import os
import streamlit as st
import torch
import sentencepiece as spm
import onnxruntime as ort
from pydub import AudioSegment
import numpy as np
import soxr
import edge_tts
import requests
from bs4 import BeautifulSoup
import urllib
import random
from huggingface_hub import hf_hub_download, InferenceClient
import tempfile

# Install necessary libraries
os.system('pip install streamlit torch onnxruntime transformers sentencepiece pydub soxr edge-tts requests beautifulsoup4')

# Load 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"))

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]"

_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():
    return random.choice(_useragent_list)

def extract_text_from_webpage(html_content):
    soup = BeautifulSoup(html_content, "html.parser")
    for tag in soup(["script", "style", "header", "footer", "nav"]):
        tag.extract()
    visible_text = soup.get_text(strip=True)
    return visible_text

def search(term, num_results=1):
    escaped_term = urllib.parse.quote_plus(term)
    start = 0
    all_results = []

    while start < num_results:
        resp = requests.get(
            url="https://www.google.com/search",
            headers={"User-Agent": get_useragent()},
            params={
                "q": term,
                "num": num_results - start,
                "hl": "en",
                "start": start,
                "safe": "active",
            },
            timeout=5,
        )
        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 = requests.get(link, headers={"User-Agent": get_useragent()})
                    webpage.raise_for_status()
                    visible_text = extract_text_from_webpage(webpage.text)
                    all_results.append({"link": link, "text": visible_text})
                except requests.exceptions.RequestException as e:
                    all_results.append({"link": link, "text": None})
            else:
                all_results.append({"link": None, "text": None})

        start += len(result_block)

    return all_results

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):
    if web_search:
        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=512, 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=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 respond(audio, web_search):
    user = transcribe(audio)
    reply = model(user, web_search)
    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

# Streamlit interface
st.title("OpenGPT 4o DEMO")

# Chat input interface
st.subheader("💬 SuperChat")
prompt = st.text_input("Say something")
if prompt:
    web_search = st.checkbox("Web Search", value=True)
    response = model(prompt, web_search)
    st.write(response)

# Audio input interface
st.subheader("🗣️ Voice Chat")
audio_file = st.file_uploader("Upload an audio file", type=["wav", "mp3"])
if audio_file:
    web_search = st.checkbox("Web Search", value=False)
    with st.spinner("Transcribing and generating response..."):
        audio_path = audio_file.name
        with open(audio_path, "wb") as f:
            f.write(audio_file.getbuffer())
        response_audio = await respond(audio_path, web_search)
        st.audio(response_audio)