Update app.py

app.py

@@ -1,3 +1,172 @@
-import gradio as gr
+import os
 
-gr.load("models/mistralai/Mixtral-8x7B-Instruct-v0.1").launch()
+# Install required libraries
+os.system("pip install streamlit edge_tts pydub soxr numpy onnxruntime sentencepiece huggingface_hub torch beautifulsoup4 requests urllib3")
+
+import streamlit as st
+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
+
+# Streamlit interface
+st.title("OpenGPT 4o DEMO")
+
+web_search = st.checkbox("Web Search", value=False)
+input_audio = st.file_uploader("Upload Audio", type=["wav", "mp3", "ogg"])
+if st.button("Transcribe and Respond"):
+    if input_audio:
+        output_audio_path = asyncio.run(respond(input_audio, web_search))
+        audio_file = open(output_audio_path, "rb")
+        audio_bytes = audio_file.read()
+        st.audio(audio_bytes, format="audio/wav")