Update app.py

app.py

@@ -1,393 +1,447 @@
-import gradio as gr
-from pathlib import Path
-from tempfile import NamedTemporaryFile
-from sentence_transformers import CrossEncoder
-import numpy as np
-from time import perf_counter
-import pandas as pd
-from pydantic import BaseModel, Field
-from phi.agent import Agent
-from phi.model.groq import Groq
-import os
-import logging
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# API Key setup
-api_key = os.getenv("GROQ_API_KEY")
-if not api_key:
-    gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.")
-    logger.error("GROQ_API_KEY not found.")
-else:
-    os.environ["GROQ_API_KEY"] = api_key
-
-# Pydantic Model for Quiz Structure
-class QuizItem(BaseModel):
-    question: str = Field(..., description="The quiz question")
-    choices: list[str] = Field(..., description="List of 4 multiple-choice options")
-    correct_answer: str = Field(..., description="The correct choice (e.g., 'C1')")
-
-class QuizOutput(BaseModel):
-    items: list[QuizItem] = Field(..., description="List of 10 quiz items")
-
-# Initialize Agents
-groq_agent = Agent(model=Groq(model="llama3-70b-8192", api_key=api_key), markdown=True)
-
-quiz_generator = Agent(
-    name="Quiz Generator",
-    role="Generates structured quiz questions and answers",
-    instructions=[
-        "Create 10 questions with 4 choices each based on the provided topic and documents.",
-        "Use the specified difficulty level (easy, average, hard) to adjust question complexity.",
-        "Ensure questions are derived only from the provided documents.",
-        "Return the output in a structured format using the QuizOutput Pydantic model.",
-        "Each question should have a unique correct answer from the choices (labeled C1, C2, C3, C4)."
-    ],
-    model=Groq(id="llama3-70b-8192", api_key=api_key),
-    response_model=QuizOutput,
-    markdown=True
-)
-
-VECTOR_COLUMN_NAME = "vector"
-TEXT_COLUMN_NAME = "text"
-proj_dir = Path.cwd()
-
-# Calling functions from backend (assuming they exist)
-from backend.semantic_search import table, retriever
-
-def generate_quiz_data(question_difficulty, topic, documents_str):
-    prompt = f"""Generate a quiz with {question_difficulty} difficulty on topic '{topic}' using only the following documents:\n{documents_str}"""
-    try:
-        response = quiz_generator.run(prompt)
-        return response.content
-    except Exception as e:
-        logger.error(f"Failed to generate quiz: {e}")
-        return None
-
-def json_to_excel(quiz_data):
-    data = []
-    gr.Warning('Generating Shareable file link..', duration=30)
-    for i, item in enumerate(quiz_data.items, 1):
-        data.append([
-            item.question,
-            "Multiple Choice",
-            item.choices[0],
-            item.choices[1],
-            item.choices[2],
-            item.choices[3],
-            '',  # Option 5 (empty)
-            item.correct_answer.replace('C', ''),
-            30,
-            ''
-        ])
-    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=True, suffix=".xlsx")
-    df.to_excel(temp_file.name, index=False)
-    return temp_file.name
-
-colorful_theme = gr.themes.Default(primary_hue="cyan", secondary_hue="yellow", neutral_hue="purple")
-
-with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
-    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 from any topic from 9th Science and evaluate themselves! ⚠️</i>
-            </center>
-            """)
-
-    topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from 9TH Science CBSE")
-    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'], value='(ACCURATE) BGE reranker', label="Embeddings")  # Removed ColBERT option
-
-    generate_quiz_btn = gr.Button("Generate Quiz!🚀")
-    quiz_msg = gr.Textbox(label="Status", interactive=False)
-    question_display = gr.HTML(visible=False)
-    download_excel = gr.File(label="Download Excel")
-
-    @generate_quiz_btn.click(inputs=[difficulty_radio, topic, model_radio], outputs=[quiz_msg, question_display, 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)
-
-        document_start = perf_counter()
-        query_vec = retriever.encode(topic)
-        documents = [doc[TEXT_COLUMN_NAME] for doc in table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()]
-        if cross_encoder == '(ACCURATE) BGE reranker':
-            cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
-            query_doc_pair = [[topic, doc] for doc in documents]
-            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]]
-
-        documents_str = '\n'.join(documents)
-        quiz_data = generate_quiz_data(question_difficulty, topic, documents_str)
-        if not quiz_data or not quiz_data.items:
-            return ["Error: Failed to generate quiz.", gr.HTML(visible=False), None]
-
-        excel_file = json_to_excel(quiz_data)
-        html_content = "<div>" + "".join(f"<h3>{i}. {item.question}</h3><p>{'<br>'.join(item.choices)}</p>" for i, item in enumerate(quiz_data.items[:10], 1)) + "</div>"
-        return ["Quiz Generated!", gr.HTML(value=html_content, visible=True), excel_file]
-
-    check_button = gr.Button("Check Score")
-    score_textbox = gr.Markdown()
-
-    @check_button.click(inputs=question_display, outputs=score_textbox)
-    def compare_answers(html_content):
-        if not quiz_data or not quiz_data.items:
-            return "Please generate a quiz first."
