Update app.py

app.py

@@ -103,488 +103,488 @@ examples = ['My transhipment cargo is missing','can u explain and tabulate diffe
 #     })
 
 
-def add_text(history, text):
-    history = [] if history is None else history
-    history = history + [(text, None)]
-    return history, gr.Textbox(value="", interactive=False)
+# def add_text(history, text):
+#     history = [] if history is None else history
+#     history = history + [(text, None)]
+#     return history, gr.Textbox(value="", interactive=False)
 
 
-def bot(history, cross_encoder):
-    top_rerank = 25
-    top_k_rank = 20
-    query = history[-1][0]
+# def bot(history, cross_encoder):
+#     top_rerank = 25
+#     top_k_rank = 20
+#     query = history[-1][0]
 
-    if not query:
-         gr.Warning("Please submit a non-empty string as a prompt")
-         raise ValueError("Empty string was submitted")
+#     if not query:
+#          gr.Warning("Please submit a non-empty string as a prompt")
+#          raise ValueError("Empty string was submitted")
 
-    logger.warning('Retrieving documents...')
+#     logger.warning('Retrieving documents...')
     
-    # if COLBERT RAGATATOUILLE PROCEDURE  : 
-    if cross_encoder=='(HIGH ACCURATE) ColBERT':
-        gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
-        RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-        RAG_db=RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-        documents_full=RAG_db.search(query,k=top_k_rank)
+#     # if COLBERT RAGATATOUILLE PROCEDURE  : 
+#     if cross_encoder=='(HIGH ACCURATE) ColBERT':
+#         gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
+#         RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
+#         RAG_db=RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
+#         documents_full=RAG_db.search(query,k=top_k_rank)
         
-        documents=[item['content'] for item in documents_full]
-        # Create Prompt
-        prompt = template.render(documents=documents, query=query)
-        prompt_html = template_html.render(documents=documents, query=query)
+#         documents=[item['content'] for item in documents_full]
+#         # Create Prompt
+#         prompt = template.render(documents=documents, query=query)
+#         prompt_html = template_html.render(documents=documents, query=query)
     
-        generate_fn = generate_hf
+#         generate_fn = generate_hf
     
-        history[-1][1] = ""
-        for character in generate_fn(prompt, history[:-1]):
-            history[-1][1] = character
-            yield history, prompt_html
-        print('Final history is ',history)
-        #store_message(db,history[-1][0],history[-1][1],cross_encoder)
-    else:
-        # Retrieve documents relevant to query
-        document_start = perf_counter()
+#         history[-1][1] = ""
+#         for character in generate_fn(prompt, history[:-1]):
+#             history[-1][1] = character
+#             yield history, prompt_html
+#         print('Final history is ',history)
+#         #store_message(db,history[-1][0],history[-1][1],cross_encoder)
+#     else:
+#         # Retrieve documents relevant to query
+#         document_start = perf_counter()
     
-        query_vec = retriever.encode(query)
-        logger.warning(f'Finished query vec')
-        doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
+#         query_vec = retriever.encode(query)
+#         logger.warning(f'Finished query vec')
+#         doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
     
         
     
-        logger.warning(f'Finished search')
-        documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
-        documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
-        logger.warning(f'start cross encoder {len(documents)}')
-        # Retrieve documents relevant to query
-        query_doc_pair = [[query, doc] for doc in documents]
-        if cross_encoder=='(FAST) MiniLM-L6v2' :
-               cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') 
-        elif cross_encoder=='(ACCURATE) BGE reranker':
-               cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
+#         logger.warning(f'Finished search')
+#         documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
+#         documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
+#         logger.warning(f'start cross encoder {len(documents)}')
+#         # Retrieve documents relevant to query
+#         query_doc_pair = [[query, doc] for doc in documents]
+#         if cross_encoder=='(FAST) MiniLM-L6v2' :
+#                cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') 
+#         elif 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)))
-        logger.warning(f'Finished cross encoder {len(documents)}')
+#         cross_scores = cross_encoder1.predict(query_doc_pair)
+#         sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
+#         logger.warning(f'Finished cross encoder {len(documents)}')
         
