Update app.py

app.py

@@ -22,161 +22,6 @@ theme = gr.themes.Soft(
     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 Jarvis JARVIS, Made by 'Tony Stark', 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 as if You are the character Jarvis, made by 'Tony Stark.' 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) + "[ANSWER]"
-        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 + "[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 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)
-
-
 # Create Gradio blocks for different functionalities
 
 # Chat interface block
@@ -228,11 +73,8 @@ with gr.Blocks(
 
 # Voice chat block
 with gr.Blocks() as voice:    
-    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)     
+    gr.HTML("<iframe src='https://kingnish-voice-chat-ai.hf.space' width='100%' height='2000px' style='border-radius: 8px;'></iframe>")
+    
 
 # Live chat block
 with gr.Blocks() as livechat: