Update app.py

app.py

@@ -1,230 +1,230 @@
-# Importing libraries
-import pandas as pd
-import json
-import gradio as gr
-from pathlib import Path
-from ragatouille import RAGPretrainedModel
-from gradio_client import Client
-from tempfile import NamedTemporaryFile
-from sentence_transformers import CrossEncoder
-import numpy as np
-from time import perf_counter
-from sentence_transformers import CrossEncoder
-
-#calling functions from other files - to call the knowledge database tables (lancedb for accurate mode) for creating quiz  
-from backend.semantic_search import table, retriever
-
-VECTOR_COLUMN_NAME = "vector"
-TEXT_COLUMN_NAME = "text"
-proj_dir = Path.cwd()
-
-# Set up logging
-import logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Replace Mixtral client with Qwen Client
-client = Client("Qwen/Qwen1.5-110B-Chat-demo")
-
-def system_instructions(question_difficulty, topic, documents_str):
-    return f"""<s> [INST] You are a great teacher and your task is to create 10 questions with 4 choices with {question_difficulty} difficulty about the topic request "{topic}" only from the below given documents, {documents_str}. Then create answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". Example: 'A10':'Q10:C3' [/INST]"""
-
-# Ragatouille database for Colbert ie highly accurate mode
-RAG_db = gr.State()
-quiz_data = None
-
-
-#defining a function to convert json file to excel file 
-def json_to_excel(output_json):
-    # Initialize list for DataFrame
-    data = []
-    gr.Warning('Generating Shareable file link..', duration=30)
-    for i in range(1, 11):  # Assuming there are 10 questions
-        question_key = f"Q{i}"
-        answer_key = f"A{i}"
-
-        question = output_json.get(question_key, '')
-        correct_answer_key = output_json.get(answer_key, '')
-        #correct_answer = correct_answer_key.split(':')[-1] if correct_answer_key else ''
-        correct_answer = correct_answer_key.split(':')[-1].replace('C', '').strip() if correct_answer_key else ''
-
-        # Extract options
-        option_keys = [f"{question_key}:C{i}" for i in range(1, 6)]
-        options = [output_json.get(key, '') for key in option_keys]
-        
-        # Add data row
-        data.append([
-            question,                     # Question Text
-            "Multiple Choice",            # Question Type
-            options[0],                   # Option 1
-            options[1],                   # Option 2
-            options[2] if len(options) > 2 else '',  # Option 3
-            options[3] if len(options) > 3 else '',  # Option 4
-            options[4] if len(options) > 4 else '',  # Option 5
-            correct_answer,               # Correct Answer
-            30,                           # Time in seconds
-            ''                            # Image Link
-        ])
-
-    # Create DataFrame
-    df = pd.DataFrame(data, columns=[
-        "Question Text",
-        "Question Type",
-        "Option 1",
-        "Option 2",
-        "Option 3",
-        "Option 4",
-        "Option 5",
-        "Correct Answer",
-        "Time in seconds",
-        "Image Link"
-    ])
-
-    temp_file = NamedTemporaryFile(delete=False, suffix=".xlsx")
-    df.to_excel(temp_file.name, index=False)
-    return temp_file.name
-# Define a colorful theme
-colorful_theme = gr.themes.Default(
-    primary_hue="cyan",      # Set a bright cyan as primary color
-    secondary_hue="yellow", # Set a bright magenta as secondary color
-    neutral_hue="purple"  # Optionally set a neutral color
-        
-)
-
-#gradio app creation for a user interface 
-with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
-    
-    
-    # Create a single row for the HTML and Image
-    with gr.Row():
-        with gr.Column(scale=2):
-            gr.Image(value='logo.png', height=200, width=200)
-        with gr.Column(scale=6):
-            gr.HTML("""
-            <center>
-                <h1><span style="color: purple;">GOVERNMENT HIGH SCHOOL,SUTHUKENY</span>STUDENTS QUIZBOT</h1>
-                <h2>Generative AI-powered Capacity building for STUDENTS</h2>
-                <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️</i>
-            </center>
-            """)
-        
-
-
-    
-    topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual")
-
-    with gr.Row():
-        difficulty_radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
-        model_radio = gr.Radio(choices=[ '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], 
-                               value='(ACCURATE) BGE reranker', label="Embeddings", 
-                               info="First query to ColBERT may take a little time")
-
