?designerLabel
-WHERE {
-  ?fashion_house p:P29 ?statement.
-  ?statement ps:P29 ?designer.
-  OPTIONAL { ?statement pq:P15 ?startTime. }
-  OPTIONAL { ?statement pq:P16 ?endTime. }
-
-  # Ensure we only calculate when both dates exist
-  FILTER(BOUND(?startTime) && BOUND(?endTime))
-
-  # Compute duration safely
-  BIND(YEAR(?endTime) - YEAR(?startTime) AS ?duration)
-
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } 
-}
-ORDER BY DESC(?duration)"""
-
-houses_most_collections_question = "Which fashion houses have the most collections recorded in the database?"
-houses_most_collections_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashionHouseLabel (COUNT(?collection) AS ?collection_count) WHERE {
-  ?fashionHouse wbt:P31 ?collection .
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}
-GROUP BY ?fashionHouseLabel
-ORDER BY DESC(?collection_count)"""
-
-
-collections_sustainability_theme_question = "Which collections include sustainability as a key theme?"
-collections_sustainability_theme_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?collectionLabel ?fashion_houseLabel ?reference_URL WHERE {
-  ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?collection.
-  ?statement pq:P34 ?description .
-  FILTER (regex(?description, "sustainability", "i"))
-     # Retrieve references for the statement
-   OPTIONAL {
-     ?statement prov:wasDerivedFrom ?reference.
-     ?reference pr:P24 ?reference_URL.
-   }
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}  """ 
-
-
-collections_jeans_question = "Which collections showcase products made with jeans?"
-collections_jeans_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?collectionLabel ?fashion_houseLabel ?reference_URL WHERE {
-  ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?collection.
-  ?statement pq:P34 ?description .
-  FILTER (regex(?description, "jeans", "i"))
-     # Retrieve references for the statement
-   OPTIONAL {
-     ?statement prov:wasDerivedFrom ?reference.
-     ?reference pr:P24 ?reference_URL.
-   }
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-}  """ 
-
-
-creative_directors_school_question = "To which school went most designers who later became creative directors of fashion houses?"
-creative_directors_school_query = """ PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?schoolLabel (COUNT(DISTINCT ?designer) AS ?designer_count) WHERE {
-  ?designer p:P10 ?fashion_house .
-
-     OPTIONAL { ?fashion_house pq:P25 ?title. }
-  FILTER (
-    REGEX(?title, "creative director", "i") || 
-    REGEX(?title, "head designer", "i") ||
-    REGEX(?title, "chief", "i") ||
-    REGEX(?title, "founder", "i")
-  )
-  
-  OPTIONAL { ?designer wbt:P9 ?school . }
-SERVICE wikibase:label { bd:serviceParam wikibase:language 'en'. } 
- }
-GROUP BY ?schoolLabel
-ORDER BY DESC(?designer_count)"""
-
-fashion_houses_thematic_collection_question = "Which fashion houses had collections with a sustainability theme, and how many of them?"
-fashion_houses_thematic_collection_query = """PREFIX wbt: <https://fashionwiki.wikibase.cloud/prop/direct/>
-PREFIX wb: <https://fashionwiki.wikibase.cloud/entity/>
-PREFIX pq: <https://fashionwiki.wikibase.cloud/prop/qualifier/>  
-PREFIX pr: <https://fashionwiki.wikibase.cloud/prop/reference/>
-PREFIX ps: <https://fashionwiki.wikibase.cloud/prop/statement/> 
-PREFIX p: <https://fashionwiki.wikibase.cloud/prop/> 
-PREFIX prov: <http://www.w3.org/ns/prov#>  
-
-SELECT ?fashion_houseLabel (COUNT(DISTINCT ?collection) AS ?collection_count) WHERE {
-  ?fashion_house p:P31 ?statement.
-  ?statement ps:P31 ?collection.
-  ?statement pq:P34 ?description .
-  FILTER (regex(?description, "sustainability", "i"))
-     # Retrieve references for the statement
-   OPTIONAL {
-     ?statement prov:wasDerivedFrom ?reference.
-     ?reference pr:P24 ?reference_URL.
