app.py

import time
import gradio as gr
import logging
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain.docstore.document import Document
import whisper_app
import llm_ops

FILE_EXT = ['wav','mp3']
MAX_NEW_TOKENS = 4096
DEFAULT_MAX_NEW_TOKENS = 1024
DEFAULT_TEMPERATURE = 0.1
DEFAULT_DURATION = 5

def create_logger():
    formatter = logging.Formatter('%(asctime)s:%(levelname)s:- %(message)s')
    console_handler = logging.StreamHandler()
    console_handler.setLevel(logging.INFO)
    console_handler.setFormatter(formatter)

    logger = logging.getLogger("APT_Realignment")
    logger.setLevel(logging.INFO)

    if not logger.hasHandlers():
        logger.addHandler(console_handler)
    logger.propagate = False
    return logger


def clear_chat():
    return []

def create_prompt():
    prompt_template = """Asnwer the questions regarding the content in the Audio . 
    Use the following context to answer. 
    If you don't know the answer, just say I don't know. 

    {context}

    Question: {question}
    Answer :"""
    prompt = PromptTemplate(
        template=prompt_template, input_variables=["context", "question"]
    )
    return prompt


logger = create_logger()

def process_documents(documents,data_chunk=1500,chunk_overlap=100):
    text_splitter = CharacterTextSplitter(chunk_size=data_chunk, chunk_overlap=chunk_overlap,separator='\n')
    texts = text_splitter.split_documents(documents)
    return texts

def audio_processor(wav_file,API_key,wav_model='small',llm='HuggingFace',temperature=0.1,max_tokens=4096,duration=5):
    device='cpu'
    logger.info("Audio File Name :",wav_file.name)
    
    whisper = whisper_app.WHISPERModel(model_name=wav_model,device=device)
    logger.info("Whisper Model Loaded || Model size:{}".format(wav_model))
    text_info = whisper.speech_to_text(audio_path=wav_file.name)
    
    metadata = {"source": f"{wav_file}","duration":text_info['duration'],"language":text_info['language']}
    document = [Document(page_content=text_info['text'], metadata=metadata)]
    
    logger.info("Document",document)
    logging.info("Loading General Text Embeddings (GTE) model{}".format('thenlper/gte-large'))
    
    embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large',model_kwargs={"device": device})
    texts = process_documents(documents=document)
    
    global vector_db
    vector_db = FAISS.from_documents(documents=texts, embedding= embedding_model)
    global qa

    if llm == 'HuggingFace':
        chat = llm_ops.get_hugging_face_model(
                            model_id="VMware/open-llama-7B-open-instruct",
                            API_key=API_key,
                            temperature=temperature,
                            max_tokens=max_tokens
                            )
    else:
        chat = llm_ops.get_openai_chat_model(API_key=API_key)
    
    chain_type_kwargs = {"prompt": create_prompt()}
    qa = RetrievalQA.from_chain_type(llm=chat,
                                chain_type='stuff',
                                retriever=vector_db.as_retriever(),
                                chain_type_kwargs=chain_type_kwargs,
                                return_source_documents=True
                            )
    return "Audio Processing completed ..."

def infer(question, history):
    # res = []
    # for human, ai in history[:-1]:
    #     pair = (human, ai)
    #     res.append(pair)
    
    # chat_history = res

    result = qa({"query": question})
    matching_docs_score = vector_db.similarity_search_with_score(question)
    logger.info("Matching Score :",matching_docs_score)
    return result["result"]

def bot(history):
    response = infer(history[-1][0], history)
    history[-1][1] = ""
    
    for character in response:     
        history[-1][1] += character
        time.sleep(0.05)
        yield history

def add_text(history, text):
    history = history + [(text, None)]
    return history, ""


def loading_file():
    return "Loading..."


css="""
#col-container {max-width: 2048px; margin-left: auto; margin-right: auto;}
"""

title = """
<div style="text-align: center;max-width: 2048px;">
    <h1>Q&A using LLAMA on Audio files</h1>
    <p style="text-align: center;">Upload a Audio file/link and  query  LLAMA-chatbot.
    <i> Tools uses State of the Art Models from  HuggingFace/OpenAI so, make sure to add your key.</i>
    </p>
</div>
"""
with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column(elem_id="col-container"):
            gr.HTML(title)
    
    with gr.Column():
        with gr.Row():
            LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='Select HuggingFace/OpenAI')
            API_key = gr.Textbox(label="Add API key", type="password",autofocus=True)
            wav_model = gr.Dropdown(['small','medium','large'],label='Select Whisper model')
    
    with gr.Group():    
        chatbot = gr.Chatbot(height=270)
    
    with gr.Row():
        question = gr.Textbox(label="Type your question !",lines=1,interactive=True)
    
    with gr.Row():
        submit_btn = gr.Button(value="Send message", variant="primary", scale = 1)
        clean_chat_btn =  gr.Button("Delete Chat")
    with gr.Column():
        with gr.Box():
            audio_file = gr.File(label="Upload Audio File ", file_types=FILE_EXT, type="file")
            with gr.Accordion(label='Advanced options', open=False):
                    max_new_tokens = gr.Slider(
                        label='Max new tokens',
                        minimum=2048,
                        maximum=MAX_NEW_TOKENS,
                        step=1,
                        value=DEFAULT_MAX_NEW_TOKENS,
                        )
                    duration = gr.Slider(label='duration in min',minimum=5,maximum = 10,step=1,value=DEFAULT_DURATION)
                    temperature = gr.Slider(
                    label='Temperature',
                    minimum=0.1,
                    maximum=4.0,
                    step=0.1,
                    value=DEFAULT_TEMPERATURE,
                    )
            with gr.Row():
                    langchain_status = gr.Textbox(label="Status", placeholder="", interactive = False)
                    load_audio = gr.Button("Upload Audio File")
    if audio_file:
        load_audio.click(loading_file, None, langchain_status, queue=False)    
        load_audio.click(audio_processor, inputs=[audio_file,API_key,wav_model,LLM_option,temperature,max_new_tokens], outputs=[langchain_status], queue=False)
    
    clean_chat_btn.click(clear_chat, [], chatbot)
    question.submit(add_text, inputs=[chatbot, question], outputs=[chatbot, question]).then(bot, chatbot, chatbot)
    submit_btn.click(add_text, inputs=[chatbot, question], outputs=[chatbot, question]).then(bot, chatbot, chatbot)

demo.launch()