Update app.py

app.py

@@ -156,7 +156,8 @@ def bot(history, cross_encoder):
         # Create a new history entry instead of modifying the tuple directly
         new_history = history[:-1] + [ (query, "") ]
         for character in generate_fn(prompt, history[:-1]):
-            new_history[-1] = (query, character)  # Update the last tuple with new text
+            new_history[-1] = (query, character) 
+            print('Character',character)# Update the last tuple with new text
             yield new_history, prompt_html
         # history[-1][1] = ""
         # for character in generate_fn(prompt, history[:-1]):
@@ -196,34 +197,7 @@ def translate_text(selected_language,history):
     response_text = history[-1][1] if history else ''
     translation = bhashini_translate(response_text, to_code=to_code)
     return translation['translated_content']
-    # 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']
+   
 
 # Gradio interface
 with gr.Blocks(theme='gradio/soft') as CHATBOT:
@@ -310,346 +284,3 @@ with gr.Blocks(theme='gradio/soft') as CHATBOT:
 # 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...')
-    
-#     # if COLBERT RAGATATOUILLE PROCEDURE  : 
-#     if cross_encoder=='(HIGH ACCURATE) ColBERT':
-#         gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait')
-#         RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-#         RAG_db=RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-#         documents_full=RAG_db.search(query,k=top_k_rank)
-        
-#         documents=[item['content'] for item in documents_full]
-#         # Create Prompt
-#         prompt = template.render(documents=documents, query=query)
-#         prompt_html = template_html.render(documents=documents, query=query)
-    
-#         generate_fn = generate_hf
-    
-#         history[-1][1] = ""
-#         for character in generate_fn(prompt, history[:-1]):
-#             history[-1][1] = character
-#             yield history, prompt_html
-#         print('Final history is ',history)
-#         #store_message(db,history[-1][0],history[-1][1],cross_encoder)
-#     else:
-#         # Retrieve documents relevant to query
-#         document_start = perf_counter()
-    
-#         query_vec = retriever.encode(query)
-#         logger.warning(f'Finished query vec')
-#         doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
-    
-        
-    
-#         logger.warning(f'Finished search')
-#         documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
-#         documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
-#         logger.warning(f'start cross encoder {len(documents)}')
-#         # Retrieve documents relevant to query
-#         query_doc_pair = [[query, doc] for doc in documents]
-#         if cross_encoder=='(FAST) MiniLM-L6v2' :
-#                cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') 
-#         elif cross_encoder=='(ACCURATE) BGE reranker':
-#                cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base')
-        
-#         cross_scores = cross_encoder1.predict(query_doc_pair)
-#         sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
-#         logger.warning(f'Finished cross encoder {len(documents)}')
-        
-#         documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
-#         logger.warning(f'num documents {len(documents)}')
-    
-#         document_time = perf_counter() - document_start
-#         logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')
-    
-#         # Create Prompt
-#         prompt = template.render(documents=documents, query=query)
-#         prompt_html = template_html.render(documents=documents, query=query)
-    
-#         generate_fn = generate_hf
-    
-#         history[-1][1] = ""
-#         for character in generate_fn(prompt, history[:-1]):
-#             history[-1][1] = character            
-#             yield history, prompt_html
-#         print('Final history is ',history)
-#         #store_message(db,history[-1][0],history[-1][1],cross_encoder)
-
-# # def system_instructions(question_difficulty, topic,documents_str):
-# #     return f"""<s> [INST] Your are a great teacher and your task is to create 10 questions with 4 choices with a {question_difficulty} difficulty  about topic request " {topic} " only from the below given documents, {documents_str} then create an answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". [/INST]"""
-
-# RAG_db = gr.State()
-
-# # def load_model():
-# #     try:
-# #         # Initialize the model
-# #         RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-# #         # Load the RAG database
-# #         RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-# #         return 'Ready to Go!!'
-# #     except Exception as e:
-# #         return f"Error loading model: {e}"
-    
-
-# # def generate_quiz(question_difficulty, topic):
-# #     if not topic.strip():
-# #         return ['Please enter a valid topic.'] + [gr.Radio(visible=False) for _ in range(10)]
-  
-# #     top_k_rank = 10