-    generate_quiz_btn = gr.Button("Generate Quiz!🚀")
-    quiz_msg = gr.Textbox()
-
-    question_radios = [gr.Radio(visible=False) for _ in range(10)]
-
-    @generate_quiz_btn.click(inputs=[difficulty_radio, topic, model_radio], outputs=[quiz_msg] + question_radios + [gr.File(label="Download Excel")])
-    def generate_quiz(question_difficulty, topic, cross_encoder):
-        top_k_rank = 10
-        documents = []
-        gr.Warning('Generating Quiz may take 1-2 minutes. Please wait.', duration=60)
-
-        if cross_encoder == '(HIGH ACCURATE) ColBERT':
-            gr.Warning('Retrieving using ColBERT.. First-time query will take 2 minute for model to load.. please wait',duration=100)
-            RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-            RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-            documents_full = RAG_db.value.search(topic, k=top_k_rank)
-            documents = [item['content'] for item in documents_full]
-        
-        else:
-            document_start = perf_counter()
-            query_vec = retriever.encode(topic)
-            doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
-
-            documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()
-            documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
-
-            query_doc_pair = [[topic, doc] for doc in documents]
-
-            # if cross_encoder == '(FAST) MiniLM-L6v2':
-            #     cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-            if cross_encoder == '(ACCURATE) BGE reranker':
-                cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
-            
-            cross_scores = cross_encoder1.predict(query_doc_pair)
-            sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
-            documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
-
-        #creating a text prompt to Qwen model combining the documents and system instruction 
-        formatted_prompt = system_instructions(question_difficulty, topic, '\n'.join(documents))
-        print('                      Formatted Prompt : ' ,formatted_prompt)
-        try:
-            response = client.predict(query=formatted_prompt, history=[], system="You are a helpful assistant.", api_name="/model_chat")
-            response1 = response[1][0][1]
-            
-            # Extract JSON
-            start_index = response1.find('{')
-            end_index = response1.rfind('}')
-            cleaned_response = response1[start_index:end_index + 1] if start_index != -1 and end_index != -1 else ''
-            print('Cleaned Response :',cleaned_response)
-            output_json = json.loads(cleaned_response)
-            # Assign the extracted JSON to quiz_data for use in the comparison function
-            global quiz_data
-            quiz_data = output_json
-            # Generate the Excel file
-            excel_file = json_to_excel(output_json)
-            
-
-            #Create a Quiz display in app
-            question_radio_list = []
-            for question_num in range(1, 11):
-                question_key = f"Q{question_num}"
-                answer_key = f"A{question_num}"
-
-                question = output_json.get(question_key)
-                answer = output_json.get(output_json.get(answer_key))
-
-                if not question or not answer:
-                    continue
-
-                choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
-                choice_list = [output_json.get(choice_key, "Choice not found") for choice_key in choice_keys]
-
-                radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True)
-                question_radio_list.append(radio)
-
-            return ['Quiz Generated!'] + question_radio_list + [excel_file]
-
-        except json.JSONDecodeError as e:
-            print(f"Failed to decode JSON: {e}")
-
-    check_button = gr.Button("Check Score")
-    score_textbox = gr.Markdown()
-
-    @check_button.click(inputs=question_radios, outputs=score_textbox)
-    def compare_answers(*user_answers):
-        user_answer_list = list(user_answers)
-        answers_list = []
-
-        for question_num in range(1, 20):
-            answer_key = f"A{question_num}"
-            answer = quiz_data.get(quiz_data.get(answer_key))
-            if not answer:
-                break
-            answers_list.append(answer)
-
-        score = sum(1 for item in user_answer_list if item in answers_list)
-
-        if score > 7:
-            message = f"### Excellent! You got {score} out of 10!"
-        elif score > 5:
-            message = f"### Good! You got {score} out of 10!"
-        else:
-            message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!"
