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.gitattributes

@@ -33,3 +33,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+animal_images/cat.png filter=lfs diff=lfs merge=lfs -text
+animal_images/frog.png filter=lfs diff=lfs merge=lfs -text
+animal_images/hippo.png filter=lfs diff=lfs merge=lfs -text
+animal_images/jaguar.png filter=lfs diff=lfs merge=lfs -text
+animal_images/sloth.png filter=lfs diff=lfs merge=lfs -text
+animal_images/toucan.png filter=lfs diff=lfs merge=lfs -text
+animal_images/turtle.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,105 @@
+import os
+import zipfile
+from functools import lru_cache
+from glob import glob
+
+import gradio as gr
+import torch
+from transformers import pipeline
+
+
+ZIP_PATH = "animal_images.zip"
+EXAMPLES_DIR = "animal_images"
+
+
+def ensure_examples_extracted():
+    """
+    If animal_images.zip exists and animal_images/ does not, extract it.
+    Works both locally and in HF Spaces.
+    """
+    if os.path.exists(EXAMPLES_DIR):
+        return
+
+    if os.path.exists(ZIP_PATH):
+        os.makedirs(EXAMPLES_DIR, exist_ok=True)
+        with zipfile.ZipFile(ZIP_PATH, "r") as z:
+            z.extractall(EXAMPLES_DIR)
+
+
+def get_example_image_paths(max_examples: int = 7):
+    """
+    Returns up to `max_examples` image file paths for Gradio examples.
+    """
+    ensure_examples_extracted()
+
+    patterns = ["*.png", "*.jpg", "*.jpeg", "*.webp", "*.bmp"]
+    paths = []
+    for pat in patterns:
+        paths.extend(glob(os.path.join(EXAMPLES_DIR, "**", pat), recursive=True))
+
+    # Keep it stable and limited to 7
+    paths = sorted(paths)[:max_examples]
+    return paths
+
+
+@lru_cache(maxsize=1)
+def get_classifier():
+    """
+    Load the HF image-classification pipeline once and reuse it.
+    """
+    device = 0 if torch.cuda.is_available() else -1
+
+    # Solid default ImageNet classifier (good for common animals)
+    model_id = "google/vit-base-patch16-224"
+
+    return pipeline(
+        task="image-classification",
+        model=model_id,
+        device=device
+    )
+
+
+def classify_image(img):
+    """
+    img is a PIL Image from gr.Image(type="pil").
+    Return a dict that gr.Label can render nicely (label -> confidence).
+    """
+    clf = get_classifier()
+    preds = clf(img, top_k=5)
+
+    # Convert to {label: score} for gr.Label
+    out = {p["label"]: float(p["score"]) for p in preds}
+    return out
+
+
+def build_demo():
+    example_paths = get_example_image_paths(7)
+    examples = [[p] for p in example_paths]  # safer format for Gradio examples
+
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            "# Animal Image Classifier\n"
+            "Upload an image (or click an example) to classify it with a pretrained Hugging Face vision model."
+        )
+
+        with gr.Row():
+            inp = gr.Image(type="pil", label="Upload an animal photo")
+            out = gr.Label(num_top_classes=5, label="Predictions (Top 5)")
+
+        btn = gr.Button("Classify")
+
+        btn.click(fn=classify_image, inputs=inp, outputs=out)
+
+        gr.Markdown("## Examples")
+        gr.Examples(
+            examples=examples,
+            inputs=inp,
+            label="Click an example image below"
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = build_demo()
+    demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+transformers
+torch
+pillow