-   }
-  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
-} GROUP BY ?fashion_houseLabel
-ORDER BY DESC(?collection_count) """
-
-
-questions_queries_dict = [ { "question": education_most_popular_question, "query": education_most_popular_query},
-                          { "question": how_many_designers_per_fashion_house_question, "query": how_many_designers_per_fashion_house_query},
-                           {"question": how_many_directors_per_fashion_house_question, "query": how_many_directors_per_fashion_house_query}, 
-                           {"question": designers_multiple_houses_question, "query":designers_multiple_houses_query }, 
-                           {"question": award_question, "query": award_question},
-                           {"question": fashion_houses_with_collections_question, "query": fashion_houses_with_collections_query},
-                           {"question": popular_year_inception_question, "query": popular_year_inception_query},
-                           {"question": longest_serving_director_question, "query": longest_serving_director_query},
-                           {"question": houses_most_collections_question, "query": houses_most_collections_query},
-                           {"question": collections_sustainability_theme_question, "query": collections_sustainability_theme_query},
-                           ]
-
-questions_queries_dict_entity = [{"question": fashion_house_directors_question.substitute({ "x": f"{"Chanel"}"}), "query": fashion_house_directors_query},
-                          { "question": designer_fashion_house_directors_question.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_fashion_house_directors_query},
-                          { "question": country_designer_question.substitute({ "x": f"{"Italy"}"}), "query": country_designer_query},
-                          { "question": designer_order_fashion_collection_question.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_order_fashion_collection_query},
-                          { "question": designer_fashion_director_question2.substitute({ "x": f"{"Alexander McQueen"}"}), "query": designer_fashion_director_query2},
-                          { "question": year_designers_birth_question.substitute({ "x": f"{"1970"}"}), "query": year_designers_birth_query}]
-
-
-# Designers
-designers_born_in_year_question = "Which designers were born in year $x?"
-creative_directors_of_house_question = "Which designers have been creative directors of $x?"
-designers_multiple_houses_question = "Which designers have worked for more than one fashion house?"
-educated_at_school_question = "Which designers were educated at $x fashion school?"
-won_award_question = "Which designers have won the $x award?"
-launched_own_label_question = "Which designers launched their own fashion label?"
-worked_at_x_between_years_question = "Which designers worked at $x between year $y and $z?"
-designers_multiple_collaborations_question = "Which designers have collaborated with multiple fashion houses?"
-designers_lived_in_city_question = "Which designers have lived in $x city?"
-designers_active_decade_question = "Which designers were active during the $x decade?"
-
-# Fashion Houses
-designers_worked_for_house_question = "Which designers have worked for fashion house $x?"
-creative_directors_per_house_question = "How many designers have worked for each fashion house as creative directors?"
-houses_founded_in_year_question = "Which fashion houses were founded in year $x?"
-houses_most_director_changes_question = "Which fashion houses have undergone the most creative director changes?"
-longest_serving_director_question = "Which fashion houses have had the longest-serving creative director?"
-houses_collaborations_question = "Which fashion houses have collaborated with other fashion houses or brands?"
-houses_haute_couture_question = "Which fashion houses have produced haute couture collections?"
-houses_active_decade_question = "Which fashion houses were active during the $x decade?"
-houses_most_collections_question = "Which fashion houses have the most collections recorded in the database?"
-
-# Fashion Collections
-designer_worked_on_collections_question = "Which fashion collections did $x work on?"
-collections_inspired_by_theme_question = "Which fashion house produced a collection inspired by $x theme?"
-collections_high_critical_acclaim_question = "Which collections received the highest critical acclaim?"
-collections_historical_references_question = "Which collections feature references to historical fashion styles?"
-collections_sustainability_theme_question = "Which collections include sustainability as a key theme?"
-collections_per_house_per_year_question = "How many collections did each fashion house produce in year $x?"
-collections_avant_garde_question = "Which collections have been described as avant-garde?"
-collections_stylistic_similarity_question = "Which collections share stylistic similarities with $x?"
-collections_evolution_over_time_question = "How has the style of $x fashion house evolved over the years?"
-collections_shown_at_fashion_week_question = "Which collections were showcased at $x fashion week?"
-
-# Designers & Fashion Houses
-studied_then_director_question = "Which designers who studied at $x fashion school later became creative directors at $y fashion house?"
-worked_at_both_houses_question = "Which designers have worked at both $x and $y fashion houses?"
-launched_label_after_working_question = "Which designers who launched their own fashion labels had previously worked at $x fashion house?"
-won_award_then_worked_question = "Which designers who won the $x award also worked for $y fashion house?"
-lived_in_city_and_director_question = "Which designers who lived in $x city have worked as creative directors for $y fashion house?"
-worked_for_three_houses_question = "Which designers have worked for at least three different fashion houses during their career?"
-designed_for_two_brands_same_year_question = "Which designers who were creative directors at $x fashion house also designed collections for another brand in the same year?"
-creative_director_and_label_founder_question = "Which designers were both fashion house creative directors and independent label founders at the same time?"
-collaborated_with_two_houses_question = "Which designers have collaborated with both $x and $y fashion houses?"
-worked_at_x_before_y_question = "Which designers worked at $x fashion house before moving to $y?"