-# #     # Load the model and database within the generate_quiz function
-# #     try:
-# #         RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
-# #         RAG_db_ = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index')
-# #         gr.Warning('Model loaded!')
-# #     except Exception as e:
-# #         return [f"Error loading model: {e}"] + [gr.Radio(visible=False) for _ in range(10)]
-  
-# #     RAG_db_ = RAG_db.value
-# #     documents_full = RAG_db_.search(topic, k=top_k_rank)
-    
-# #     generate_kwargs = dict(
-# #         temperature=0.2,
-# #         max_new_tokens=4000,
-# #         top_p=0.95,
-# #         repetition_penalty=1.0,
-# #         do_sample=True,
-# #         seed=42,
-# #     )
-    
-# #     question_radio_list = []
-# #     count = 0
-# #     while count <= 3:
-# #         try:
-# #             documents = [item['content'] for item in documents_full]
-# #             document_summaries = [f"[DOCUMENT {i+1}]: {summary}{count}" for i, summary in enumerate(documents)]
-# #             documents_str = '\n'.join(document_summaries)
-# #             formatted_prompt = system_instructions(question_difficulty, topic, documents_str)
-            
-# #             pre_prompt = [
-# #                 {"role": "system", "content": formatted_prompt}
-# #             ]
-# #             response = client.text_generation(
-# #                 formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False,
-# #             )
-# #             output_json = json.loads(f"{response}")
-            
-# #             global quiz_data
-# #             quiz_data = output_json
-            
-# #             for question_num in range(1, 11):
-# #                 question_key = f"Q{question_num}"
-# #                 answer_key = f"A{question_num}"
-# #                 question = quiz_data.get(question_key)
-# #                 answer = quiz_data.get(quiz_data.get(answer_key))
-                
-# #                 if not question or not answer:
-# #                     continue
-                
-# #                 choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)]
-# #                 choice_list = [quiz_data.get(choice_key, "Choice not found") for choice_key in choice_keys]
-                
-# #                 radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True)
-# #                 question_radio_list.append(radio)
-            
-# #             if len(question_radio_list) == 10:
-# #                 break
-# #             else:
-# #                 count += 1
-# #                 continue
-# #         except Exception as e:
-# #             count += 1
-# #             if count == 3:
-# #                 return ['Sorry. Pls try with another topic!'] + [gr.Radio(visible=False) for _ in range(10)]
-# #             continue
-    
-# #     return ['Quiz Generated!'] + question_radio_list
-
-# # def compare_answers(*user_answers):
-# #     user_answer_list = user_answers
-# #     answers_list = [quiz_data.get(quiz_data.get(f"A{question_num}")) for question_num in range(1, 11)]
-    
-# #     score = sum(1 for answer in user_answer_list if answer in answers_list)
-    
-# #     if score > 7:
-# #         message = f"### Excellent! You got {score} out of 10!"
-# #     elif score > 5:
-# #         message = f"### Good! You got {score} out of 10!"
-# #     else:
-# #         message = f"### You got {score} out of 10! Don’t worry, you can prepare well and try better next time!"
-    
-# #     return message
-
-# #with gr.Blocks(theme='Insuz/SimpleIndigo') as demo:
-# with gr.Blocks(theme='NoCrypt/miku') as CHATBOT:
-#     with gr.Row():
-#         with gr.Column(scale=10):
-#             # gr.Markdown(
-#             #     """
-#             #     # Theme preview: `paris`
-#             #     To use this theme, set `theme='earneleh/paris'` in `gr.Blocks()` or `gr.Interface()`.
-#             #     You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
-#             #     of this theme.
-#             #     """
-#             # )
-#             gr.HTML(value="""<div style="color: #FF4500;"><h1>ADWITIYA-</h1> <h1><span style="color: #008000">Custom Manual Chatbot and Quizbot</span></h1>
-#             </div>""", elem_id='heading')
-        
-#             gr.HTML(value=f"""
-#             <p style="font-family: sans-serif; font-size: 16px;">
-#               Using GenAI for CBIC Capacity Building - A free chat bot developed by National Customs Targeting Center  using Open source LLMs for CBIC Officers
-#             </p>
-#             """, elem_id='Sub-heading')
-#             #usage_count = get_and_increment_value_count(db,collection_name, field_name)
-#             gr.HTML(value=f"""<p style="font-family: Arial, sans-serif; font-size: 14px;">Developed by NCTC,Mumbai  . Suggestions may be sent to <a href="mailto:[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)
-
-  
-#     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)
-
-
-
-
-# # 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"])
-# CHATBOT.queue()
-# CHATBOT.launch(debug=True)
-