Update app.py

app.py

@@ -1,29 +1,55 @@
-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()
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load your Keras model from Hugging Face
+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)
+
+# Customize your label names here based on your model's training
+class_names = [
+    "Bacterial Spot", "Early Blight", "Healthy", "Late Blight"
+]
+
+# Predict function for a single image
+def classify(image):
+    if image is None:
+        return None
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+
+    image = image.resize((224, 224))  # Resize to expected input
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    predictions = model.predict(img_array)[0]
+    confidences = {class_names[i]: float(predictions[i]) for i in range(len(class_names))}
+    return confidences
+
+# Build the UI using Blocks
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+    gr.Markdown(
+        """
+        # Analisis Penyakit Tanaman 🍅
+        Unggah gambar daun tanaman untuk mengidentifikasi potensi penyakit.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Unggah Gambar Daun")
+            submit_button = gr.Button("Analisis Gambar", variant="primary")
+        
+        with gr.Column():
+            label_output = gr.Label(label="Hasil Analisis")
+
+    submit_button.click(
+        fn=classify,
+        inputs=image_input,
+        outputs=label_output,
+        api_name="predict"
+    )
+
+app.launch(show_api=True)