Upload 9 files

.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 +1,101 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jan 29 11:12:02 2026
+
+@author: atulkar
+"""
+
+import os
+import gradio as gr
+from transformers import pipeline
+
+# -----------------------------
+# Load Image Classification pipeline (pretrained)
+# -----------------------------
+# Good general-purpose ImageNet-style classifier
+clf = pipeline(
+    task="image-classification",
+    model="google/vit-base-patch16-224"
+)
+
+# -----------------------------
+# Locate example images (works locally + on HF Spaces)
+# -----------------------------
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+EXAMPLES_DIR = os.path.join(BASE_DIR, "animal_images")
+
+EXAMPLE_FILES = [
+    "cat.png",
+    "frog.png",
+    "hippo.png",
+    "jaguar.png",
+    "sloth.png",
+    "toucan.png",
+    "turtle.png",
+]
+
+examples = []
+missing = []
+for fname in EXAMPLE_FILES:
+    fpath = os.path.join(EXAMPLES_DIR, fname)
+    if os.path.exists(fpath):
+        examples.append([fpath])
+    else:
+        missing.append(fname)
+
+# -----------------------------
+# Prediction function
+# -----------------------------
+def classify_image(img):
+    """
+    img comes in as a PIL image (because gr.Image(type="pil"))
+    Returns a dict for gr.Label: {label: confidence}
+    """
+    if img is None:
+        return {}
+
+    preds = clf(img, top_k=3)
+    return {p["label"]: float(p["score"]) for p in preds}
+
+
+# -----------------------------
+# Build Gradio App
+# -----------------------------
+with gr.Blocks(title="Animal Image Classifier") as demo:
+    gr.Markdown("# Animal Image Classifier")
+    gr.Markdown(
+        "Upload an animal image (or click an example). "
+        "This app uses a Hugging Face `image-classification` pipeline."
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            inp = gr.Image(type="pil", label="Input Image")
+            with gr.Row():
+                btn = gr.Button("Submit", variant="primary")
+                clr = gr.Button("Clear")
+        with gr.Column(scale=1):
+            out = gr.Label(num_top_classes=3, label="Top Predictions")
+
+    btn.click(fn=classify_image, inputs=inp, outputs=out)
+    clr.click(fn=lambda: (None, {}), inputs=None, outputs=[inp, out])
+
+    if examples:
+        gr.Examples(
+            examples=examples,
+            inputs=inp,
+            label="Examples (from ./animal_images/)",
+        )
+
+    if missing:
+        gr.Markdown(
+            "\n\nMake sure the folder is next to `app.py` locally, "
+              "and uploaded into your Hugging Face Space repo when deploying."
+        )
+
+# -----------------------------
+# Launch
+# -----------------------------
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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