initial commit

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+# Load the image classification pipeline with your model
+pipe = pipeline("image-classification", model="surgeonwz/plant-village")
+
+def predict(image):
+    predictions = pipe(image)
+    return {p['label']: p['score'] for p in predictions}
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Plant Image"),
+    outputs=gr.Label(num_top_classes=3, label="Analysis Results"),
+    title="Plant Disease Detector",
+    description="Upload an image of a plant leaf to classify its health."
+)
+
+iface.launch(share=True)

requirements.txt

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+gradio
+transformers
+torch
+Pillow