Create app.py

app.py

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+import time
+from PIL import Image
+import gradio as gr
+from transformers import pipeline
+
+MODEL_MAP = {
+    "ViT (Base/16, 224)": "google/vit-base-patch16-224",
+    "ResNet-50":           "microsoft/resnet-50",
+    "EfficientNet-B0":     "google/efficientnet-b0"
+}
+
+# Lazy-load to keep startup fast
+_pipes = {}
+def get_pipe(model_id: str):
+    if model_id not in _pipes:
+        _pipes[model_id] = pipeline("image-classification", model=model_id, top_k=5)
+    return _pipes[model_id]
+
+def predict(img: Image.Image, model_name: str):
+    if img is None:
+        return "Upload an image.", None
+    model_id = MODEL_MAP[model_name]
+    pipe = get_pipe(model_id)
+    t0 = time.time()
+    preds = pipe(img)
+    latency_ms = int((time.time() - t0) * 1000)
+    # Clean top-k dict for Gradio Label
+    scores = {p["label"]: round(float(p["score"]), 3) for p in preds}
+    return scores, f"{model_name} • ~{latency_ms} ms"
+
+with gr.Blocks(title="Image Classifier – Multi-Model") as demo:
+    gr.Markdown("# 🐶🐱 Image Classifier (Multi-Model)\nUpload an image, choose a backbone, see top-5 predictions.")
+    with gr.Row():
+        with gr.Column():
+            img = gr.Image(type="pil", label="Image")
+            model = gr.Dropdown(list(MODEL_MAP.keys()), value="ViT (Base/16, 224)", label="Backbone")
+            btn = gr.Button("Predict")
+        with gr.Column():
+            out = gr.Label(label="Top-5")
+            info = gr.Markdown()
+    btn.click(fn=predict, inputs=[img, model], outputs=[out, info])
+
+if __name__ == "__main__":
+    demo.launch()