-        documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
-        logger.warning(f'num documents {len(documents)}')
+#         documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
+#         logger.warning(f'num documents {len(documents)}')
     
-        document_time = perf_counter() - document_start
-        logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')
+#         document_time = perf_counter() - document_start
+#         logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')
     
-        # Create Prompt
-        prompt = template.render(documents=documents, query=query)
-        prompt_html = template_html.render(documents=documents, query=query)
+#         # Create Prompt
+#         prompt = template.render(documents=documents, query=query)
+#         prompt_html = template_html.render(documents=documents, query=query)
     
-        generate_fn = generate_hf
+#         generate_fn = generate_hf
     
-        history[-1][1] = ""
-        for character in generate_fn(prompt, history[:-1]):
-            history[-1][1] = character            
-            yield history, prompt_html
-        print('Final history is ',history)
-        #store_message(db,history[-1][0],history[-1][1],cross_encoder)
-
-def system_instructions(question_difficulty, topic,documents_str):
-    return f"""<s> [INST] Your are a great teacher and your task is to create 10 questions with 4 choices with a {question_difficulty} difficulty  about topic request " {topic} " only from the below given documents, {documents_str} then create an 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#". [/INST]"""
-
-RAG_db = gr.State()
-
-def load_model():
-    try:
-        # Initialize the model
-        RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-        # Load the RAG database
-        RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-        return 'Ready to Go!!'
-    except Exception as e:
-        return f"Error loading model: {e}"
+#         history[-1][1] = ""
+#         for character in generate_fn(prompt, history[:-1]):
+#             history[-1][1] = character            
+#             yield history, prompt_html
+#         print('Final history is ',history)
+#         #store_message(db,history[-1][0],history[-1][1],cross_encoder)
+
+# def system_instructions(question_difficulty, topic,documents_str):
+#     return f"""<s> [INST] Your are a great teacher and your task is to create 10 questions with 4 choices with a {question_difficulty} difficulty  about topic request " {topic} " only from the below given documents, {documents_str} then create an 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#". [/INST]"""
+
+# RAG_db = gr.State()
+
+# def load_model():
+#     try:
+#         # Initialize the model
+#         RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
+#         # Load the RAG database
+#         RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
+#         return 'Ready to Go!!'
+#     except Exception as e:
+#         return f"Error loading model: {e}"
     
 
-def generate_quiz(question_difficulty, topic):
-    if not topic.strip():
-        return ['Please enter a valid topic.'] + [gr.Radio(visible=False) for _ in range(10)]
+# def generate_quiz(question_difficulty, topic):
+#     if not topic.strip():
+#         return ['Please enter a valid topic.'] + [gr.Radio(visible=False) for _ in range(10)]
   
-    top_k_rank = 10
-    # Load the model and database within the generate_quiz function
-    try:
-        RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-        RAG_db_ = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-        gr.Warning('Model loaded!')
-    except Exception as e:
-        return [f"Error loading model: {e}"] + [gr.Radio(visible=False) for _ in range(10)]
+#     top_k_rank = 10
+#     # Load the model and database within the generate_quiz function
+#     try:
+#         RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
+#         RAG_db_ = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
+#         gr.Warning('Model loaded!')
+#     except Exception as e:
+#         return [f"Error loading model: {e}"] + [gr.Radio(visible=False) for _ in range(10)]
   
-    RAG_db_ = RAG_db.value
-    documents_full = RAG_db_.search(topic, k=top_k_rank)
+#     RAG_db_ = RAG_db.value
+#     documents_full = RAG_db_.search(topic, k=top_k_rank)
     
-    generate_kwargs = dict(
-        temperature=0.2,
-        max_new_tokens=4000,
-        top_p=0.95,
-        repetition_penalty=1.0,
-        do_sample=True,
-        seed=42,
-    )
+#     generate_kwargs = dict(
+#         temperature=0.2,
+#         max_new_tokens=4000,
+#         top_p=0.95,
+#         repetition_penalty=1.0,
+#         do_sample=True,
+#         seed=42,
+#     )
     
