Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import __main__
+
+# 1. THE CRITICAL FIX: 
+def label_func(x): return x.parent.name
+__main__.label_func = label_func
+
+# 2. LOAD THE LEARNER:
+learn = load_learner('waste_model_448_final_v3.pkl')
+categories = learn.dls.vocab
+
+# 3. PREDICTION LOGIC:
+def predict(img):
+    img = PILImage.create(img)
+    pred, pred_idx, probs = learn.predict(img)
+    
+    # Return a dictionary of {Category: Probability} for the Gradio UI
+    return {categories[i]: float(probs[i]) for i in range(len(categories))}
+
+# 4. GRADIO INTERFACE DESIGN:
+demo = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"), 
+    outputs=gr.Label(num_top_classes=3),
+    title="♻️ ConvNeXt-50 Waste Classifier",
+    description="This AI model identifies waste categories with **98.65% accuracy**. Upload a clear image to begin.",
+    article="Developed as part of a Mini Project focusing on high-resolution (448px) deep learning for environmental sustainability."
+)
+
+# 5. EXECUTION:
+if __name__ == "__main__":
+    demo.launch()