-
-        return message
-
-QUIZBOT.queue()
-QUIZBOT.launch(debug=True)
-
+# Importing libraries
+import pandas as pd
+import json
+import gradio as gr
+from pathlib import Path
+from ragatouille import RAGPretrainedModel
+from gradio_client import Client
+from tempfile import NamedTemporaryFile
+from sentence_transformers import CrossEncoder
+import numpy as np
+from time import perf_counter
+from sentence_transformers import CrossEncoder
+
+#calling functions from other files - to call the knowledge database tables (lancedb for accurate mode) for creating quiz  
+from backend.semantic_search import table, retriever
+
+VECTOR_COLUMN_NAME = "vector"
+TEXT_COLUMN_NAME = "text"
+proj_dir = Path.cwd()
+
+# Set up logging
+import logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Replace Mixtral client with Qwen Client
+client = Client("Qwen/Qwen1.5-110B-Chat-demo")
+
+def system_instructions(question_difficulty, topic, documents_str):
+    return f"""<s> [INST] You are a great teacher and your task is to create 10 questions with 4 choices with {question_difficulty} difficulty about the topic request "{topic}" only from the below given documents, {documents_str}. Then create answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". Example: 'A10':'Q10:C3' [/INST]"""
+
+# Ragatouille database for Colbert ie highly accurate mode
+RAG_db = gr.State()
+quiz_data = None
+
+
+#defining a function to convert json file to excel file 
+def json_to_excel(output_json):
+    # Initialize list for DataFrame
+    data = []
+    gr.Warning('Generating Shareable file link..', duration=30)
+    for i in range(1, 11):  # Assuming there are 10 questions
+        question_key = f"Q{i}"
+        answer_key = f"A{i}"
+
+        question = output_json.get(question_key, '')
+        correct_answer_key = output_json.get(answer_key, '')
+        #correct_answer = correct_answer_key.split(':')[-1] if correct_answer_key else ''
+        correct_answer = correct_answer_key.split(':')[-1].replace('C', '').strip() if correct_answer_key else ''
+
+        # Extract options
+        option_keys = [f"{question_key}:C{i}" for i in range(1, 6)]
+        options = [output_json.get(key, '') for key in option_keys]
+        
+        # Add data row
+        data.append([
+            question,                     # Question Text
+            "Multiple Choice",            # Question Type
+            options[0],                   # Option 1
+            options[1],                   # Option 2
+            options[2] if len(options) > 2 else '',  # Option 3
+            options[3] if len(options) > 3 else '',  # Option 4
+            options[4] if len(options) > 4 else '',  # Option 5
+            correct_answer,               # Correct Answer
+            30,                           # Time in seconds
+            ''                            # Image Link
+        ])
+
+    # Create DataFrame
+    df = pd.DataFrame(data, columns=[
+        "Question Text",
+        "Question Type",
+        "Option 1",
+        "Option 2",
+        "Option 3",
+        "Option 4",
+        "Option 5",
+        "Correct Answer",
+        "Time in seconds",
+        "Image Link"
+    ])
+
+    temp_file = NamedTemporaryFile(delete=False, suffix=".xlsx")
+    df.to_excel(temp_file.name, index=False)
+    return temp_file.name
+# Define a colorful theme
+colorful_theme = gr.themes.Default(
+    primary_hue="cyan",      # Set a bright cyan as primary color
+    secondary_hue="yellow", # Set a bright magenta as secondary color
+    neutral_hue="purple"  # Optionally set a neutral color
+        
+)
+
+#gradio app creation for a user interface 
+with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
+    
+    
+    # Create a single row for the HTML and Image
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Image(value='logo.png', height=200, width=200)
+        with gr.Column(scale=6):
+            gr.HTML("""
+            <center>
+                <h1><span style="color: purple;">GOVERNMENT HIGH SCHOOL,SUTHUKENY</span> STUDENTS QUIZBOT</h1>
+                <h2>Generative AI-powered Capacity building for STUDENTS</h2>
+                <i>⚠️STUDENTS CAN CREATE QUIZ AND EVALUATE BY THEMSELVES ! ⚠️</i>
+            </center>
+            """)
+        
+
+
+    
+    topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual")
+
+    with gr.Row():
+        difficulty_radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
+        model_radio = gr.Radio(choices=[ '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], 
+                               value='(ACCURATE) BGE reranker', label="Embeddings", 
+                               info="First query to ColBERT may take a little time")
+
+    generate_quiz_btn = gr.Button("Generate Quiz!🚀")
