Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# 1. Choose a Hugging Face image classification model
+#    You can change this later (for digits, beans, general images etc.)
+MODEL_ID = "nateraw/vit-base-beans"  # demo model trained on 3 types of beans
+
+# 2. Create a pipeline (this downloads and loads the model)
+classifier = pipeline("image-classification", model=MODEL_ID)
+
+# 3. Define a prediction function Gradio will use
+def predict(image):
+    """
+    image: PIL.Image (Gradio handles conversion)
+    returns: dict {label: score} for the top classes
+    """
+    results = classifier(image)
+
+    # Gradio's Label component likes a dict: {label: confidence}
+    output = {r["label"]: float(r["score"]) for r in results}
+    return output
+
+# 4. Build the Gradio Interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload or draw an image"),
+    outputs=gr.Label(num_top_classes=3, label="Predictions"),
+    title="Vision Transformer Image Classifier",
+    description=(
+        "Upload a small image and this app will classify it using a pretrained "
+        "Vision Transformer model from Hugging Face."
+    ),
+    examples=[],  # you can add example images later
+)
+
+# 5. Run the app (Spaces will call launch() automatically)
+if __name__ == "__main__":
+    demo.launch()