Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Download and load model
+model_url = "https://huggingface.co/chimithecat/penyakit_tomat/resolve/main/Tomato_Models.h5"
+model_path = tf.keras.utils.get_file("Tomato_Models.h5", model_url)
+model = tf.keras.models.load_model(model_path)
+
+# Define class names (update based on your training labels)
+class_names = [
+    "Bacterial Spot", "Early Blight", "Healthy", "Late Blight"
+]
+
+def predict(img: Image.Image):
+    img = img.resize((224, 224))  # Resize to match training size
+    img = np.array(img) / 255.0
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    predictions = model.predict(img)[0]
+    return {class_names[i]: float(predictions[i]) for i in range(len(class_names))}
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=4),
+    title="Tomato Leaf Disease Classifier",
+    description="Upload a tomato leaf image to detect its disease"
+).launch()