-        # Placeholder for user answers (adjust based on actual UI implementation)
-        user_answers = []  # Implement parsing logic if using radio inputs
-        correct_answers = [item.correct_answer for item in quiz_data.items[:10]]
-        score = sum(1 for u, c in zip(user_answers, correct_answers) if u == c)
-        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
-
-if __name__ == "__main__":
-    QUIZBOT.queue().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 from any topic from 10 science and evaluate themselves! ⚠️</i>
-#             </center>
-#             """)
-        
-
-
-    
-#     topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any CHAPTER NAME")
-
-#     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, 11):
-#             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)
-
+import gradio as gr
+from sentence_transformers import CrossEncoder
+import numpy as np
+from time import perf_counter
+from backend.semantic_search import table, retriever
+import os
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# API Key setup (optional, depending on retriever needs)
+api_key = os.getenv("GROQ_API_KEY")
+if not api_key:
+    gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets' if required.")
+    logger.error("GROQ_API_KEY not found.")
+else:
+    os.environ["GROQ_API_KEY"] = api_key
+
+VECTOR_COLUMN_NAME = "vector"
+TEXT_COLUMN_NAME = "text"
+
+def retrieve_documents(topic):
+    gr.Warning('Retrieving documents may take a moment. Please wait.', duration=30)
+    top_k_rank = 10
+    documents = []
+
+    document_start = perf_counter()
+    query_vec = retriever.encode(topic)
+    documents = [doc[TEXT_COLUMN_NAME] for doc in table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()]
+    
+    # Apply BGE reranker
+    cross_encoder = CrossEncoder('BAAI/bge-reranker-base')
+    query_doc_pair = [[topic, doc] for doc in documents]
+    cross_scores = cross_encoder.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]]
+
+    elapsed_time = perf_counter() - document_start
+    return f"Retrieved {len(documents)} documents in {elapsed_time:.2f} seconds:\n\n" + "\n".join(documents)
+
+with gr.Blocks(title="Document Retrieval Test") as APP:
+    with gr.Row():
+        gr.Markdown("# Document Retrieval Test")
+    
+    topic = gr.Textbox(label="Enter Topic", placeholder="Enter a topic to search")
+    output = gr.Textbox(label="Retrieved Documents", interactive=False)
+
+    submit_btn = gr.Button("Retrieve Documents")
+    submit_btn.click(fn=retrieve_documents, inputs=[topic], outputs=[output])
+
+if __name__ == "__main__":
+    APP.queue().launch(server_name="0.0.0.0", server_port=7860)
+# import gradio as gr
+# from pathlib import Path
+# from tempfile import NamedTemporaryFile
+# from sentence_transformers import CrossEncoder
+# import numpy as np
+# from time import perf_counter
+# import pandas as pd
+# from pydantic import BaseModel, Field
+# from phi.agent import Agent
+# from phi.model.groq import Groq
+# import os
+# import logging
+
+# # Set up logging
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+
+# # API Key setup
+# api_key = os.getenv("GROQ_API_KEY")
+# if not api_key:
+#     gr.Warning("GROQ_API_KEY not found. Set it in 'Repository secrets'.")
+#     logger.error("GROQ_API_KEY not found.")
+# else:
+#     os.environ["GROQ_API_KEY"] = api_key
+
+# # Pydantic Model for Quiz Structure
+# class QuizItem(BaseModel):
+#     question: str = Field(..., description="The quiz question")
+#     choices: list[str] = Field(..., description="List of 4 multiple-choice options")
+#     correct_answer: str = Field(..., description="The correct choice (e.g., 'C1')")
+
+# class QuizOutput(BaseModel):
+#     items: list[QuizItem] = Field(..., description="List of 10 quiz items")
+
+# # Initialize Agents
+# groq_agent = Agent(model=Groq(model="llama3-70b-8192", api_key=api_key), markdown=True)
+
+# quiz_generator = Agent(
+#     name="Quiz Generator",
+#     role="Generates structured quiz questions and answers",
+#     instructions=[
+#         "Create 10 questions with 4 choices each based on the provided topic and documents.",
+#         "Use the specified difficulty level (easy, average, hard) to adjust question complexity.",
+#         "Ensure questions are derived only from the provided documents.",
+#         "Return the output in a structured format using the QuizOutput Pydantic model.",
+#         "Each question should have a unique correct answer from the choices (labeled C1, C2, C3, C4)."
