app.py

import gradio as gr
import edge_tts
import asyncio
import tempfile
import numpy as np
import soxr
from pydub import AudioSegment
import torch
import sentencepiece as spm
import onnxruntime as ort
from huggingface_hub import hf_hub_download, InferenceClient
import requests
from bs4 import BeautifulSoup
import urllib
import random
import re

# List of user agents to choose from for requests
_useragent_list = [
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:66.0) Gecko/20100101 Firefox/66.0',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',
    'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36 Edg/111.0.1661.62',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0'
]

def get_useragent():
    """Returns a random user agent from the list."""
    return random.choice(_useragent_list)

def extract_text_from_webpage(html_content):
    """Extracts visible text from HTML content using BeautifulSoup."""
    soup = BeautifulSoup(html_content, "html.parser")
    # Remove unwanted tags
    for tag in soup(["script", "style", "header", "footer", "nav"]):
        tag.extract()
    # Get the remaining visible text
    visible_text = soup.get_text(strip=True)
    visible_text = visible_text[:8000]
    return visible_text

def search(term, num_results=2, timeout=5, ssl_verify=None):
    """Performs a Google search and returns the results."""
    escaped_term = urllib.parse.quote_plus(term)
    all_results = []
    resp = requests.get(
            url="https://www.google.com/search",
            headers={"User-Agent": get_useragent()}, # Set random user agent
            params={
                "q": term,
                "num": num_results,
                "udm": 14,
            },
            timeout=timeout,
            verify=ssl_verify,
        )
    resp.raise_for_status() # Raise an exception if request fails
    soup = BeautifulSoup(resp.text, "html.parser")
    result_block = soup.find_all("div", attrs={"class": "g"})
    for result in result_block:
        link = result.find("a", href=True)
        if link:
            link = link["href"]
            try:
                    # Fetch webpage content
                webpage = requests.get(link, headers={"User-Agent": get_useragent()})
                webpage.raise_for_status()
                    # Extract visible text from webpage
                visible_text = extract_text_from_webpage(webpage.text)
                all_results.append({"link": link, "text": visible_text})
            except requests.exceptions.RequestException as e:
                print(f"Error fetching or processing {link}: {e}")
                all_results.append({"link": link, "text": None})
        else:
            all_results.append({"link": None, "text": None})
    print(all_results)
    return all_results

# Speech Recognition Model Configuration
model_name = "neongeckocom/stt_en_citrinet_512_gamma_0_25"
sample_rate = 16000

# Download preprocessor, encoder and tokenizer
preprocessor = torch.jit.load(hf_hub_download(model_name, "preprocessor.ts", subfolder="onnx"))
encoder = ort.InferenceSession(hf_hub_download(model_name, "model.onnx", subfolder="onnx"))
tokenizer = spm.SentencePieceProcessor(hf_hub_download(model_name, "tokenizer.spm", subfolder="onnx"))

# Mistral Model Configuration
client1 = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
system_instructions1 = "<s>[SYSTEM] Answer as OpenGPT 4o, Made by 'KingNish', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses. The expectation is that I will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"

def resample(audio_fp32, sr):
    return soxr.resample(audio_fp32, sr, sample_rate)

def to_float32(audio_buffer):
    return np.divide(audio_buffer, np.iinfo(audio_buffer.dtype).max, dtype=np.float32)

def transcribe(audio_path):
    audio_file = AudioSegment.from_file(audio_path)
    sr = audio_file.frame_rate
    audio_buffer = np.array(audio_file.get_array_of_samples())

    audio_fp32 = to_float32(audio_buffer)
    audio_16k = resample(audio_fp32, sr)

    input_signal = torch.tensor(audio_16k).unsqueeze(0)
    length = torch.tensor(len(audio_16k)).unsqueeze(0)
    processed_signal, _ = preprocessor.forward(input_signal=input_signal, length=length)
    
    logits = encoder.run(None, {'audio_signal': processed_signal.numpy(), 'length': length.numpy()})[0][0]

    blank_id = tokenizer.vocab_size()
    decoded_prediction = [p for p in logits.argmax(axis=1).tolist() if p != blank_id]
    text = tokenizer.decode_ids(decoded_prediction)

    return text

def model(text, web_search, max_tokens, temperature):
    if web_search is True:
        """Performs a web search, feeds the results to a language model, and returns the answer."""
        web_results = search(text)
        web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
        formatted_prompt = system_instructions1 + text + "[WEB]" + str(web2) + "[OpenGPT 4o]"
        stream = client1.text_generation(formatted_prompt, max_new_tokens=max_tokens, temperature=temperature, stream=True, details=True, return_full_text=False)
        return "".join([response.token.text for response in stream if response.token.text != "</s>"])
    else:
        formatted_prompt = system_instructions1 + text + "[OpenGPT 4o]"
        stream = client1.text_generation(formatted_prompt, max_new_tokens=max_tokens, temperature=temperature, stream=True, details=True, return_full_text=False)
        return "".join([response.token.text for response in stream if response.token.text != "</s>"])

async def respond(audio, web_search, voice, max_tokens, temperature):
    user = transcribe(audio)
    reply = model(user, web_search, max_tokens, temperature)
    communicate = edge_tts.Communicate(reply, voice=voice)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    return tmp_path, user, reply

# List of available voices for edge_tts
voices = ["en-US-JennyNeural", "en-US-GuyNeural", "en-GB-SoniaNeural", "en-AU-NatashaNeural"]

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🎙️ **OpenGPT 4o - Advanced Voice Assistant**")
    with gr.Tabs():
        with gr.TabItem("Conversation"):
            with gr.Row():
                with gr.Column():
                    audio_input = gr.Audio(label="🎤 Speak or Upload Audio", sources="microphone", type="filepath")
                    web_search = gr.Checkbox(label="🔍 Enable Web Search", value=False)
                    voice = gr.Dropdown(label="🎤 Choose Voice", choices=voices, value="en-US-JennyNeural")
                    max_tokens = gr.Slider(minimum=50, maximum=500, value=300, label="Max Tokens")
                    temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature")
                with gr.Column():
                    audio_output = gr.Audio(label="🤖 AI Response", autoplay=True)
                    user_text = gr.Textbox(label="👤 You Said", interactive=False)
                    ai_text = gr.Textbox(label="🤖 AI Response", interactive=False)
        
        with gr.TabItem("History"):
            history = gr.Dataframe(headers=["User Input", "AI Response"], interactive=False)
        
        with gr.TabItem("Settings"):
            gr.Markdown("### ⚙️ Settings")
            gr.Markdown("Adjust the parameters to customize the AI's behavior.")

    # Store conversation history
    conversation_history = []

    def update_history(user_input, ai_response):
        conversation_history.append([user_input, ai_response])
        return conversation_history

    # Automatically submit when audio is detected
    audio_input.change(
        fn=respond,
        inputs=[audio_input, web_search, voice, max_tokens, temperature],
        outputs=[audio_output, user_text, ai_text]
    ).then(
        fn=update_history,
        inputs=[user_text, ai_text],
        outputs=history
    )

if __name__ == "__main__":
    demo.queue(max_size=200).launch()