-
-# Designers & Collections
-designed_for_multiple_houses_same_season_question = "Which designers have designed collections for multiple fashion houses in the same season?"
-couture_and_ready_to_wear_question = "Which designers who specialized in haute couture have also designed ready-to-wear collections?"
-similar_theme_collections_question = "Which designers who worked on $x collection had previously worked on a similar-themed collection for another brand?"
-award_winner_inspired_by_history_question = "Which designers who have won an award have produced a collection inspired by $x historical period?"
-alma_mater_referenced_in_collection_question = "Which designers who studied at $x school designed a collection that referenced their alma mater?"
-
-# Fashion Houses & Collections
-same_historical_period_collections_question = "Which fashion houses have produced collections inspired by the same historical period?"
-collaborations_with_artists_question = "Which fashion houses have produced collections featuring collaborations with artists from $x field (e.g., photography, sculpture, film)?"
-change_director_different_collection_question = "Which fashion houses have changed creative directors and immediately released a significantly different collection?"
-avant_garde_and_minimalist_question = "Which fashion houses have produced both avant-garde and minimalist collections?"
-inspired_by_other_brands_question = "Which fashion houses have produced a collection inspired by a previous designer’s work from another brand?"
-
-# Multi-Category (Designers, Houses, Collections)
-designer_worked_house_then_influenced_question = "Which designers who worked for $x fashion house also designed a collection influenced by $y movement?"
-designer_lived_in_city_referenced_collection_question = "Which designers who lived in $x city during year $y produced a collection with references to that place?"
-multiple_designers_same_collection_question = "Which fashion houses have had at least three designers contribute to the same collection?"
-worked_elsewhere_during_collection_question = "Which collections were created by a designer who was also working at another fashion house at the time?"
-education_influenced_collection_question = "Which fashion collections were influenced by a designer’s education at $x fashion school?"
-
-# Designers & Fashion Houses
-studied_then_director_question = "Which designers who studied at $x fashion school later became creative directors at $y fashion house?"
-worked_at_both_houses_question = "Which designers have worked at both $x and $y fashion houses?"
-launched_label_after_working_question = "Which designers who launched their own fashion labels had previously worked at $x fashion house?"
-won_award_then_worked_question = "Which designers who won the $x award also worked for $y fashion house?"
-lived_in_city_and_director_question = "Which designers who lived in $x city have worked as creative directors for $y fashion house?"
-worked_for_three_houses_question = "Which designers have worked for at least three different fashion houses during their career?"
-designed_for_two_brands_same_year_question = "Which designers who were creative directors at $x fashion house also designed collections for another brand in the same year?"
-creative_director_and_label_founder_question = "Which designers were both fashion house creative directors and independent label founders at the same time?"
-collaborated_with_two_houses_question = "Which designers have collaborated with both $x and $y fashion houses?"
-worked_at_x_before_y_question = "Which designers worked at $x fashion house before moving to $y?"
-
-# Designers & Collections
-designed_for_multiple_houses_same_season_question = "Which designers have designed collections for multiple fashion houses in the same season?"
-couture_and_ready_to_wear_question = "Which designers who specialized in haute couture have also designed ready-to-wear collections?"
-similar_theme_collections_question = "Which designers who worked on $x collection had previously worked on a similar-themed collection for another brand?"
-award_winner_inspired_by_history_question = "Which designers who have won an award have produced a collection inspired by $x historical period?"
-alma_mater_referenced_in_collection_question = "Which designers who studied at $x school designed a collection that referenced their alma mater?"
-
-# Fashion Houses & Collections
-same_historical_period_collections_question = "Which fashion houses have produced collections inspired by the same historical period?"
-collaborations_with_artists_question = "Which fashion houses have produced collections featuring collaborations with artists from $x field (e.g., photography, sculpture, film)?"
-change_director_different_collection_question = "Which fashion houses have changed creative directors and immediately released a significantly different collection?"
-avant_garde_and_minimalist_question = "Which fashion houses have produced both avant-garde and minimalist collections?"
-inspired_by_other_brands_question = "Which fashion houses have produced a collection inspired by a previous designer’s work from another brand?"
-
-# Multi-Category (Designers, Houses, Collections)
-designer_worked_house_then_influenced_question = "Which designers who worked for $x fashion house also designed a collection influenced by $y movement?"
-designer_lived_in_city_referenced_collection_question = "Which designers who lived in $x city during year $y produced a collection with references to that place?"
-multiple_designers_same_collection_question = "Which fashion houses have had at least three designers contribute to the same collection?"
-worked_elsewhere_during_collection_question = "Which collections were created by a designer who was also working at another fashion house at the time?"
-education_influenced_collection_question = "Which fashion collections were influenced by a designer’s education at $x fashion school?"
