Create app.py

app.py

@@ -0,0 +1,26 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load model - HF Spaces handles the caching automatically
+print("Loading MobileNet...")
+classifier = pipeline(
+    "image-classification", 
+    model="google/mobilenet_v2_1.0_224"
+)
+
+def classify_image(img):
+    try:
+        results = classifier(img)
+        return {result['label']: float(result['score']) for result in results}
+    except Exception as e:
+        return {"Error": str(e)}
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=5),
+    title="📱 MobileNet Classifier",
+    description="Fast, lightweight image classification running on Hugging Face Spaces."
+)
+
+demo.launch()