Update app.py

app.py

@@ -1,437 +1,437 @@
-
-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')
-#___________________
-# 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 = ['Tabulate the difference between veins and arteries','What are defects in Human eye?',
-            'Frame 5 short questions and 5 MCQ on Chapter 2 ','Suggest creative and engaging ideas to teach students on Chapter on Metals and Non Metals '
-            ]
-
-
-
-# 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]"""
-
-
-#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>CHEERFULL CBSE-</h1> <h1><span style="color: #008000">AI Assisted Fun Learning</span></h1>
-            </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: 14px;">Developed by K M Ramyasri , TGT,GHS.SUTHUKENY . Suggestions may be sent to <a href="mailto:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</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:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</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])
-
-    # 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)
-
-
-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;">AI NANBAN</span> - CBSE Class Quiz Maker</h1>
-      <h2>AI-powered Learning Game</h2>
-      <i>⚠️ Students create quiz from any topic /CBSE Chapter ! ⚠️</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 from CBSE notes")
-
-    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)
-    
-        
-
-        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
-
-
-
-demo = gr.TabbedInterface([CHATBOT,QUIZBOT], ["AI ChatBot", "AI Nanban-Quizbot"])
-
-demo.queue()
-demo.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 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')
+#___________________
+# 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 = ['Tabulate the difference between veins and arteries','What are defects in Human eye?',
+            'Frame 5 short questions and 5 MCQ on Chapter 2 ','Suggest creative and engaging ideas to teach students on Chapter on Metals and Non Metals '
+            ]
+
+
+
+# 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]"""
+
+
+#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>CHEERFULL CBSE-</h1> <h1><span style="color: #008000">AI Assisted Fun Learning</span></h1>
+            </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: 14px;">Developed by K M Ramyasri , TGT,GHS.SUTHUKENY . Suggestions may be sent to <a href="mailto:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</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:[email protected]" style="color: #00008B; font-style: italic;">[email protected]</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])
+
+    # 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)
+
+
+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)
+    
+        
+
+        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
+
+
+
+demo = gr.TabbedInterface([CHATBOT,QUIZBOT], ["AI ChatBot", "AI Quizbot"])
+
+demo.queue()
+demo.launch(debug=True)