src1/generate_queries_alternative.py

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?")