File size: 1,324 Bytes
49cb8ba 6f44266 49cb8ba 6f44266 49cb8ba 6f44266 49cb8ba 6f44266 49cb8ba 6f44266 49cb8ba 6f44266 bd07181 6f44266 49cb8ba bd07181 6f44266 49cb8ba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
import requests
import time
import threading
import gradio as gr
from transformers import pipeline
# Load Whisper model (adjust size as needed)
model = pipeline("automatic-speech-recognition", model="openai/whisper-base")
def get_public_ip():
response = requests.get('https://api.ipify.org?format=json')
return response.json()["ip"]
def simulate_interaction():
# *** Placeholder for simulating activity ***
print("Simulating interaction...")
def transcribe_audio(audio):
transcription = model(audio)["text"]
return transcription
def background_interaction():
"""Placeholder for background interaction"""
while True:
simulate_interaction()
time.sleep(60 * 5)
# Gradio interface with IP display and speech-to-text
iface = gr.Interface(
[get_public_ip, transcribe_audio],
[gr.Textbox(lines=1, placeholder="Public IP will appear here"), gr.Audio(source="microphone", type="filepath")],
[gr.Textbox(), gr.Textbox()],
title="Public IP Retriever & Whisper Transcription",
description="Get your approximate public IP and transcribe audio using Whisper."
)
# Start background interaction thread (optional)
interaction_thread = threading.Thread(target=background_interaction)
interaction_thread.start()
# Launch the Gradio interface
iface.launch()
|