app.py

"""
Credit to Derek Thomas, derek@huggingface.co
"""

import subprocess

# subprocess.run(["pip", "install", "--upgrade", "transformers[torch,sentencepiece]==4.34.1"])

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,generate_gemini
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 = ['My transhipment cargo is missing',
            '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 add_text(history, text):
    history = [] if history is None else history
    history = history + [(text, None)]
    print('add_text function done..returning history' ,history)
    return history, gr.Textbox(value="", interactive=False)


def bot(history, api_kind):
    top_rerank = 15
    top_k_rank = 8
    query = history[-1][0]
    print('history[-1][0]',history[-1][0])
    print('api kind ',api_kind)

    if not query:
         gr.Warning("Please submit a non-empty string as a prompt")
         raise ValueError("Empty string was submitted")

    logger.warning('Retrieving documents...')
    # Retrieve documents relevant to query
    document_start = perf_counter()

    query_vec = retriever.encode(query)
    print(query)
    query_vec_flat = [arr.flatten() for arr in query_vec]
    logger.warning(f'Finished query vec')
    #documents = table.search(query_vec_flat, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()



    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]
    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)

    if api_kind == "HuggingFace":
         generate_fn = generate_hf
    elif api_kind == "Gemini":
         print("Gemini condition satisfied")
         generate_fn = generate_gemini
    elif api_kind is None:
         gr.Warning("API name was not provided")
         raise ValueError("API name was not provided")
    else:
         gr.Warning(f"API {api_kind} is not supported")
         raise ValueError(f"API {api_kind} is not supported")
    try:
        count_tokens = lambda text: len([token.strip() for token in text.split() if token.strip()])
        print(prompt_html,'token count is',count_tokens(prompt_html))
        history[-1][1] = ""
        for character in generate_fn(prompt, history[:-1]):
            history[-1][1] = character
            yield history, prompt_html
        print('final history is ',history)
        # return history[-1][1], prompt_html
           
    except Exception as e:  # Catch any exception
        print('An unexpected error occurred during generation:', str(e))
        yield f"An unexpected error occurred during generation: {str(e)}"

with gr.Blocks(theme='WeixuanYuan/Soft_dark') as demo:
    # Beautiful heading with logo
    gr.HTML(value="""
    <div style="display: flex; align-items: center; justify-content: space-between;">
      <h1 style="color: #008000">ADWITIYA - <span style="color: #008000">Customs Manual Chatbot</span></h1>
      <img src='logo.png' alt="Chatbot" width="50" height="50" />
    </div>
    """, elem_id="heading")

    # Formatted description
    gr.HTML(value="""<p style="font-family: sans-serif; font-size: 16px;">A free chat bot developed by National Customs   		Targeting Center  using Open source LLMs.(Dedicated to 75th Batch IRS Probationers)</p>""", elem_id="description")
    
    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)

    api_kind = gr.Radio(choices=["HuggingFace","Gemini"], value="HuggingFace")

    #prompt_html = gr.HTML()
    prompt_html = gr.Textbox(label='Retrieved Documents')
    try:
        # Turn off interactivity while generating if you click
        txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
                bot, [chatbot, api_kind], [chatbot, prompt_html])
    except Exception as e:
        print ('Exception  txt btn click ' ,str(e))
    # Turn it back on
    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
    try:
        # Turn off interactivity while generating if you hit enter
        txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
                bot, [chatbot, api_kind], [chatbot, prompt_html])
    except Exception as e:
        print ('Exception  ' ,str(e))

    # 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)