+    quiz_msg = gr.Textbox()
+
+    question_radios = [gr.Radio(visible=False) for _ in range(10)]
+
+    @generate_quiz_btn.click(inputs=[difficulty_radio, topic, model_radio], outputs=[quiz_msg] + question_radios + [gr.File(label="Download Excel")])
+    def generate_quiz(question_difficulty, topic, cross_encoder):
+        top_k_rank = 10
+        documents = []
+        gr.Warning('Generating Quiz may take 1-2 minutes. Please wait.', duration=60)
+
+        if cross_encoder == '(HIGH ACCURATE) ColBERT':
+            gr.Warning('Retrieving using ColBERT.. First-time query will take 2 minute for model to load.. please wait',duration=100)
+            RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
+            RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
+            documents_full = RAG_db.value.search(topic, k=top_k_rank)
+            documents = [item['content'] for item in documents_full]
+        
+        else:
+            document_start = perf_counter()
+            query_vec = retriever.encode(topic)
+            doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
+
+            documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()
+            documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
+
+            query_doc_pair = [[topic, doc] for doc in documents]
+
+            # if cross_encoder == '(FAST) MiniLM-L6v2':
+            #     cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+            if cross_encoder == '(ACCURATE) BGE reranker':
+                cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
+            
+            cross_scores = cross_encoder1.predict(query_doc_pair)
+            sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
+            documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
+
+        #creating a text prompt to Qwen model combining the documents and system instruction 
+        formatted_prompt = system_instructions(question_difficulty, topic, '\n'.join(documents))
+        print('                      Formatted Prompt : ' ,formatted_prompt)
+        try:
+            response = client.predict(query=formatted_prompt, history=[], system="You are a helpful assistant.", api_name="/model_chat")
+            response1 = response[1][0][1]
+            
+            # Extract JSON
+            start_index = response1.find('{')
+            end_index = response1.rfind('}')
+            cleaned_response = response1[start_index:end_index + 1] if start_index != -1 and end_index != -1 else ''
+            print('Cleaned Response :',cleaned_response)
+            output_json = json.loads(cleaned_response)
+            # Assign the extracted JSON to quiz_data for use in the comparison function
+            global quiz_data
+            quiz_data = output_json
+            # Generate the Excel file
+            excel_file = json_to_excel(output_json)
+            
+
+            #Create a Quiz display in app
+            question_radio_list = []
+            for question_num in range(1, 11):
+                question_key = f"Q{question_num}"
+                answer_key = f"A{question_num}"
+
+                question = output_json.get(question_key)
+                answer = output_json.get(output_json.get(answer_key))
+
+                if not question or not answer:
+                    continue
+
+                choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
+                choice_list = [output_json.get(choice_key, "Choice not found") for choice_key in choice_keys]
+
+                radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True)
+                question_radio_list.append(radio)
+
+            return ['Quiz Generated!'] + question_radio_list + [excel_file]
+
+        except json.JSONDecodeError as e:
+            print(f"Failed to decode JSON: {e}")
+
+    check_button = gr.Button("Check Score")
+    score_textbox = gr.Markdown()
+
+    @check_button.click(inputs=question_radios, outputs=score_textbox)
+    def compare_answers(*user_answers):
+        user_answer_list = list(user_answers)
+        answers_list = []
+
+        for question_num in range(1, 20):
+            answer_key = f"A{question_num}"
+            answer = quiz_data.get(quiz_data.get(answer_key))
+            if not answer:
+                break
+            answers_list.append(answer)
+
+        score = sum(1 for item in user_answer_list if item in answers_list)
+
+        if score > 7:
+            message = f"### Excellent! You got {score} out of 10!"
+        elif score > 5:
+            message = f"### Good! You got {score} out of 10!"
+        else:
+            message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!"
+
+        return message
+
+QUIZBOT.queue()
+QUIZBOT.launch(debug=True)
+