Update app.py

app.py

@@ -1,4 +1,207 @@
 
+# from ragatouille import RAGPretrainedModel
+# import subprocess
+# import json
+# import spaces
+# import firebase_admin
+# from firebase_admin import credentials, firestore
+# import logging
+# from pathlib import Path
+# from time import perf_counter
+# from datetime import datetime
+# import gradio as gr
+# from jinja2 import Environment, FileSystemLoader
+# import numpy as np
+# from sentence_transformers import CrossEncoder
+# from huggingface_hub import InferenceClient
+# from os import getenv
+
+# from backend.query_llm import generate_hf, generate_openai
+# from backend.semantic_search import table, retriever
+# from huggingface_hub import InferenceClient
+
+
+# VECTOR_COLUMN_NAME = "vector"
+# TEXT_COLUMN_NAME = "text"
+# HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
+# proj_dir = Path(__file__).parent
+# # Setting up the logging
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+# client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1",token=HF_TOKEN)
+# # Set up the template environment with the templates directory
+# env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
+
+# # Load the templates directly from the environment
+# template = env.get_template('template.j2')
+# template_html = env.get_template('template_html.j2')
+
+# 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()
+
+# 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")
+
+#     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()
+
+#     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)
+
+#     @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)
+#         gr.Warning('Generation of Quiz may take 1 to 2  minute. Pls wait')
+        
+
+#         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)
+        
+#                 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))
+        
+#                     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}")
+        
+#                     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
+
+#         print('Question radio list ' , question_radio_list)
+
+#         return ['Quiz Generated!']+ question_radio_list
+
+#     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_anwser_list = []
+#         user_anwser_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 = 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 !"
+
+#         return message
+
+
+# QUIZBOT.queue()
+# QUIZBOT.launch(debug=True)
+
+
+################################################
 from ragatouille import RAGPretrainedModel
 import subprocess
 import json
@@ -13,552 +216,139 @@ import gradio as gr
 from jinja2 import Environment, FileSystemLoader
 import numpy as np
 from sentence_transformers import CrossEncoder
-from huggingface_hub import InferenceClient
 from os import getenv
-
 from backend.query_llm import generate_hf, generate_openai
 from backend.semantic_search import table, retriever
-from huggingface_hub import InferenceClient
-
+from gradio_client import Client  # Modified here
 
 VECTOR_COLUMN_NAME = "vector"
 TEXT_COLUMN_NAME = "text"
-HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
 proj_dir = Path(__file__).parent
+
 # Setting up the logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
-client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1",token=HF_TOKEN)
+
+# Replace Mixtral client with Qwen Client
+client = Client("Qwen/Qwen1.5-110B-Chat-demo")  # Modified here
+
 # Set up the template environment with the templates directory
 env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))
 
 # Load the templates directly from the environment
 template = env.get_template('template.j2')
 template_html = env.get_template('template_html.j2')
-#___________________
-# service_account_key='firebase.json'
-# # Create a Certificate object from the service account info
-# cred = credentials.Certificate(service_account_key)
-# # Initialize the Firebase Admin 
-# firebase_admin.initialize_app(cred)
-
-# # # Create a reference to the Firestore database
-# db = firestore.client()
-# #db usage
-# collection_name = 'Nirvachana'  # Replace with your collection name
-# field_name = 'message_count'  # Replace with your field name for count
-# Examples
-examples = ['My transhipment cargo is missing','can u explain and tabulate difference between b 17 bond and a warehousing bond',
-            'What are benefits of  the AEO Scheme and eligibility criteria?',
-            'What are penalties for customs offences? ', 'what are penalties to customs officers misusing their powers under customs act?','What are eligibility criteria for exemption from cost recovery charges','list in detail what is procedure for obtaining new approval for openeing a CFS attached to an ICD']
-
-
-
-# def get_and_increment_value_count(db , collection_name, field_name):
-#     """
-#     Retrieves a value count from the specified Firestore collection and field,
-#     increments it by 1, and updates the field with the new value."""
-#     collection_ref = db.collection(collection_name)
-#     doc_ref = collection_ref.document('count_doc')  # Assuming a dedicated document for count
-
-#     # Use a transaction to ensure consistency across reads and writes
-#     try:
-#         with db.transaction() as transaction:
-#             # Get the current value count (or initialize to 0 if it doesn't exist)
-#             current_count_doc = doc_ref.get()
-#             current_count_data = current_count_doc.to_dict()
-#             if current_count_data:
-#                 current_count = current_count_data.get(field_name, 0)
-#             else:
-#                 current_count = 0
-#             # Increment the count
-#             new_count = current_count + 1
-#             # Update the document with the new count
-#             transaction.set(doc_ref, {field_name: new_count})
-#             return new_count
-#     except Exception as e:
-#         print(f"Error retrieving and updating value count: {e}")
-#         return None  # Indicate error
-        
-# def update_count_html():
-#     usage_count = get_and_increment_value_count(db ,collection_name, field_name)
-#     ccount_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>
-# """)
-#     return count_html
-    
-# def store_message(db,query,answer,cross_encoder):
-#     timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-#     # Create a new document reference with a dynamic document name based on timestamp
-#     new_completion= db.collection('Nirvachana').document(f"chatlogs_{timestamp}")
-#     new_completion.set({
-#         'query': query,
-#         'answer':answer,
-#         'created_time': firestore.SERVER_TIMESTAMP,
-#         'embedding': cross_encoder,
-#         'title': 'Expenditure observer bot'
-#     })
-
-
-# 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]
-
-#     if not query:
-#          gr.Warning("Please submit a non-empty string as a prompt")
-#          raise ValueError("Empty string was submitted")
-
-#     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)
-        
-#         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
-    
-#         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)
-    
-        
-    
-#         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)}')
-        
-#         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...')
-    
-#         # Create Prompt
-#         prompt = template.render(documents=documents, query=query)
-#         prompt_html = template_html.render(documents=documents, query=query)
-    
-#         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}"
-    
-
-# 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)]
-  
-#     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)
-            
-#             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
-            
-#             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
-                
-#                 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)
-            
-#             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
-
-# 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)
-    
-#     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
-
-# #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 ')
-
-#         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>
-# # # """)
-   
-#     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)
-
-#     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])
+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#". [/INST]"""
 
