app.py

import requests
import gradio as gr
from ragatouille import RAGPretrainedModel
import logging
from pathlib import Path
from time import perf_counter
from sentence_transformers import CrossEncoder
from huggingface_hub import InferenceClient
from jinja2 import Environment, FileSystemLoader
import numpy as np
from os import getenv
from backend.query_llm import generate_hf, generate_qwen
from backend.semantic_search import table, retriever
from huggingface_hub import InferenceClient


# Bhashini API translation function
api_key = getenv('API_KEY')
user_id = getenv('USER_ID')

def bhashini_translate(text: str, from_code: str = "en", to_code: str = "hi") -> dict:
    """Translates text from source language to target language using the Bhashini API."""
    
    if not text.strip():
        print('Input text is empty. Please provide valid text for translation.')
        return {"status_code": 400, "message": "Input text is empty", "translated_content": None, "speech_content": None}
    else:
        print('Input text - ',text)
    print(f'Starting translation process from {from_code} to {to_code}...')
    print(f'Starting translation process from {from_code} to {to_code}...')
    gr.Warning(f'Translating to {to_code}...')
    
    url = 'https://meity-auth.ulcacontrib.org/ulca/apis/v0/model/getModelsPipeline'
    headers = {
        "Content-Type": "application/json",
        "userID": user_id,
        "ulcaApiKey": api_key
    }
    payload = {
        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}}}],
        "pipelineRequestConfig": {"pipelineId": "64392f96daac500b55c543cd"}
    }
    
    print('Sending initial request to get the pipeline...')
    response = requests.post(url, json=payload, headers=headers)
    
    if response.status_code != 200:
        print(f'Error in initial request: {response.status_code}')
        return {"status_code": response.status_code, "message": "Error in translation request", "translated_content": None}

    print('Initial request successful, processing response...')
    response_data = response.json()
    service_id = response_data["pipelineResponseConfig"][0]["config"][0]["serviceId"]
    callback_url = response_data["pipelineInferenceAPIEndPoint"]["callbackUrl"]
    
    print(f'Service ID: {service_id}, Callback URL: {callback_url}')
    
    headers2 = {
        "Content-Type": "application/json",
        response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["name"]: response_data["pipelineInferenceAPIEndPoint"]["inferenceApiKey"]["value"]
    }
    compute_payload = {
        "pipelineTasks": [{"taskType": "translation", "config": {"language": {"sourceLanguage": from_code, "targetLanguage": to_code}, "serviceId": service_id}}],
        "inputData": {"input": [{"source": text}], "audio": [{"audioContent": None}]}
    }
    
    print(f'Sending translation request with text: "{text}"')
    compute_response = requests.post(callback_url, json=compute_payload, headers=headers2)
    
    if compute_response.status_code != 200:
        print(f'Error in translation request: {compute_response.status_code}')
        return {"status_code": compute_response.status_code, "message": "Error in translation", "translated_content": None}
    
    print('Translation request successful, processing translation...')
    compute_response_data = compute_response.json()
    translated_content = compute_response_data["pipelineResponse"][0]["output"][0]["target"]
    
    print(f'Translation successful. Translated content: "{translated_content}"')
    return {"status_code": 200, "message": "Translation successful", "translated_content": translated_content}


# Existing chatbot functions
VECTOR_COLUMN_NAME = "vector"
TEXT_COLUMN_NAME = "text"
HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN")
proj_dir = Path(__file__).parent

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1", token=HF_TOKEN)
env = Environment(loader=FileSystemLoader(proj_dir / 'templates'))

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 history else ''
    print('\nQuery: ',query )
    print('\nHistory:',history)
    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 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]
        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
    else:
        document_start = perf_counter()
    
        query_vec = retriever.encode(query)
        doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank)
    
        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]
    
        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)))
        
        documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
    
        document_time = perf_counter() - document_start
    
        prompt = template.render(documents=documents, query=query)
        prompt_html = template_html.render(documents=documents, query=query)
    
        #generate_fn = generate_hf
        generate_fn=generate_qwen
        # Create a new history entry instead of modifying the tuple directly
        new_history = history[:-1] + [ (prompt, "") ] # query replaced prompt
        output=''
        # for character in generate_fn(prompt, history[:-1]):
        #     #new_history[-1] = (query, character) 
        #     output+=character
        output=generate_fn(prompt, history[:-1])
        
        print('Output:',output)
        new_history[-1] = (prompt, output) #query replaced with prompt
        print('New History',new_history)
        #print('prompt html',prompt_html)# Update the last tuple with new text
        
        history_list = list(history[-1])
        history_list[1] = output  # Assuming `character` is what you want to assign
        # Update the history with the modified list converted back to a tuple
        history[-1] = tuple(history_list)

            #history[-1][1] = character
        # yield new_history, prompt_html
        yield history, prompt_html
         # new_history,prompt_html
        # history[-1][1] = ""
        # for character in generate_fn(prompt, history[:-1]):
        #     history[-1][1] = character
        #     yield history, prompt_html

#def translate_text(response_text, selected_language):
    
def translate_text(selected_language,history):
    
    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]
    response_text = history[-1][1] if history else ''
    print('response_text for translation',response_text)
    translation = bhashini_translate(response_text, to_code=to_code)
    return translation['translated_content']
   

# Gradio interface
with gr.Blocks(theme='gradio/soft') as CHATBOT:
    history_state = gr.State([])
    with gr.Row():
        with gr.Column(scale=10):
            gr.HTML(value="""<div style="color: #FF4500;"><h1>m-</h1>MITHRA<h1><span style="color: #008000">student Manual Chatbot </span></h1></div>""")
            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>""")
            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:nctc-admin@gov.in" style="color: #00008B; font-style: italic;">nctc-admin@gov.in</a>.</p>""")

        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 little time)")
    language_dropdown = gr.Dropdown(
        choices=[
            "Hindi", "Gom", "Kannada", "Dogri", "Bodo", "Urdu", "Tamil", "Kashmiri", "Assamese", "Bengali", "Marathi",
            "Sindhi", "Maithili", "Punjabi", "Malayalam", "Manipuri", "Telugu", "Sanskrit", "Nepali", "Santali",
            "Gujarati", "Odia"
        ],
        value="Hindi",  # default to Hindi
        label="Select Language for Translation"
    )
    
    prompt_html = gr.HTML()
    
    translated_textbox = gr.Textbox(label="Translated Response")
    def update_history_and_translate(txt, cross_encoder, history_state, language_dropdown):
        print('History state',history_state)
        history = history_state
        history.append((txt, ""))
        #history_state.value=(history)
        
        # Call bot function
        # bot_output = list(bot(history, cross_encoder))
        bot_output = next(bot(history, cross_encoder))
        print('bot_output',bot_output)
        #history, prompt_html = bot_output[-1]
        history, prompt_html = bot_output
        print('History',history)
        # Update the history state
        history_state[:] = history
        
        # Translate text
        translated_text = translate_text(language_dropdown, history)
        return history, prompt_html, translated_text

    txt_msg = txt_btn.click(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox])
    txt_msg = txt.submit(update_history_and_translate, [txt, cross_encoder, history_state, language_dropdown], [chatbot, prompt_html, translated_textbox])

    examples = ['CAN U SAY THE DIFFERENCES BETWEEN METALS AND NON METALS?','WHAT IS IONIC BOND?',
            'EXPLAIN ASEXUAL REPRODUCTION']            

    gr.Examples(examples, txt)


# Launch the Gradio application
CHATBOT.launch(share=True,debug=True)