app.py

import gradio as gr
import speech_recognition as sr
from huggingface_hub import InferenceClient
import random
import textwrap
import pyttsx3

# Initialize the speech recognition and TTS engine
recognizer = sr.Recognizer()
tts_engine = pyttsx3.init()

# Define the model to be used
model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
client = InferenceClient(model)

# Embedded system prompt
system_prompt_text = (
    "You are a smart and helpful co-worker of Thailand based multi-national company PTT, "
    "and PTTEP. You help with any kind of request and provide a detailed answer to the question. "
    "But if you are asked about something unethical or dangerous, you must refuse and provide a safe and respectful way to handle that."
)

# Read the content of the info.md file with UTF-8 encoding
with open("info.md", "r", encoding="utf-8") as file:
    info_md_content = file.read()

# Chunk the info.md content into smaller sections
chunk_size = 2500  # Adjust this size as needed
info_md_chunks = textwrap.wrap(info_md_content, chunk_size)

def get_all_chunks(chunks):
    return "\n\n".join(chunks)

def format_prompt_mixtral(message, history, info_md_chunks):
    prompt = "<s>"
    all_chunks = get_all_chunks(info_md_chunks)
    prompt += f"{all_chunks}\n\n"  # Add all chunks of info.md at the beginning
    prompt += f"{system_prompt_text}\n\n"  # Add the system prompt

    if history:
        for user_prompt, bot_response in history:
            prompt += f"[INST] {user_prompt} [/INST]"
            prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def chat_inf(prompt, history, seed, temp, tokens, top_p, rep_p):
    generate_kwargs = dict(
        temperature=temp,
        max_new_tokens=tokens,
        top_p=top_p,
        repetition_penalty=rep_p,
        do_sample=True,
        seed=seed,
    )

    formatted_prompt = format_prompt_mixtral(prompt, history, info_md_chunks)
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""
    for response in stream:
        output += response.token.text
        yield [(prompt, output)]
    history.append((prompt, output))
    yield history

def clear_fn():
    return None, None

rand_val = random.randint(1, 1111111111111111)

def check_rand(inp, val):
    if inp:
        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
    else:
        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))

def recognize_speech(audio):
    with sr.AudioFile(audio) as source:
        audio_data = recognizer.record(source)  # Record the audio
        try:
            # Recognize the speech using Google's API
            text = recognizer.recognize_google(audio_data)
            return text
        except sr.UnknownValueError:
            return "Sorry, I could not understand the audio."
        except sr.RequestError:
            return "Error: Could not request results from the speech recognition service."

def speak_text(text):
    # Convert text to speech using pyttsx3
    tts_engine.save_to_file(text, 'output.mp3')  # Save the TTS audio
    tts_engine.runAndWait()  # Wait until TTS is done

with gr.Blocks() as app:  
    gr.HTML("""<center><h1 style='font-size:xx-large;'>PTT Chatbot</h1><br><h3>running on Huggingface Inference</h3><br><h7>EXPERIMENTAL</center>""")
    
    with gr.Row():
        chat = gr.Chatbot(height=500)
    
    with gr.Group():
        with gr.Row():
            with gr.Column(scale=3):
                inp = gr.Audio(type="filepath")  # Audio input
                with gr.Row():
                    with gr.Column(scale=2):
                        btn = gr.Button("Chat")
                    with gr.Column(scale=1):
                        with gr.Group():
                            stop_btn = gr.Button("Stop")
                            clear_btn = gr.Button("Clear")
            with gr.Column(scale=1):
                with gr.Group():
                    rand = gr.Checkbox(label="Random Seed", value=True)
                    seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
                    tokens = gr.Slider(label="Max new tokens", value=3840, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens")
                    temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
                    top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
                    rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)

    hid1 = gr.Number(value=1, visible=False)

    output_audio = gr.Audio(label="Output Audio", type="filepath", interactive=False)  # Create an output audio component

    def handle_chat(audio_input, chat_history, seed, temp, tokens, top_p, rep_p):
        user_message = recognize_speech(audio_input)  # Recognize speech input
        if "Sorry" in user_message:  # Check for error in recognition
            return chat_history, user_message, None
        response_gen = chat_inf(user_message, chat_history, seed, temp, tokens, top_p, rep_p)
        response = next(response_gen)[0][-1][1]  # Get the response text
        speak_text(response)  # Speak the response text
        return chat_history + [(user_message, response)], response, 'output.mp3'  # Return the filename for audio output

    go = btn.click(handle_chat, [inp, chat, seed, temp, tokens, top_p, rep_p], [chat, inp, output_audio])  # Use output_audio instead of "output.mp3"

    stop_btn.click(None, None, None, cancels=[go])
    clear_btn.click(clear_fn, None, [inp, chat])

app.queue(default_concurrency_limit=10).launch(share=True, auth=("admin", "0112358"))