+#     ],
+#     model=Groq(id="llama3-70b-8192", api_key=api_key),
+#     response_model=QuizOutput,
+#     markdown=True
+# )
+
+# VECTOR_COLUMN_NAME = "vector"
+# TEXT_COLUMN_NAME = "text"
+# proj_dir = Path.cwd()
+
+# # Calling functions from backend (assuming they exist)
+# from backend.semantic_search import table, retriever
+
+# def generate_quiz_data(question_difficulty, topic, documents_str):
+#     prompt = f"""Generate a quiz with {question_difficulty} difficulty on topic '{topic}' using only the following documents:\n{documents_str}"""
+#     try:
+#         response = quiz_generator.run(prompt)
+#         return response.content
+#     except Exception as e:
+#         logger.error(f"Failed to generate quiz: {e}")
+#         return None
+
+# def json_to_excel(quiz_data):
+#     data = []
+#     gr.Warning('Generating Shareable file link..', duration=30)
+#     for i, item in enumerate(quiz_data.items, 1):
+#         data.append([
+#             item.question,
+#             "Multiple Choice",
+#             item.choices[0],
+#             item.choices[1],
+#             item.choices[2],
+#             item.choices[3],
+#             '',  # Option 5 (empty)
+#             item.correct_answer.replace('C', ''),
+#             30,
+#             ''
+#         ])
+#     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=True, suffix=".xlsx")
+#     df.to_excel(temp_file.name, index=False)
+#     return temp_file.name
+
+# colorful_theme = gr.themes.Default(primary_hue="cyan", secondary_hue="yellow", neutral_hue="purple")
+
+# with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT:
+#     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 from any topic from 9th Science and evaluate themselves! ⚠️</i>
+#             </center>
+#             """)
+
+#     topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from 9TH Science CBSE")
+#     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'], value='(ACCURATE) BGE reranker', label="Embeddings")  # Removed ColBERT option
+
+#     generate_quiz_btn = gr.Button("Generate Quiz!🚀")
+#     quiz_msg = gr.Textbox(label="Status", interactive=False)
+#     question_display = gr.HTML(visible=False)
+#     download_excel = gr.File(label="Download Excel")
+
+#     @generate_quiz_btn.click(inputs=[difficulty_radio, topic, model_radio], outputs=[quiz_msg, question_display, 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)
+
+#         document_start = perf_counter()
+#         query_vec = retriever.encode(topic)
+#         documents = [doc[TEXT_COLUMN_NAME] for doc in table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()]
+#         if cross_encoder == '(ACCURATE) BGE reranker':
+#             cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
+#             query_doc_pair = [[topic, doc] for doc in documents]
+#             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]]
+
+#         documents_str = '\n'.join(documents)
+#         quiz_data = generate_quiz_data(question_difficulty, topic, documents_str)
+#         if not quiz_data or not quiz_data.items:
+#             return ["Error: Failed to generate quiz.", gr.HTML(visible=False), None]
+
+#         excel_file = json_to_excel(quiz_data)
+#         html_content = "<div>" + "".join(f"<h3>{i}. {item.question}</h3><p>{'<br>'.join(item.choices)}</p>" for i, item in enumerate(quiz_data.items[:10], 1)) + "</div>"
+#         return ["Quiz Generated!", gr.HTML(value=html_content, visible=True), excel_file]
+
+#     check_button = gr.Button("Check Score")
+#     score_textbox = gr.Markdown()
+
+#     @check_button.click(inputs=question_display, outputs=score_textbox)
+#     def compare_answers(html_content):
+#         if not quiz_data or not quiz_data.items:
+#             return "Please generate a quiz first."
+#         # Placeholder for user answers (adjust based on actual UI implementation)
+#         user_answers = []  # Implement parsing logic if using radio inputs
+#         correct_answers = [item.correct_answer for item in quiz_data.items[:10]]
+#         score = sum(1 for u, c in zip(user_answers, correct_answers) if u == c)
+#         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
+
+# if __name__ == "__main__":
+#     QUIZBOT.queue().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 from any topic from 10 science and evaluate themselves! ⚠️</i>
+# #             </center>
+# #             """)
+        
+
+
+    
+# #     topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any CHAPTER NAME")
+
+# #     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, 11):
+# #             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)
+