-    question_radio_list = []
-    count = 0
-    while count <= 3:
-        try:
-            documents = [item['content'] for item in documents_full]
-            document_summaries = [f"[DOCUMENT {i+1}]: {summary}{count}" for i, summary in enumerate(documents)]
-            documents_str = '\n'.join(document_summaries)
-            formatted_prompt = system_instructions(question_difficulty, topic, documents_str)
+#     question_radio_list = []
+#     count = 0
+#     while count <= 3:
+#         try:
+#             documents = [item['content'] for item in documents_full]
+#             document_summaries = [f"[DOCUMENT {i+1}]: {summary}{count}" for i, summary in enumerate(documents)]
+#             documents_str = '\n'.join(document_summaries)
+#             formatted_prompt = system_instructions(question_difficulty, topic, documents_str)
             
-            pre_prompt = [
-                {"role": "system", "content": formatted_prompt}
-            ]
-            response = client.text_generation(
-                formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False,
-            )
-            output_json = json.loads(f"{response}")
+#             pre_prompt = [
+#                 {"role": "system", "content": formatted_prompt}
+#             ]
+#             response = client.text_generation(
+#                 formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False,
+#             )
+#             output_json = json.loads(f"{response}")
             
-            global quiz_data
-            quiz_data = output_json
+#             global quiz_data
+#             quiz_data = output_json
             
-            for question_num in range(1, 11):
-                question_key = f"Q{question_num}"
-                answer_key = f"A{question_num}"
-                question = quiz_data.get(question_key)
-                answer = quiz_data.get(quiz_data.get(answer_key))
+#             for question_num in range(1, 11):
+#                 question_key = f"Q{question_num}"
+#                 answer_key = f"A{question_num}"
+#                 question = quiz_data.get(question_key)
+#                 answer = quiz_data.get(quiz_data.get(answer_key))
                 
-                if not question or not answer:
-                    continue
+#                 if not question or not answer:
+#                     continue
                 
-                choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
-                choice_list = [quiz_data.get(choice_key, "Choice not found") for choice_key in choice_keys]
+#                 choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
+#                 choice_list = [quiz_data.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)
+#                 radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True)
+#                 question_radio_list.append(radio)
             
-            if len(question_radio_list) == 10:
-                break
-            else:
-                count += 1
-                continue
-        except Exception as e:
-            count += 1
-            if count == 3:
-                return ['Sorry. Pls try with another topic!'] + [gr.Radio(visible=False) for _ in range(10)]
-            continue
+#             if len(question_radio_list) == 10:
+#                 break
+#             else:
+#                 count += 1
+#                 continue
+#         except Exception as e:
+#             count += 1
+#             if count == 3:
+#                 return ['Sorry. Pls try with another topic!'] + [gr.Radio(visible=False) for _ in range(10)]
+#             continue
     
-    return ['Quiz Generated!'] + question_radio_list
+#     return ['Quiz Generated!'] + question_radio_list
 
-def compare_answers(*user_answers):
-    user_answer_list = user_answers
-    answers_list = [quiz_data.get(quiz_data.get(f"A{question_num}")) for question_num in range(1, 11)]
+# def compare_answers(*user_answers):
+#     user_answer_list = user_answers
+#     answers_list = [quiz_data.get(quiz_data.get(f"A{question_num}")) for question_num in range(1, 11)]
     
-    score = sum(1 for answer in user_answer_list if answer in answers_list)
+#     score = sum(1 for answer in user_answer_list if answer 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!"
+#     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
+#     return message
 
