Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the fine‑tuned model (adjust path if needed)
+model = tf.keras.models.load_model("model/O_R_tlearn_fine_tune_vgg16.keras")
+
+# Class names
+CLASS_NAMES = ["Organic (O)", "Recyclable (R)"]
+
+def predict_image(image):
+    """
+    image: PIL Image or numpy array (H, W, 3)
+    Returns: label string and confidence score
+    """
+    # Resize to 150x150 (the model's input size)
+    img = image.resize((150, 150))
+    img_array = np.array(img) / 255.0          # rescale as during training
+    img_array = np.expand_dims(img_array, axis=0)  # add batch dimension
+
+    pred = model.predict(img_array)[0][0]      # sigmoid output
+    confidence = pred if pred > 0.5 else 1 - pred
+    label = CLASS_NAMES[0] if pred < 0.5 else CLASS_NAMES[1]
+    return f"{label} (confidence: {confidence:.2f})"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Waste Classifier (Organic vs Recyclable)",
+    description="Upload an image of waste to classify it as Organic (O) or Recyclable (R)."
+)
+
+if __name__ == "__main__":
+    iface.launch()