-#     # 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 it back on
-#     txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
-
-#     # 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.Column(scale=2):
-#         gr.HTML("""
-#         <center>
-            
-#             <h2>Ready!</h2>
-            
-#         </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")
-
-#     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()
-    
-#     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
-#     )
-    
-#     check_button = gr.Button("Check Score")
-#     score_textbox = gr.Markdown()
-    
-#     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)
-
-RAG_db=gr.State()
+# RA
+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')
+        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')
+        <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):
         load_btn = gr.Button("Click to Load!🚀")
-        load_text=gr.Textbox()
-        load_btn.click(load_model,[],load_text)
-        
-   
+        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")
 
     with gr.Row():
-        radio = gr.Radio(
-            ["easy", "average", "hard"], label="How difficult should the quiz be?"
-        )
-
+        radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
 
     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)]
+    quiz_msg = gr.Textbox()
 
-    print(question_radios)
+    question_radios = [gr.Radio(visible=False) for _ in range(10)]
 
     @spaces.GPU
-    @generate_quiz_btn.click(inputs=[radio, topic], outputs=[quiz_msg]+question_radios, api_name="generate_quiz")
+    @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)
-        gr.Warning('Generation of Quiz may take 1 to 2  minute. Pls wait')
-        
+        top_k_rank = 10
+        RAG_db_ = RAG_db.value
+        documents_full = RAG_db_.search(topic, k=top_k_rank)
+
+        gr.Warning('Generation of Quiz may take 1 to 2 minutes. Please wait.')
 
-        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:
+        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)
+                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,
+
+                # Use Qwen Client for quiz generation
+                response = client.predict(
+                    query=formatted_prompt,
+                    history=[],
+                    system="You are a helpful assistant.",  # Modified to match Qwen's API
+                    api_name="/model_chat"
                 )
-                output_json = json.loads(f"{response}")
                 
-        
-                print(response)
+                output_json = json.loads(f"{response}")
                 print('output json', 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))
-        
+
                     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}")
-        
-                    radio = gr.Radio(choices=choice_list, label=question,
-                                     visible=True, interactive=True)
-        
+                    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)
-                if len(question_radio_list)==10:
+
+                if len(question_radio_list) == 10:
                     break
                 else:
-                    print('10 questions not generated . So trying again!')
-                    count+=1
+                    print('10 questions not generated. Trying again!')
+                    count += 1
                     continue
             except Exception as e:
-                count+=1
+                count += 1
                 print(f"Exception occurred: {e}")
-                if count==3:
+                if count == 3:
                     print('Retry exhausted')
-                    gr.Warning('Sorry. Pls try with another topic !')
+                    gr.Warning('Sorry. Please try with another topic!')
                 else:
-                    print(f"Trying again..{count} time...please wait")
+                    print(f"Trying again.. {count} time... please wait")
                     continue
 
-        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")
-
     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
-
+        user_anwser_list = list(user_answers)
         answers_list = []
 
         for question_num in range(1, 20):
@@ -568,23 +358,16 @@ with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green",
                 break
             answers_list.append(answer)
 
-        score = 0
+        score = sum(1 for item in user_anwser_list if item in answers_list)
 
-        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!"
+        if score > 5:
+            message = f"### Good! You got {score} out of 10!"
+        elif score > 7:
+            message = f"### Excellent! You got {score} out of 10!"
         else:
-             message = f"### You got {score} over 10! Dont worry . You can prepare well and try better next time !"
+            message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!"
 
         return message
 
-
-
-# demo = gr.TabbedInterface([CHATBOT,QUIZBOT], ["AI ChatBot", "AI Quizbot"])
-
 QUIZBOT.queue()
 QUIZBOT.launch(debug=True)