Update app.py

app.py

@@ -1,134 +1,95 @@
-
+import requests
+import gradio as gr
 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 jinja2 import Environment, FileSystemLoader
+import numpy as np
 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
+# Bhashini API translation function
+api_key = '22fedab828-fff6-4562-8c1b-2a95ebdb5276'
+user_id = '7e477b6801dc4ff9a70a6ac13939b1e6'
+
+def bhashini_translate(text: str, from_code: str = "en", to_code: str = "te") -> dict:
+    """Translates text from source language to target language using the Bhashini API."""
+    url = 'https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/getModelsPipeline'
+    headers = {
+        "Content-Type": "application/json",
+        "userID": user_id,
+        "ulcaApiKey": api_key
+    }
+    payload = {
+        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}}}],
+        "pipelineRequestConfig": {"pipelineId": "64392f96daac500b55c543cd"}
+    }
+    response = requests.post(url, json=payload, headers=headers)
+    
+    if response.status_code != 200:
+        return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": None}
+
+    response_data = response.json()
+    service_id = response_data["pipelineResponseConfig"][0]["config"][0]["serviceId"]
+    callback_url = response_data["pipelineInferenceAPIEndPoint"]["callbackUrl"]
+    headers2 = {
+        "Content-Type": "application/json",
+        response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["name"]: response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["value"]
+    }
+    compute_payload = {
+        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}, "serviceId": service_id}}],
+        "inputData": {"input": [{"source": text}], "audio": [{"audioContent": None}]}
+    }
+    
+    compute_response = requests.post(callback_url, json=compute_payload, headers=headers2)
+    if compute_response.status_code != 200:
+        return {"status_code": compute_response.status_code, "message": "Error in translation", "translated_content": None}
+    
+    compute_response_data = compute_response.json()
+    translated_content = compute_response_data["pipelineResponse"][0]["output"][0]["target"]
+    
+    return {"status_code": 200, "message": "Translation successful", "translated_content": translated_content}
 
 
+# Existing chatbot functions
 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
+client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=HF_TOKEN)
 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")
+        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':
+    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)
+        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
+        documents = [item['content'] for item in documents_full]
         prompt = template.render(documents=documents, query=query)
         prompt_html = template_html.render(documents=documents, query=query)
     
@@ -138,40 +99,28 @@ def bot(history, cross_encoder):
         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')
+        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)
     
@@ -179,412 +128,436 @@ def bot(history, cross_encoder):
     
         history[-1][1] = ""
         for character in generate_fn(prompt, history[:-1]):
-            history[-1][1] = character            
+            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 translate_text(response_text, selected_language):
+    iso_language_codes = {
+        "Hindi": "hi",
+        "Gom": "gom",
+        "Kannada": "kn",
+        "Dogri": "doi",
+        "Bodo": "brx",
+        "Urdu": "ur",
+        "Tamil": "ta",
+        "Kashmiri": "ks",
+        "Assamese": "as",
+        "Bengali": "bn",
+        "Marathi": "mr",
+        "Sindhi": "sd",
+        "Maithili": "mai",
+        "Punjabi": "pa",
+        "Malayalam": "ml",
+        "Manipuri": "mni",
+        "Telugu": "te",
+        "Sanskrit": "sa",
+        "Nepali": "ne",
+        "Santali": "sat",
+        "Gujarati": "gu",
+        "Odia": "or"
+    }
     
+    to_code = iso_language_codes[selected_language]
+    translation = bhashini_translate(response_text, to_code=to_code)
+    return translation['translated_content']
 
-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:
+# Gradio interface
 with gr.Blocks(theme='NoCrypt/miku') as CHATBOT:
     with gr.Row():
         with gr.Column(scale=10):
-            # gr.Markdown(
-            #     """
-            #     # Theme preview: `paris`
-            #     To use this theme, set `theme='earneleh/paris'` in `gr.Blocks()` or `gr.Interface()`.
-            #     You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
-            #     of this theme.
-            #     """
-            # )
-            gr.HTML(value="""<div style="color: #FF4500;"><h1>ADWITIYA-</h1> <h1><span style="color: #008000">Custom Manual Chatbot and Quizbot</span></h1>
-            </div>""", elem_id='heading')
-        
-            gr.HTML(value=f"""
-            <p style="font-family: sans-serif; font-size: 16px;">
-              Using GenAI for CBIC Capacity Building - A free chat bot developed by National Customs Targeting Center  using Open source LLMs for CBIC Officers
-            </p>
-            """, elem_id='Sub-heading')
-            #usage_count = get_and_increment_value_count(db,collection_name, field_name)
-            gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;">Developed by NCTC,Mumbai  . Suggestions may be sent to <a href="mailto:nctc-admin@gov.in" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.</p>""", elem_id='Sub-heading1 ')
+            gr.HTML(value="""<div style="color: #FF4500;"><h1>ADWITIYA-</h1> <h1><span style="color: #008000">Custom Manual Chatbot and Quizbot</span></h1></div>""")
+            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>""")
+            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>""")
 
