# 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
# 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 os import getenv
# from backend.query_llm import generate_hf, generate_openai
# from backend.semantic_search import table, retriever
# from gradio_client import Client  # Modified here

# VECTOR_COLUMN_NAME = "vector"
# TEXT_COLUMN_NAME = "text"
# proj_dir = Path(__file__).parent

# # Setting up the logging
# logging.basicConfig(level=logging.INFO)
# logger = logging.getLogger(__name__)

# # 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')

# 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 is 'A10':'Q10:C3' [/INST]"""

# # 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')
#         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>
#         """)

#     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) for _ in range(10)]

#     @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 minutes. Please wait.')

#         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)

#                 # 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"
#                 )
#                 print(response)
#                 response1=response[1][0][1]
#                 # Find the first and last curly braces
#                 start_index = response1.find('{')
#                 end_index = response1.rfind('}')
                
#                 # Extract only the JSON part
#                 if start_index != -1 and end_index != -1:
#                     cleaned_response = response1[start_index:end_index + 1]  # Include the last closing brace
                
#                     # Try parsing the cleaned JSON
#                     try:
#                         output_json = json.loads(cleaned_response)
#                         print('Parsed JSON:', output_json)  # Successfully print the dictionary
#                     except json.JSONDecodeError as e:
#                         print(f"Failed to decode JSON: {e}")
#                 else:
#                     print("No valid JSON found in the 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 = [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)
#                 print('question_radio_list',question_radio_list)

#                 if len(question_radio_list) == 10:
#                     break
#                 else:
#                     print('10 questions not generated. 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. Please try with another topic!')
#                 else:
#                     print(f"Trying again.. {count} time... please wait")
#                     continue

#         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 = list(user_answers)
#         answers_list = []

#         for question_num in range(1, 20):
#             answer_key = f"A{question_num}"
#             answer = quiz_data.get(quiz_data.get(answer_key))
#             if not answer:
#                 break
#             answers_list.append(answer)

#         score = sum(1 for item in user_anwser_list if item in answers_list)

#         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} out of 10! Don't worry. You can prepare well and try better next time!"

#         return message

# QUIZBOT.queue()
# QUIZBOT.launch(debug=True)
# ##############??????????????????????????????
# import pandas as pd
# import json
# import gradio as gr
# from pathlib import Path
# from ragatouille import RAGPretrainedModel
# from gradio_client import Client
# from jinja2 import Environment, FileSystemLoader
# from tempfile import NamedTemporaryFile

# VECTOR_COLUMN_NAME = "vector"
# TEXT_COLUMN_NAME = "text"
# #proj_dir = Path(__file__).parent
# proj_dir = Path.cwd() 
# # Setting up the 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")

# # 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] 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 is 'A10':'Q10:C3' [/INST]"""

# # RA
# RAG_db = gr.State()

# 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 ''
        
#         # 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"
#     ])
#     # Create a temporary file and save the DataFrame to it
#     temp_file = NamedTemporaryFile(delete=False, suffix=".xlsx")
#     df.to_excel(temp_file.name, index=False)

#     # # Save to Excel file
#     # excel_path = proj_dir / "quiz_questions.xlsx"
#     # df.to_excel(str(excel_path), index=False)
#     return temp_file.name

# 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>
#         """)

#     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) for _ in range(10)]

#     #@gr.dependencies.GPU
#     @generate_quiz_btn.click(inputs=[radio, topic], outputs=[quiz_msg] + question_radios + [gr.File(label="Download Excel")], 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 minutes. Please wait.',duration=60)

#         question_radio_list = []
#         excel_file = None

#         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)

#                 # Use Qwen Client for quiz generation
#                 response = client.predict(
#                     query=formatted_prompt,
#                     history=[],
#                     system="You are a helpful assistant.", 
#                     api_name="/model_chat"
#                 )
#                 print(response)
#                 response1 = response[1][0][1]

#                 # Find the first and last curly braces
#                 start_index = response1.find('{')
#                 end_index = response1.rfind('}')
                
