Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import gradio as gr
 import edge_tts
 import asyncio
@@ -13,12 +14,12 @@ import requests
 from bs4 import BeautifulSoup
 import urllib
 import random
-import speech_recognition as sr
 
 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',
@@ -159,204 +160,17 @@ async def respond(audio, web_search):
         await communicate.save(tmp_path)
     return tmp_path
 
-def listen_for_speech(web_search):
-    recognizer = sr.Recognizer()
-    with sr.Microphone() as source:
-        print("Listening for speech...")
-        audio_data = recognizer.listen(source)
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
-            tmp_path = tmp_file.name
-            with open(tmp_path, 'wb') as f:
-                f.write(audio_data.get_wav_data())
-    return asyncio.run(respond(tmp_path, web_search))
-
 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)
-    demo.add_listener(listen_for_speech, inputs=[web_search], outputs=[output])
+        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()
-
-
 # import gradio as gr
 # import edge_tts
 # import asyncio