-#with gr.Blocks(theme='Insuz/SimpleIndigo') as demo:
-with gr.Blocks(theme='NoCrypt/miku') as CHATBOT:
-    with gr.Row():
-        with gr.Column(scale=10):
-            # gr.Markdown(
-            #     """
-            #     # Theme preview: `paris`
-            #     To use this theme, set `theme='earneleh/paris'` in `gr.Blocks()` or `gr.Interface()`.
-            #     You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
-            #     of this theme.
-            #     """
-            # )
-            gr.HTML(value="""<div style="color: #FF4500;"><h1>ADWITIYA-</h1> <h1><span style="color: #008000">Custom Manual Chatbot and Quizbot</span></h1>
-            </div>""", elem_id='heading')
+# #with gr.Blocks(theme='Insuz/SimpleIndigo') as demo:
+# with gr.Blocks(theme='NoCrypt/miku') as CHATBOT:
+#     with gr.Row():
+#         with gr.Column(scale=10):
+#             # gr.Markdown(
+#             #     """
+#             #     # Theme preview: `paris`
+#             #     To use this theme, set `theme='earneleh/paris'` in `gr.Blocks()` or `gr.Interface()`.
+#             #     You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
+#             #     of this theme.
+#             #     """
+#             # )
+#             gr.HTML(value="""<div style="color: #FF4500;"><h1>ADWITIYA-</h1> <h1><span style="color: #008000">Custom Manual Chatbot and Quizbot</span></h1>
+#             </div>""", elem_id='heading')
         
-            gr.HTML(value=f"""
-            <p style="font-family: sans-serif; font-size: 16px;">
-              Using GenAI for CBIC Capacity Building - A free chat bot developed by National Customs Targeting Center  using Open source LLMs for CBIC Officers
-            </p>
-            """, elem_id='Sub-heading')
-            #usage_count = get_and_increment_value_count(db,collection_name, field_name)
-            gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;">Developed by NCTC,Mumbai  . Suggestions may be sent to <a href="mailto:nctc-admin@gov.in" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.</p>""", elem_id='Sub-heading1 ')
+#             gr.HTML(value=f"""
+#             <p style="font-family: sans-serif; font-size: 16px;">
+#               Using GenAI for CBIC Capacity Building - A free chat bot developed by National Customs Targeting Center  using Open source LLMs for CBIC Officers
+#             </p>
+#             """, elem_id='Sub-heading')
+#             #usage_count = get_and_increment_value_count(db,collection_name, field_name)
+#             gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;">Developed by NCTC,Mumbai  . Suggestions may be sent to <a href="mailto:nctc-admin@gov.in" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.</p>""", elem_id='Sub-heading1 ')
 
-        with gr.Column(scale=3):
-            gr.Image(value='logo.png',height=200,width=200)
+#         with gr.Column(scale=3):
+#             gr.Image(value='logo.png',height=200,width=200)
 
     
-#     gr.HTML(value="""<div style="color: #FF4500;"><h1>CHEERFULL CBSE-</h1> <h1><span style="color: #008000">AI Assisted Fun Learning</span></h1>
-#     <img src='logo.png' alt="Chatbot" width="50" height="50" />
-#     </div>""", elem_id='heading')
-
-#     gr.HTML(value=f"""
-#     <p style="font-family: sans-serif; font-size: 16px;">
-#       A free Artificial Intelligence  Chatbot assistant trained on CBSE Class 10 Science Notes to engage and help students and teachers of Puducherry.
-#     </p>
-#     """, elem_id='Sub-heading')
-#     #usage_count = get_and_increment_value_count(db,collection_name, field_name)
-#     gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 16px;">Developed by K M Ramyasri , PGT . Suggestions may be sent to <a href="mailto:ramyadevi1607@yahoo.com" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.</p>""", elem_id='Sub-heading1 ')
-# #     count_html = gr.HTML(value=f"""
-# #     <div style="display: flex; justify-content: flex-end;">
-# #         <span style="font-weight: bold; color: maroon; font-size: 18px;">No of Usages:</span>
-# #         <span style="font-weight: bold; color: maroon; font-size: 18px;">{usage_count}</span>
-# #     </div>
-# # """)
+# #     gr.HTML(value="""<div style="color: #FF4500;"><h1>CHEERFULL CBSE-</h1> <h1><span style="color: #008000">AI Assisted Fun Learning</span></h1>
+# #     <img src='logo.png' alt="Chatbot" width="50" height="50" />
+# #     </div>""", elem_id='heading')
+
+# #     gr.HTML(value=f"""
+# #     <p style="font-family: sans-serif; font-size: 16px;">
+# #       A free Artificial Intelligence  Chatbot assistant trained on CBSE Class 10 Science Notes to engage and help students and teachers of Puducherry.
+# #     </p>
+# #     """, elem_id='Sub-heading')
+# #     #usage_count = get_and_increment_value_count(db,collection_name, field_name)
+# #     gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 16px;">Developed by K M Ramyasri , PGT . Suggestions may be sent to <a href="mailto:ramyadevi1607@yahoo.com" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.</p>""", elem_id='Sub-heading1 ')
+# # #     count_html = gr.HTML(value=f"""
+# # #     <div style="display: flex; justify-content: flex-end;">
+# # #         <span style="font-weight: bold; color: maroon; font-size: 18px;">No of Usages:</span>
+# # #         <span style="font-weight: bold; color: maroon; font-size: 18px;">{usage_count}</span>
+# # #     </div>
+# # # """)
    
