chat

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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

theme = gr.themes.Soft(
    primary_hue="blue",
    secondary_hue="orange")


# 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)
    return visible_text

def search(term, num_results=1, lang="en", advanced=True, sleep_interval=0, 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 = []

    # Fetch results in batches
    while start < num_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 - start, # Number of results to fetch in this batch
                "hl": lang,
                "start": start,
                "safe": safe,
            },
            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"})

        # If no results, continue to the next batch
        if not result_block:
            start += 1
            continue

        # Extract link and text from each result
        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:
                    # Handle errors fetching or processing webpage
                    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) # Update starting index for next batch

    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/Mixtral-8x7B-Instruct-v0.1")
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):
    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=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

with gr.Blocks(theme=theme) as demo:    
    with gr.Row():
        web_search = gr.Checkbox(label="Web Search", value=False)
        input = gr.Audio(label="User Input", sources="microphone", type="filepath")
        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()


# 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

# theme = gr.themes.Soft(
#     primary_hue="blue",
#     secondary_hue="orange")


# # 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)
#     return visible_text

# def search(term, num_results=1, lang="en", advanced=True, sleep_interval=0, 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 = []

#     # Fetch results in batches
#     while start < num_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 - start, # Number of results to fetch in this batch
#                 "hl": lang,
#                 "start": start,
#                 "safe": safe,
#             },
#             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"})

#         # If no results, continue to the next batch
#         if not result_block:
#             start += 1
#             continue

#         # Extract link and text from each result
#         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:
#                     # Handle errors fetching or processing webpage
#                     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) # Update starting index for next batch

#     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/Mixtral-8x7B-Instruct-v0.1")
# 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):
#     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=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

# with gr.Blocks(theme=theme) as demo:    
#     with gr.Row():
#         web_search = gr.Checkbox(label="Web Search", value=False)
#         input = gr.Audio(label="User Input", sources="microphone", type="filepath")
#         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()