app.py

import gradio as gr

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

# Import modules from other files
from chatbot import chatbot, model_inference, BOT_AVATAR, EXAMPLES, model_selector, decoding_strategy, temperature, max_new_tokens, repetition_penalty, top_p
from live_chat import videochat

# Define Gradio theme
theme = gr.themes.Soft(
    primary_hue="blue",
    secondary_hue="orange",
    neutral_hue="gray",
    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif']
).set(
    body_background_fill_dark="#111111",
    block_background_fill_dark="#111111",
    block_border_width="1px",
    block_title_background_fill_dark="#1e1c26",
    input_background_fill_dark="#292733",
    button_secondary_background_fill_dark="#24212b",
    border_color_primary_dark="#343140",
    background_fill_secondary_dark="#111111",
    color_accent_soft_dark="transparent"
)

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

# 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 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):
    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() as voice:    
    gr.Markdown("## Temproraly Not Working (Update in Progress)")
    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)


# Create Gradio blocks for different functionalities

# Chat interface block
with gr.Blocks(
        fill_height=True,
        css=""".gradio-container .avatar-container {height: 40px width: 40px !important;} #duplicate-button {margin: auto; color: white; background: #f1a139; border-radius: 100vh; margin-top: 2px; margin-bottom: 2px;}""",
) as chat:
    gr.Markdown("### Image Chat, Image Generation and Normal Chat")
    with gr.Row(elem_id="model_selector_row"):
        # model_selector defined in chatbot.py
        pass  
    # decoding_strategy, temperature, top_p defined in chatbot.py
    decoding_strategy.change(
        fn=lambda selection: gr.Slider(
            visible=(
                    selection
                    in [
                        "contrastive_sampling",
                        "beam_sampling",
                        "Top P Sampling",
                        "sampling_top_k",
                    ]
            )
        ),
        inputs=decoding_strategy,
        outputs=temperature,
    )
    decoding_strategy.change(
        fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])),
        inputs=decoding_strategy,
        outputs=top_p,
    )
    gr.ChatInterface(
        fn=model_inference,
        chatbot=chatbot,
        examples=EXAMPLES,
        multimodal=True,
        cache_examples=False,
        additional_inputs=[
            model_selector,
            decoding_strategy,
            temperature,
            max_new_tokens,
            repetition_penalty,
            top_p,
            gr.Checkbox(label="Web Search", value=True),
        ],
    )    

# Live chat block
with gr.Blocks() as livechat:
    gr.Interface(
        fn=videochat,
        inputs=[gr.Image(type="pil",sources="webcam", label="Upload Image"), gr.Textbox(label="Prompt", value="what he is doing")],
        outputs=gr.Textbox(label="Answer")
    )

# Other blocks (instant, dalle, playground, image, instant2, video)
with gr.Blocks() as instant:
    gr.HTML("<iframe src='https://kingnish-sdxl-flash.hf.space' width='100%' height='2000px' style='border-radius: 8px;'></iframe>")

with gr.Blocks() as dalle:
    gr.HTML("<iframe src='https://kingnish-image-gen-pro.hf.space' width='100%' height='2000px' style='border-radius: 8px;'></iframe>")

with gr.Blocks() as playground:
    gr.HTML("<iframe src='https://fluently-fluently-playground.hf.space' width='100%' height='2000px' style='border-radius: 8px;'></iframe>")

with gr.Blocks() as image:
    gr.Markdown("""### More models are coming""")
    gr.TabbedInterface([ instant, dalle, playground], ['Instant🖼️','Powerful🖼️', 'Playground🖼'])    

with gr.Blocks() as instant2:
    gr.HTML("<iframe src='https://kingnish-instant-video.hf.space' width='100%' height='3000px' style='border-radius: 8px;'></iframe>")

with gr.Blocks() as video:
    gr.Markdown("""More Models are coming""")
    gr.TabbedInterface([ instant2], ['Instant🎥'])   

# Main application block
with gr.Blocks(theme=theme, title="OpenGPT 4o DEMO") as demo:
    gr.Markdown("# OpenGPT 4o")
    gr.TabbedInterface([chat, voice, livechat, image, video], ['💬 SuperChat'])

demo.queue(max_size=300)
demo.launch()