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Expenditure_observer_bot / app.py

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	from ragatouille import RAGPretrainedModel
	import subprocess
	import json
	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 backend.query_llm import generate_hf, generate_openai
	from backend.semantic_search import table, retriever

	VECTOR_COLUMN_NAME = "vector"
	TEXT_COLUMN_NAME = "text"

	proj_dir = Path(__file__).parent
	# Setting up the logging
	logging.basicConfig(level=logging.INFO)
	logger = logging.getLogger(__name__)

	# 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()

	# Examples
	examples = ['when i have to report to constituency?','what is social media and what are rules related to it for expenditure monitoring ',
	'how many reports to be submitted by Expenditure observer with annexure names ?','what is expenditure limits for parlimentary constituency and assembly constituency'
	]
	#db usage
	collection_name = 'Nirvachana' # Replace with your collection name
	field_name = 'message_count' # Replace with your field name for count


	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 = 15
	top_k_rank = 10
	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/mockingbird')
	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
	print('Final history is ',history)
	yield history, prompt_html
	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=='(FAIRLY 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
	print('Final history is ',history)
	yield history, prompt_html
	store_message(db,history[-1][0],history[-1][1],cross_encoder)


	with gr.Blocks(theme='Insuz/SimpleIndigo') as demo:
	gr.HTML(value="""<div style="display: flex; align-items: center; justify-content: space-between;">
	<h1 style="color: #008000">NIRVACHANA - <span style="color: #008000">Expenditure Observer AI Assistant</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 chat bot assistant for Expenditure Observers on Compendium on Election Expenditure Monitoring using Open source LLMs. <br>
	The bot can answer questions in natural language, taking relevant extracts from the ECI document which can be accessed <a href="https://www.eci.gov.in/eci-backend/public/api/download?url=LMAhAK6sOPBp%2FNFF0iRfXbEB1EVSLT41NNLRjYNJJP1KivrUxbfqkDatmHy12e%2Fzk1vx4ptJpQsKYHA87guoLjnPUWtHeZgKtEqs%2FyzfTTYIC0newOHHOjl1rl0u3mJBSIq%2Fi7zDsrcP74v%2FKr8UNw%3D%3D" style="color: #00008B; text-decoration: none;">CLICK HERE !</a>.
	</p>
	<p style="font-family: sans-serif; font-size: 14px; color: #808080;">
	Disclaimer: This is an independent initiative and AI responses are guidance in nature. It is advised to reconfirm with the latest ECI circulars/instructions.
	</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 Ramesh M IRS (C& CE) (R-19187), Suggestions may be sent to <a href="mailto:mramesh.irs@gov.in" style="color: #00008B; font-style: italic;">mramesh.irs@gov.in</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','(FAIRLY ACCURATE) BGE reranker','(HIGH ACCURATE) ColBERT'], value='(FAIRLY 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)

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