Update placeholder for image

app.py

@@ -25,34 +25,23 @@ def predict(data, model_choice):
         print("Error during prediction:", e)  # Debugging statement
         return {"error": str(e)}
 
-# Define the interface based on the selected model
+# Function to update 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()
+        return gr.update(visible=True), gr.update(visible=False)
     elif model_choice == "Image Deepfake Detection":
-        return gr.Image(type="filepath"), gr.Label()
-    else:
-        return None, None
+        return gr.update(visible=False), gr.update(visible=True)
 
 # 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])
-    
+    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=[input_placeholder, model_choice], outputs=output_placeholder)
+    submit_button.click(fn=predict, inputs=[gr.State(audio_input), gr.State(image_input), model_choice], outputs=output)
     
     iface.launch()
-