push app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+vit_classifier = pipeline("image-classification", model="MichaelMM2000/vit-base-animals10")
+
+clip_detector = pipeline(model="openai/clip-vit-base-patch32", task="zero-shot-image-classification")
+
+# Animals-10 class names
+labels_animals10 = [
+    "butterfly", "cat", "chicken", "cow", "dog",
+    "elephant", "horse", "sheep", "spider", "squirrel"
+]
+
+def classify_animal(image):
+    # Run ViT classifier
+    vit_results = vit_classifier(image)
+    vit_output = {result['label']: result['score'] for result in vit_results}
+
+    # Run CLIP zero-shot classifier
+    clip_results = clip_detector(image, candidate_labels=[f"a photo of a {label}" for label in labels_animals10])
+    clip_output = {result['label']: result['score'] for result in clip_results}
+
+    return {
+        "ViT Classification": vit_output,
+        "CLIP Zero-Shot Classification": clip_output
+    }
+
+# Optional: you can add example images
+example_images = [
+    ["example_images/cat.jpeg"],
+    ["example_images/chicken1.jpeg"],
+    ["example_images/chicken2.jpeg"],
+    ["example_images/elefant.jpg"],
+    ["example_images/spider.jpeg"],
+    ["example_images/butterfly.jpg"]
+]
+
+iface = gr.Interface(
+    fn=classify_animal,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.JSON(),
+    title="Animals-10 Classification: ViT vs CLIP",
+    description="Upload an animal image to compare predictions from your trained ViT model and a zero-shot CLIP model.",
+    examples=example_images
+)
+
+iface.launch()