Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow import keras
+from huggingface_hub import hf_hub_download
+
+# Load your model
+model_path = hf_hub_download("dk00069/WasteClassifier01", "waste_classifier_final.h5")
+model = keras.models.load_model(model_path)
+
+# Your class labels
+labels = ["Cardboard", "Glass", "Metal", "Paper", "Plastic", "Trash"]
+
+def classify(image):
+    img = image.resize((224, 224))
+    img_array = np.expand_dims(np.array(img) / 255.0, axis=0)
+    preds = model.predict(img_array)[0]
+    return {labels[i]: float(preds[i]) for i in range(len(labels))}
+
+gr.Interface(
+    fn=classify,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="🗑️ Waste Classifier",
+    description="Upload a waste image to classify it!"
+).launch()
+```
+
+This gives you a **live public demo link** you can share with anyone! 🚀
+
+---
+
+## 🎯 Your Model's Public Link
+```
+https://huggingface.co/dk00069/WasteClassifier01