app.py

"""
Credit to Derek Thomas, [email protected]
"""
from ragatouille import RAGPretrainedModel
import subprocess

import logging
from pathlib import Path
from time import perf_counter

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')

# crossEncoder
#cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') 
#cross_encoder = CrossEncoder('BAAI/bge-reranker-base')
# Examples
examples = ['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'
            ]


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=='ColBERT':
        gr.Warning('Retrieving using ColBERT')
        RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
        RAG_db=RAG.from_index('.ragatouille/colbert/indexes/mockingbird')
        documents_full=RAG_db.search(query)
        
        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
    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=='MiniLM-L6v2' :
               cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') 
        elif cross_encoder=='BGE reranker':
               cross_encoder = CrossEncoder('BAAI/bge-reranker-base')
        
        cross_scores = cross_encoder.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


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="""<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: #008000; text-decoration: none;">here</a>.</p>""",elem_id='Sub-heading')
    

    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=['MiniLM-L6v2','BGE reranker','ColBERT'], value='BGE reranker',label="Embeddings", info="Choose MiniLM for Speed, BGE reranker for accuracy,ColBERT for both")

    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])

    # 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])

    # 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)