Create app.py

app.py

@@ -0,0 +1,29 @@
+import gradio as gr
+from transformers import pipeline
+from optimum.pipelines import onnx_pipeline
+
+# Load Models
+clean_pipe = pipeline("image-classification", model="WinKawaks/vit-small-patch16-224")
+mal_pipe = onnx_pipeline("image-classification", model="WinKawaks/vit-small-patch16-224", accelerator="ort")
+
+# Interface Functions
+
+def classify_image(model_type, image):
+    if model_type == "Clean Model":
+        return clean_pipe(image)
+    elif model_type == "Malicious Model":
+        return mal_pipe(image)
+    else:
+        return "Invalid model type"
+
+# Gradio Interface
+inputs = [
+    gr.inputs.Radio(choices=["Clean Model", "Malicious Model"], label="Select Model"),
+    gr.inputs.Image(type="filepath", label="Upload Image")
+]
+
+outputs = gr.outputs.Label(num_top_classes=1, label="Classification Result")
+
+app = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title="Model Comparison: Clean vs Malicious", description="Compare the behavior of a clean model and a potentially malicious model using the same image input.")
+
+app.launch()