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import gradio as gr
from transformers import pipeline
import spaces
# Load the models using pipeline
audio_model = pipeline("audio-classification", model="MelodyMachine/Deepfake-audio-detection-V2")
image_model = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection")
# Define the prediction function
@spaces.GPU
def predict(audio, image, model_choice):
print("Data received:", audio if model_choice == "Audio Deepfake Detection" else image) # Debugging statement
try:
if model_choice == "Audio Deepfake Detection":
result = audio_model(audio)
elif model_choice == "Image Deepfake Detection":
result = image_model(image)
else:
return {"error": "Invalid model choice"}
print("Raw prediction result:", result) # Debugging statement
# Convert the result to the expected format
output = {item['label']: item['score'] for item in result}
print("Formatted prediction result:", output) # Debugging statement
return output
except Exception as e:
print("Error during prediction:", e) # Debugging statement
return {"error": str(e)}
# Update interface based on the selected model
@spaces.GPU
def update_interface(model_choice):
if model_choice == "Audio Deepfake Detection":
return gr.update(visible=True), gr.update(visible=False)
elif model_choice == "Image Deepfake Detection":
return gr.update(visible=False), gr.update(visible=True)
# Create Gradio interface
with gr.Blocks() as iface:
model_choice = gr.Radio(choices=["Audio Deepfake Detection", "Image Deepfake Detection"], label="Select Model", value="Audio Deepfake Detection")
audio_input = gr.Audio(type="filepath", label="Upload Audio File")
image_input = gr.Image(type="filepath", label="Upload Image File", visible=False)
output = gr.Label()
model_choice.change(fn=update_interface, inputs=model_choice, outputs=[audio_input, image_input])
submit_button = gr.Button("Submit")
submit_button.click(fn=predict, inputs=[audio_input, image_input, model_choice], outputs=output)
iface.launch()
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