-    chatbot = gr.Chatbot(
-            [],
-            elem_id="chatbot",
-            avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
-                           'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
-            bubble_full_width=False,
-            show_copy_button=True,
-            show_share_button=True,
-            )
+#     chatbot = gr.Chatbot(
+#             [],
+#             elem_id="chatbot",
+#             avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
+#                            'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
+#             bubble_full_width=False,
+#             show_copy_button=True,
+#             show_share_button=True,
+#             )
 
-    with gr.Row():
-        txt = gr.Textbox(
-                scale=3,
-                show_label=False,
-                placeholder="Enter text and press enter",
-                container=False,
-                )
-        txt_btn = gr.Button(value="Submit text", scale=1)
+#     with gr.Row():
+#         txt = gr.Textbox(
+#                 scale=3,
+#                 show_label=False,
+#                 placeholder="Enter text and press enter",
+#                 container=False,
+#                 )
+#         txt_btn = gr.Button(value="Submit text", scale=1)
 
-    cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2','(ACCURATE) BGE reranker','(HIGH ACCURATE) ColBERT'], value='(ACCURATE) BGE reranker',label="Embeddings", info="Only First query to Colbert may take litte time)")
+#     cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2','(ACCURATE) BGE reranker','(HIGH ACCURATE) ColBERT'], value='(ACCURATE) BGE reranker',label="Embeddings", info="Only First query to Colbert may take litte time)")
 
-    prompt_html = gr.HTML()
-    # Turn off interactivity while generating if you click
-    txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
-            bot, [chatbot, cross_encoder], [chatbot, prompt_html])#.then(update_count_html,[],[count_html])
+#     prompt_html = gr.HTML()
+#     # Turn off interactivity while generating if you click
+#     txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+#             bot, [chatbot, cross_encoder], [chatbot, prompt_html])#.then(update_count_html,[],[count_html])
 
-    # Turn it back on
-    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
+#     # Turn it back on
+#     txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
 
-    # Turn off interactivity while generating if you hit enter
-    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
-            bot, [chatbot, cross_encoder], [chatbot, prompt_html])#.then(update_count_html,[],[count_html])
+#     # Turn off interactivity while generating if you hit enter
+#     txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+#             bot, [chatbot, cross_encoder], [chatbot, prompt_html])#.then(update_count_html,[],[count_html])
 
-    # Turn it back on
-    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
+#     # Turn it back on
+#     txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
 
-    # Examples
-    gr.Examples(examples, txt)
+#     # Examples
+#     gr.Examples(examples, txt)
 
 
 
 
-with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green"), css="style.css") as QUIZBOT:
-    with gr.Column(scale=4):
-        gr.HTML("""
-        <center>
-            <h1><span style="color: purple;">ADWITIYA</span> Customs Manual Quizbot</h1>
-            <h2>Generative AI-powered Capacity building for Training Officers</h2>
-            <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions! ⚠️</i>
-        </center>
-        """)
+# with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green"), css="style.css") as QUIZBOT:
+#     with gr.Column(scale=4):
+#         gr.HTML("""
+#         <center>
+#             <h1><span style="color: purple;">ADWITIYA</span> Customs Manual Quizbot</h1>
+#             <h2>Generative AI-powered Capacity building for Training Officers</h2>
+#             <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions! ⚠️</i>
+#         </center>
+#         """)
     
