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import gradio as gr
from transformers import pipeline
# 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
def predict(data, model_choice):
print("Data received:", data) # Debugging statement
try:
if model_choice == "Audio Deepfake Detection":
result = audio_model(data)
elif model_choice == "Image Deepfake Detection":
result = image_model(data)
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)}
# Define the interface based on the selected model
def update_interface(model_choice):
if model_choice == "Audio Deepfake Detection":
return gr.Audio(type="filepath"), gr.Label()
elif model_choice == "Image Deepfake Detection":
return gr.Image(type="filepath"), gr.Label()
else:
return None, None
# Create the 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")
input_component, output_component = update_interface(model_choice.value)
def update_inputs(model_choice):
input_component, output_component = update_interface(model_choice)
input_placeholder.update(visible=False)
output_placeholder.update(visible=False)
input_placeholder.update(visible=True, component=input_component)
output_placeholder.update(visible=True, component=output_component)
input_placeholder = gr.Placeholder(gr.Component, visible=True)
output_placeholder = gr.Placeholder(gr.Component, visible=True)
model_choice.change(fn=update_inputs, inputs=model_choice, outputs=[input_placeholder, output_placeholder])
submit_button = gr.Button("Submit")
submit_button.click(fn=predict, inputs=[input_placeholder, model_choice], outputs=output_placeholder)
iface.launch()
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