Update app.py

app.py

@@ -1,3 +1,175 @@
+# 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
@@ -167,4 +339,5 @@ with gr.Blocks(theme=theme) as demo:
         gr.Interface(fn=respond, inputs=[input, web_search], outputs=[output], live=True)
 
 if __name__ == "__main__":
-    demo.queue(max_size=200).launch()
+    demo.queue(max_size=200).launch() 
+