Update app.py

app.py

@@ -5,32 +5,17 @@ from transformers import pipeline
 image_model = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection")
 
 # Define the prediction function
-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
+def predict(image):
+ result = image_model(image)
+ 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
+ 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
-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:
  image_input = gr.Image(type="filepath", label="Upload Image File", visible=False)