         with gr.Column(scale=3):
-            gr.Image(value='logo.png',height=200,width=200)
-
-    
-#     gr.HTML(value="""<div style="color: #FF4500;"><h1>CHEERFULL CBSE-</h1> <h1><span style="color: #008000">AI Assisted Fun Learning</span></h1>
-#     <img src='logo.png' alt="Chatbot" width="50" height="50" />
-#     </div>""", elem_id='heading')
-
-#     gr.HTML(value=f"""
-#     <p style="font-family: sans-serif; font-size: 16px;">
-#       A free Artificial Intelligence  Chatbot assistant trained on CBSE Class 10 Science Notes to engage and help students and teachers of Puducherry.
-#     </p>
-#     """, elem_id='Sub-heading')
-#     #usage_count = get_and_increment_value_count(db,collection_name, field_name)
-#     gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 16px;">Developed by K M Ramyasri , PGT . Suggestions may be sent to <a href="mailto:ramyadevi1607@yahoo.com" style="color: #00008B; font-style: italic;">ramyadevi1607@yahoo.com</a>.</p>""", elem_id='Sub-heading1 ')
-# #     count_html = gr.HTML(value=f"""
-# #     <div style="display: flex; justify-content: flex-end;">
-# #         <span style="font-weight: bold; color: maroon; font-size: 18px;">No of Usages:</span>
-# #         <span style="font-weight: bold; color: maroon; font-size: 18px;">{usage_count}</span>
-# #     </div>
-# # """)
-   
+            gr.Image(value='logo.png', height=200, width=200)
+
     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,
-            )
+        [],
+        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,
-                )
+            scale=3,
+            show_label=False,
+            placeholder="Enter text and press enter",
+            container=False,
+        )
         txt_btn = gr.Button(value="Submit text", scale=1)
-
-    cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2','(ACCURATE) BGE reranker','(HIGH ACCURATE) ColBERT'], value='(ACCURATE) BGE reranker',label="Embeddings", info="Only First query to Colbert may take litte time)")
-
+    
+    cross_encoder = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], value='(ACCURATE) BGE reranker', label="Embeddings", info="Only First query to Colbert may take little time)")
+    language_dropdown = gr.Dropdown(
+        choices=[
+            "Hindi", "Gom", "Kannada", "Dogri", "Bodo", "Urdu", "Tamil", "Kashmiri", "Assamese", "Bengali", "Marathi",
+            "Sindhi", "Maithili", "Punjabi", "Malayalam", "Manipuri", "Telugu", "Sanskrit", "Nepali", "Santali",
+            "Gujarati", "Odia"
+        ],
+        value="Hindi",  # default to Hindi
+        label="Select Language for Translation"
+    )
+    
     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])
+    translated_textbox = gr.Textbox(label="Translated Response")
 
-    # 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_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+        bot, [chatbot, cross_encoder], [chatbot, prompt_html]).then(
+        translate_text, [txt, language_dropdown], translated_textbox
+    )
+    
     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)
-
-
-
+        bot, [chatbot, cross_encoder], [chatbot, prompt_html]).then(
+        translate_text, [txt, language_dropdown], translated_textbox
+    )
 
-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>
-        """)
+# Launch the Gradio application
+CHATBOT.launch(share=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')
+
+
+# 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...')
     
-    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)
+#     # 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)
     
-    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_fn = generate_hf
     
-    generate_quiz_btn = gr.Button("Generate Quiz!🚀")
-    quiz_msg = gr.Textbox()
+#         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()
     
-    question_radios = [gr.Radio(visible=False) for _ in range(10)]
+#         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)
     
-    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()
+#         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}"
     
-    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()
+# # 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
 
-# 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")
+# # 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():
-#         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)]
+#         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 ')
 
-#     print(question_radios)
+#         with gr.Column(scale=3):
+#             gr.Image(value='logo.png',height=200,width=200)
 
-#     @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)
-    
-        
+  
+#     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,
+#             )
 
-#         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,
+#     with gr.Row():
+#         txt = gr.Textbox(
+#                 scale=3,
+#                 show_label=False,
+#                 placeholder="Enter text and press enter",
+#                 container=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)
+#         txt_btn = gr.Button(value="Submit text", scale=1)
 
-#         return ['Quiz Generated!']+ question_radio_list
+#     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)")
 
-#     check_button = gr.Button("Check Score")
+#     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])
 
-#     score_textbox = gr.Markdown()
+#     # Turn it back on
+#     txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
 
-#     @check_button.click(inputs=question_radios, outputs=score_textbox)
-#     def compare_answers(*user_answers):
-#         user_anwser_list = []
-#         user_anwser_list = user_answers
+#     # 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])
 
-#         answers_list = []
+#     # Turn it back on
+#     txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
 
-#         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)
+#     # Examples
+#     gr.Examples(examples, txt)
 
-#         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
 
+# # 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 = gr.TabbedInterface([CHATBOT, QUIZBOT], ["AI ChatBot", "AI Quizbot"])
+# CHATBOT.queue()
+# CHATBOT.launch(debug=True)
 
-# demo.queue()
-# demo.launch(debug=True)