Upload 2 files

app.py

@@ -0,0 +1,47 @@
+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(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
+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()

requirements.txt

@@ -0,0 +1,3 @@
+torch
+gradio
+transformers