Upload app.py

app.py

@@ -0,0 +1,29 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load model with correct name
+model = tf.keras.models.load_model("waste_classification_finetuned.h5")
+IMG_SIZE = (224, 224)
+
+# Class labels
+index_to_label = {
+    0: "paper", 1: "e-waste", 2: "metal", 3: "light blubs",
+    4: "organic", 5: "plastic", 6: "clothes", 7: "glass", 8: "batteries"
+}
+
+def classify_image(img):
+    img = tf.image.resize(img, IMG_SIZE)
+    img = tf.expand_dims(img, axis=0)
+    img = tf.cast(img, tf.float32) / 255.0
+    pred = model.predict(img)[0]
+    return {index_to_label[i]: float(pred[i]) for i in range(len(pred))}
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy", label="Upload Waste Image"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Smart Waste Classifier"
+)
+
+demo.launch()