-    with gr.Column(scale=2):
-        gr.HTML("""
-        <center>
+#     with gr.Column(scale=2):
+#         gr.HTML("""
+#         <center>
             
-            <h2>Ready!</h2>
+#             <h2>Ready!</h2>
             
-        </center>
-        """)
-        # load_btn = gr.Button("Click to Load!🚀")
-        # load_text = gr.Textbox()
-        # load_btn.click(fn=load_model, outputs=load_text)
+#         </center>
+#         """)
+#         # load_btn = gr.Button("Click to Load!🚀")
+#         # load_text = gr.Textbox()
+#         # load_btn.click(fn=load_model, outputs=load_text)
     
-    topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual")
+#     topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual")
 
-    with gr.Row():
-        radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
+#     with gr.Row():
+#         radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
     
-    generate_quiz_btn = gr.Button("Generate Quiz!🚀")
-    quiz_msg = gr.Textbox()
+#     generate_quiz_btn = gr.Button("Generate Quiz!🚀")
+#     quiz_msg = gr.Textbox()
     
-    question_radios = [gr.Radio(visible=False) for _ in range(10)]
+#     question_radios = [gr.Radio(visible=False) for _ in range(10)]
     
-    generate_quiz_btn.click(
-        fn=generate_quiz, 
-        inputs=[radio, topic], 
-        outputs=[quiz_msg] + question_radios
-    )
+#     generate_quiz_btn.click(
+#         fn=generate_quiz, 
+#         inputs=[radio, topic], 
+#         outputs=[quiz_msg] + question_radios
+#     )
     
-    check_button = gr.Button("Check Score")
-    score_textbox = gr.Markdown()
+#     check_button = gr.Button("Check Score")
+#     score_textbox = gr.Markdown()
     
-    check_button.click(
-        fn=compare_answers,
-        inputs=question_radios,
-        outputs=score_textbox
-    )
+#     check_button.click(
+#         fn=compare_answers,
+#         inputs=question_radios,
+#         outputs=score_textbox
+#     )
 
-demo = gr.TabbedInterface([CHATBOT, QUIZBOT], ["AI ChatBot", "AI Quizbot"])
-demo.queue()
-demo.launch(debug=True)
+# demo = gr.TabbedInterface([CHATBOT, QUIZBOT], ["AI ChatBot", "AI Quizbot"])
+# demo.queue()
+# demo.launch(debug=True)
 
-# RAG_db=gr.State()
+RAG_db=gr.State()
 
-# with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green"), css="style.css") as QUIZBOT:
-#     def load_model():
-#         RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-#         RAG_db.value=RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-#         return 'Ready to Go!!'
-#     with gr.Column(scale=4):
-#         gr.HTML("""
-#     <center>
-#       <h1><span style="color: purple;">ADWITIYA</span> Customs Manual  Quizbot</h1>
-#       <h2>Generative AI-powered Capacity building for Training Officers</h2>
-#       <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️</i>
-#     </center>
-#     """)
-#         #gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
-#     with gr.Column(scale=2):
-#         load_btn = gr.Button("Click to Load!🚀")
-#         load_text=gr.Textbox()
-#         load_btn.click(load_model,[],load_text)
+with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green"), css="style.css") as QUIZBOT:
+    def load_model():
+        RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
+        RAG_db.value=RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
+        return 'Ready to Go!!'
+    with gr.Column(scale=4):
+        gr.HTML("""
+    <center>
+      <h1><span style="color: purple;">ADWITIYA</span> Customs Manual  Quizbot</h1>
+      <h2>Generative AI-powered Capacity building for Training Officers</h2>
+      <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️</i>
+    </center>
+    """)
+        #gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
+    with gr.Column(scale=2):
+        load_btn = gr.Button("Click to Load!🚀")
+        load_text=gr.Textbox()
+        load_btn.click(load_model,[],load_text)
         
