Add application file

app.py

@@ -1,7 +1,34 @@
+# 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
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 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()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()