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
from functools import lru_cache
import concurrent.futures

# Configuration for concurrency
MAX_WORKERS = 4  # Adjust based on your system resources
executor = concurrent.futures.ThreadPoolExecutor(max_workers=MAX_WORKERS)

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)
    return visible_text

# Perform a Google search and return the results
def search(term, num_results=3, lang="en", advanced=True, timeout=5, safe="active", ssl_verify=None):
    """Performs a Google search and returns the results."""
    escaped_term = urllib.parse.quote_plus(term)
    start = 0
    all_results = []
    # Limit the number of characters from each webpage to stay under the token limit
    max_chars_per_page = 4000  # Adjust this value based on your token limit and average webpage length

    with requests.Session() as session:
        while start < num_results:
            resp = session.get(
                url="https://www.google.com/search",
                headers={"User-Agent":'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'},
                params={
                    "q": term,
                    "num": num_results - start,
                    "hl": lang,
                    "start": start,
                    "safe": safe,
                },
                timeout=timeout,
                verify=ssl_verify,
            )
            resp.raise_for_status()
            soup = BeautifulSoup(resp.text, "html.parser")
            result_block = soup.find_all("div", attrs={"class": "g"})
            if not result_block:
                start += 1
                continue
            for result in result_block:
                link = result.find("a", href=True)
                if link:
                    link = link["href"]
                    try:
                        webpage = session.get(link, headers={"User-Agent": get_useragent()})
                        webpage.raise_for_status()
                        visible_text = extract_text_from_webpage(webpage.text)
                        # Truncate text if it's too long
                        if len(visible_text) > max_chars_per_page:
                            visible_text = visible_text[:max_chars_per_page] + "..."
                        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})
            start += len(result_block)
    return all_results

@lru_cache(maxsize=1)  # Cache the models to avoid reloading
def load_speech_recognition_models():
    """Loads and caches speech recognition models."""
    model_name = "neongeckocom/stt_en_citrinet_512_gamma_0_25"
    sample_rate = 16000
    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"))
    return preprocessor, encoder, tokenizer

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

# Mistral Model Configuration
client1 = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
system_instructions1 = "<s>[SYSTEM] Answer as Real 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):
    """Transcribes audio using cached models."""
    preprocessor, encoder, tokenizer = load_speech_recognition_models()
    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

async def run_model(text, web_search):
    """Runs the language model asynchronously."""
    if web_search:
        web_results = await asyncio.get_event_loop().run_in_executor(executor, search, text)  # Run search in executor
        web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
        formatted_prompt = system_instructions1 + text + "[WEB]" + str(web2) + "[ANSWER]"
    else:
        formatted_prompt = system_instructions1 + text + "[JARVIS]"
    stream = client1.text_generation(formatted_prompt, max_new_tokens=512, stream=True, details=True, return_full_text=False)
    return "".join([response.token.text for response in stream if response.token.text != "</s>"])

async def generate_speech(reply):
    """Generates speech asynchronously."""
    communicate = edge_tts.Communicate(reply)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    return tmp_path

async def respond(audio, web_search):
    """Handles user input, model processing, and response generation."""
    user = await asyncio.get_event_loop().run_in_executor(executor, transcribe, audio)  # Run transcription in executor
    reply = await run_model(user, web_search)
    audio_path = await generate_speech(reply)
    return audio_path

with gr.Blocks() as demo:    
    with gr.Row():
        web_search = gr.Checkbox(label="Web Search", value=False)
        input = gr.Audio(label="Voice Chat", sources="microphone", type="numpy")
        output = gr.Audio(label="AI",autoplay=True)
        gr.Interface(fn=respond, inputs=[input, web_search], outputs=[output], live=True)
 
if __name__ == "__main__":
    demo.queue(max_size=200).launch()