    
-#     topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual")
+    topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual")
 
-#     with gr.Row():
-#         radio = gr.Radio(
-#             ["easy", "average", "hard"], label="How difficult should the quiz be?"
-#         )
+    with gr.Row():
+        radio = gr.Radio(
+            ["easy", "average", "hard"], label="How difficult should the quiz be?"
+        )
 
 
-#     generate_quiz_btn = gr.Button("Generate Quiz!🚀")
-#     quiz_msg=gr.Textbox()
+    generate_quiz_btn = gr.Button("Generate Quiz!🚀")
+    quiz_msg=gr.Textbox()
 
-#     question_radios = [gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(
-#         visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(
-#         visible=False), gr.Radio(visible=False), gr.Radio(visible=False)]
+    question_radios = [gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(
+        visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(
+        visible=False), gr.Radio(visible=False), gr.Radio(visible=False)]
 
-#     print(question_radios)
+    print(question_radios)
 
-#     @spaces.GPU
-#     @generate_quiz_btn.click(inputs=[radio, topic], outputs=[quiz_msg]+question_radios, api_name="generate_quiz")
-#     def generate_quiz(question_difficulty, topic):
-#         top_k_rank=10
-#         RAG_db_=RAG_db.value
-#         documents_full=RAG_db_.search(topic,k=top_k_rank)
+    @spaces.GPU
+    @generate_quiz_btn.click(inputs=[radio, topic], outputs=[quiz_msg]+question_radios, api_name="generate_quiz")
+    def generate_quiz(question_difficulty, topic):
+        top_k_rank=10
+        RAG_db_=RAG_db.value
+        documents_full=RAG_db_.search(topic,k=top_k_rank)
     
         
 
-#         generate_kwargs = dict(
-#             temperature=0.2,
-#             max_new_tokens=4000,
-#             top_p=0.95,
-#             repetition_penalty=1.0,
-#             do_sample=True,
-#             seed=42,
-#         )
-#         question_radio_list = []
-#         count=0
-#         while count<=3:
-#             try:
-#                 documents=[item['content'] for item in documents_full]
-#                 document_summaries = [f"[DOCUMENT {i+1}]: {summary}{count}" for i, summary in enumerate(documents)]
-#                 documents_str='\n'.join(document_summaries)
-#                 formatted_prompt = system_instructions(
-#                     question_difficulty, topic,documents_str)
-#                 print(formatted_prompt)
-#                 pre_prompt = [
-#                     {"role": "system", "content": formatted_prompt}
-#                 ]
-#                 response = client.text_generation(
-#                     formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False,
-#                 )
-#                 output_json = json.loads(f"{response}")
+        generate_kwargs = dict(
+            temperature=0.2,
+            max_new_tokens=4000,
+            top_p=0.95,
+            repetition_penalty=1.0,
+            do_sample=True,
+            seed=42,
+        )
+        question_radio_list = []
+        count=0
+        while count<=3:
+            try:
+                documents=[item['content'] for item in documents_full]
+                document_summaries = [f"[DOCUMENT {i+1}]: {summary}{count}" for i, summary in enumerate(documents)]
+                documents_str='\n'.join(document_summaries)
+                formatted_prompt = system_instructions(
+                    question_difficulty, topic,documents_str)
+                print(formatted_prompt)
+                pre_prompt = [
+                    {"role": "system", "content": formatted_prompt}
+                ]
+                response = client.text_generation(
+                    formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False,
+                )
+                output_json = json.loads(f"{response}")
                 
         
-#                 print(response)
-#                 print('output json', output_json)
+                print(response)
+                print('output json', output_json)
         
-#                 global quiz_data
+                global quiz_data
         
-#                 quiz_data = output_json
+                quiz_data = output_json
         
                 
         