-
-
-

src1/visual_qa.py

@@ -1,128 +0,0 @@
-
-import json
-import pandas as pd
-from transformers import CLIPProcessor, CLIPModel
-import torch
-import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-import pandas as pd
-import chromadb
-import os
-from transformers import CLIPProcessor, CLIPModel
-import torch
-from datetime import datetime
-from datasets import load_dataset
-
-
-def initialize_collection(collection_name="clip_image_embeddings"):
-    # Initialize ChromaDB client (PersistentClient stores embeddings between runs)
-    client = chromadb.PersistentClient(path="./chroma_db")  # Change path if needed
-
-    # Get a list of existing collection names
-    existing_collections = [col for col in client.list_collections()]  # v0.6.0 change
-
-    if collection_name in existing_collections:
-        collection = client.get_collection(name=collection_name)
-        print(f"Using existing collection: {collection_name}")
-    else:
-        collection = client.create_collection(name=collection_name)
-
-    return collection
-
-
-
-
-def main_create_image_collection(df_emb, collection_name="clip_image_embeddings"):
-    documents = []
-    embeddings_all = df_emb["fashion_clip_image"].tolist()
-
-    for i in range(len(df_emb)):
-        if i >= len(df_emb["image_url"]):
-            print(f"Index {i} out of range for image_url column")
-            continue
-
-        image_url = df_emb["image_url"].iloc[i]
-        if image_url is not None:
-            documents.append(image_url)
-        else:
-            documents.append(None)
-
-    collection = initialize_collection(collection_name)
-
-    for i, d in enumerate(documents):
-        embeddings = embeddings_all[i]
-        embedding_id = str(i)  # Convert index to string
-
-        if embeddings is None:
-            continue  # Skip if no embedding
-
-        # Check if ID already exists
-        existing_entry = collection.get(ids=[embedding_id])
-
-        if existing_entry and existing_entry["ids"]:  # If the ID is found, skip adding
-            continue
-
-        collection.add(
-            ids=[embedding_id],
-            embeddings=embeddings,
-            documents=[d]
-        )
-
-    return collection
-
-
-
-model_name = "patrickjohncyh/fashion-clip"
-device = "mps"
-model = CLIPModel.from_pretrained(model_name).to(device)
-processor = CLIPProcessor.from_pretrained(model_name)
-
-def main_text_retrieve_images(text, result_query=None, n_retrieved=5):
-    #df_emb = pd.read_json("data/fashion_show_data_all_embeddings.json", lines=True)
-
-    dataset = load_dataset("traopia/fashion_show_data_all_embeddings.json")
-    # This returns a DatasetDict with splits as keys (usually 'train' by default).
-    # To get the whole dataset, you can access the first split like this:
-    split_name = list(dataset.keys())[0]
-    full_dataset = dataset[split_name]
-
-    # Convert to pandas DataFrame
-    df_emb = full_dataset.to_pandas()
-    df_emb = df_emb.drop_duplicates(subset='image_urls')
-    df_emb['fashion_clip_image'] = df_emb['fashion_clip_image'].apply(lambda x: x[0] if type(x) == list else None)
-    df_emb['image_url'] = df_emb['image_urls'].apply(lambda x: x[0] if x else None)
-    df_emb = df_emb.drop_duplicates(subset='image_url')
-
-    #print("DataFrame head:", df_emb.head())  # Debugging statement
-
-    if result_query:
-        df_small = pd.DataFrame(result_query, columns=["image_url"])
-        df_filtered = df_emb.merge(df_small[['image_url']], on='image_url', how='inner')
-        df_emb = df_filtered
-        # Generate a collection name based on the current timestamp
-        collection_name = f"collection_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
-        collection = main_create_image_collection(df_emb, collection_name=collection_name)
-    else:
-        collection = main_create_image_collection(df_emb)
-
-    inputs = processor(text=[text], return_tensors="pt", padding=True, truncation=True, max_length=77).to(device)
-
-    with torch.no_grad():
-        text_features = model.get_text_features(**inputs).cpu().numpy()  # Should work without crashing
-
-    results = collection.query(
-        query_embeddings=text_features[0],
-        n_results=n_retrieved
-    )
-    #print(results)
-    result_doc = pd.DataFrame(results['documents'][0], columns=["image_url"])
-    df_result = df_emb.merge(result_doc[['image_url']], on='image_url', how='inner')
-    # Remove columns fashion_clip_image, image_urls, and description
-    df_result = df_result.drop(columns=['fashion_clip_image', 'description', 'editor', 'publish_date', 'image_urls'])
-    return df_result.to_dict(orient='records')
-
-if __name__ == "__main__":
-    text = "dress"
-    result = main_text_retrieve_images(text)
-    print(result)