app.py

# import gradio as gr

# def greet(name):
#    return "Hello " + name + "!!"

# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()

import torch
import gradio as gr
from transformers import AutoModelForSequenceClassification

# Load your generator model checkpoint
generator_checkpoint_path = "/home/linux/Documents/Ravi_PHD_Data/hifi-gan/cp_hifigan/date_elevan_feb_twozerotwofour/g_00375000"

# Define your inference function
def generate_deepfake(wave_file):
    # Load generator model
    generator_model = AutoModelForSequenceClassification.from_pretrained(generator_checkpoint_path)

    # Process input wave file (e.g., convert to spectrogram, extract features)
    # Perform deepfake generation using the loaded model
    # Replace the following lines with your actual deepfake generation logic
    # For demonstration purposes, we'll just return the input wave file as-is.
    deepfake_wave_file = wave_file

    # Return the deepfake wave file
    return deepfake_wave_file

# Create a Gradio interface
inputs = gr.inputs.Audio(label="Upload a wave file")
outputs = gr.outputs.Audio(label="Deepfake wave file")
gr.Interface(fn=generate_deepfake, inputs=inputs, outputs=outputs).launch()