app.py

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)