#                 # Extract only the JSON part
#                 if start_index != -1 and end_index != -1:
#                     cleaned_response = response1[start_index:end_index + 1]
                
#                     # Try parsing the cleaned JSON
#                     try:
#                         output_json = json.loads(cleaned_response)
#                         print('Parsed JSON:', output_json) 
#                         global quiz_data
#                         quiz_data = output_json

#                         # Generate the Excel file
#                         excel_file = json_to_excel(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)
#                         print('question_radio_list', question_radio_list)

#                         if len(question_radio_list) == 10:
#                             break
#                         else:
#                             print('10 questions not generated. Trying again!')
#                             count += 1
#                             continue
#                     except json.JSONDecodeError as e:
#                         print(f"Failed to decode JSON: {e}")
#                 else:
#                     print("No valid JSON found in the response.")
                  
#             except Exception as e:
#                 count += 1
#                 print(f"Exception occurred: {e}")
#                 if count == 3:
#                     print('Retry exhausted')
#                     gr.Warning('Sorry. Please try with another topic!')
#                 else:
#                     print(f"Trying again.. {count} time... please wait")
#                     continue

#         return ['Quiz Generated!'] + question_radio_list + [excel_file]

#     check_button = gr.Button("Check Score")
#     score_textbox = gr.Markdown()

#     @check_button.click(inputs=question_radios, outputs=score_textbox)
#     def compare_answers(*user_answers):
#         user_answer_list = list(user_answers)
#         answers_list = []

#         for question_num in range(1, 20):
#             answer_key = f"A{question_num}"
#             answer = quiz_data.get(quiz_data.get(answer_key))
#             if not answer:
#                 break
#             answers_list.append(answer)

#         score = sum(1 for item in user_answer_list if item in answers_list)

#         if score > 7:
#             message = f"### Excellent! You got {score} out of 10!"
#         elif score > 5:
#             message = f"### Good! You got {score} out of 10!"
#         else:
#             message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!"

#         return message

# QUIZBOT.queue()
# QUIZBOT.launch(debug=True)

#?????????????????????????????????
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

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]"""

# RA
RAG_db = gr.State()

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 ''
        
        # 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

with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green")) 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>
#         """)
    
    topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual")

    with gr.Row():
        difficulty_radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?")
        model_radio = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(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 a minute for model to load.. please wait')
            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')
            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)))
            documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
        
        formatted_prompt = system_instructions(question_difficulty, topic, '\n'.join(documents))

        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 ''
            output_json = json.loads(cleaned_response)

            # Generate the Excel file
            excel_file = json_to_excel(output_json)

            question_radio_list = []
            for question_num in range(1, 11):
                question_key = f"Q{question_num}"
                answer_key = f"A{question_num}"

                question = output_json.get(question_key)
                answer = output_json.get(output_json.get(answer_key))

                if not question or not answer:
                    continue

                choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
                choice_list = [output_json.get(choice_key, "Choice not found") for choice_key in choice_keys]

                radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True)
                question_radio_list.append(radio)

            return ['Quiz Generated!'] + question_radio_list + [excel_file]

        except json.JSONDecodeError as e:
            print(f"Failed to decode JSON: {e}")

    check_button = gr.Button("Check Score")
    score_textbox = gr.Markdown()

    @check_button.click(inputs=question_radios, outputs=score_textbox)
    def compare_answers(*user_answers):
        user_answer_list = list(user_answers)
        answers_list = []

        for question_num in range(1, 20):
            answer_key = f"A{question_num}"
            answer = quiz_data.get(quiz_data.get(answer_key))
            if not answer:
                break
            answers_list.append(answer)

        score = sum(1 for item in user_answer_list if item in answers_list)

        if score > 7:
            message = f"### Excellent! You got {score} out of 10!"
        elif score > 5:
            message = f"### Good! You got {score} out of 10!"
        else:
            message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!"

        return message

QUIZBOT.queue()
QUIZBOT.launch(debug=True)