-#                 for question_num in range(1, 11):
-#                     question_key = f"Q{question_num}"
-#                     answer_key = f"A{question_num}"
+                for question_num in range(1, 11):
+                    question_key = f"Q{question_num}"
+                    answer_key = f"A{question_num}"
         
-#                     question = quiz_data.get(question_key)
-#                     answer = quiz_data.get(quiz_data.get(answer_key))
+                    question = quiz_data.get(question_key)
+                    answer = quiz_data.get(quiz_data.get(answer_key))
         
-#                     if not question or not answer:
-#                         continue
+                    if not question or not answer:
+                        continue
         
-#                     choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
-#                     choice_list = []
-#                     for choice_key in choice_keys:
-#                         choice = quiz_data.get(choice_key, "Choice not found")
-#                         choice_list.append(f"{choice}")
+                    choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
+                    choice_list = []
+                    for choice_key in choice_keys:
+                        choice = quiz_data.get(choice_key, "Choice not found")
+                        choice_list.append(f"{choice}")
         
-#                     radio = gr.Radio(choices=choice_list, label=question,
-#                                      visible=True, interactive=True)
+                    radio = gr.Radio(choices=choice_list, label=question,
+                                     visible=True, interactive=True)
         
-#                     question_radio_list.append(radio)
-#                 if len(question_radio_list)==10:
-#                     break
-#                 else:
-#                     print('10 questions not generated . So trying again!')
-#                     count+=1
-#                     continue
-#             except Exception as e:
-#                 count+=1
-#                 print(f"Exception occurred: {e}")
-#                 if count==3:
-#                     print('Retry exhausted')
-#                     gr.Warning('Sorry. Pls try with another topic !')
-#                 else:
-#                     print(f"Trying again..{count} time...please wait")
-#                     continue
+                    question_radio_list.append(radio)
+                if len(question_radio_list)==10:
+                    break
+                else:
+                    print('10 questions not generated . So trying again!')
+                    count+=1
+                    continue
+            except Exception as e:
+                count+=1
+                print(f"Exception occurred: {e}")
+                if count==3:
+                    print('Retry exhausted')
+                    gr.Warning('Sorry. Pls try with another topic !')
+                else:
+                    print(f"Trying again..{count} time...please wait")
+                    continue
 
-#         print('Question radio list ' , question_radio_list)
+        print('Question radio list ' , question_radio_list)
 
-#         return ['Quiz Generated!']+ question_radio_list
+        return ['Quiz Generated!']+ question_radio_list
 
-#     check_button = gr.Button("Check Score")
+    check_button = gr.Button("Check Score")
 
-#     score_textbox = gr.Markdown()
+    score_textbox = gr.Markdown()
 
-#     @check_button.click(inputs=question_radios, outputs=score_textbox)
-#     def compare_answers(*user_answers):
-#         user_anwser_list = []
-#         user_anwser_list = user_answers
+    @check_button.click(inputs=question_radios, outputs=score_textbox)
+    def compare_answers(*user_answers):
+        user_anwser_list = []
+        user_anwser_list = user_answers
 
-#         answers_list = []
+        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)
+        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 = 0
+        score = 0
 
-#         for item in user_anwser_list:
-#             if item in answers_list:
-#                 score += 1
-#         if score>5:
-#              message = f"### Good ! You got {score} over 10!"
-#         elif score>7:
-#              message = f"### Excellent ! You got {score} over 10!"
-#         else:
-#              message = f"### You got {score} over 10! Dont worry . You can prepare well and try better next time !"
+        for item in user_anwser_list:
+            if item in answers_list:
+                score += 1
+        if score>5:
+             message = f"### Good ! You got {score} over 10!"
+        elif score>7:
+             message = f"### Excellent ! You got {score} over 10!"
+        else:
+             message = f"### You got {score} over 10! Dont worry . You can prepare well and try better next time !"
 
-#         return message
+        return message
 
 
 
 # demo = gr.TabbedInterface([CHATBOT,QUIZBOT], ["AI ChatBot", "AI Quizbot"])
 
-# demo.queue()
-# demo.launch(debug=True)
+QUIZBOT.queue()
+QUIZBOT.launch(debug=True)