Update app.py

app.py

@@ -3,49 +3,69 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 
-# Load your Keras model from Hugging Face
-model = pipeline("image-classification", model="wellCh4n/tomato-leaf-disease-classification-vit", top_k=10)
+# Constants
+IMAGE_SIZE = (300, 300)
 
-# Customize your label names here based on your model's training
-class_names = [
-    "Bacterial Spot", "Early Blight", "Healthy", "Late Blight"
-]
+# Class index to label mapping
+CLASS_NAMES = {
+    0: "A healthy tomato leaf",
+    1: "A tomato leaf with Leaf Mold",
+    2: "A tomato leaf with Target Spot",
+    3: "A tomato leaf with Late Blight",
+    4: "A tomato leaf with Early Blight",
+    5: "A tomato leaf with Bacterial Spot",
+    6: "A tomato leaf with Septoria Leaf Spot",
+    7: "A tomato leaf with Tomato Mosaic Virus",
+    8: "A tomato leaf with Tomato Yellow Leaf Curl Virus",
+    9: "A tomato leaf with Spider Mites Two-spotted Spider Mite"
+}
 
+# Load the model from Hugging Face Hub
+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)
+
+# Prediction function
 def classify(image):
     if image is None:
-        return None
+        return "Tidak ada gambar.", ""
+
     if not isinstance(image, Image.Image):
         image = Image.fromarray(image)
 
-    image = image.resize((300, 300))  # 👈 match the model's input shape
-    img_array = np.array(image) / 255.0
+    img = image.resize(IMAGE_SIZE)
+    img_array = np.array(img) / 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
+    predicted_index = int(np.argmax(predictions))
+    confidence = float(predictions[predicted_index])
+    label = CLASS_NAMES[predicted_index]
 
-# Build the UI using Blocks
+    return f"{label}", f"Confidence: {confidence*100:.2f}%"
+
+# UI with Gradio Blocks
 with gr.Blocks(theme=gr.themes.Soft()) as app:
     gr.Markdown(
         """
-        # Analisis Penyakit Tanaman 🍅
-        Unggah gambar daun tanaman untuk mengidentifikasi potensi penyakit.
+        # 🍅 Tomato Leaf Disease Classifier  
+        Upload a photo of a tomato leaf to detect its potential disease.
         """
     )
 
     with gr.Row():
         with gr.Column():
-            image_input = gr.Image(type="pil", label="Unggah Gambar Daun")
-            submit_button = gr.Button("Analisis Gambar", variant="primary")
-        
+            image_input = gr.Image(type="pil", label="Upload Leaf Image")
+            submit_button = gr.Button("Analyze", variant="primary")
+
         with gr.Column():
-            label_output = gr.Label(label="Hasil Analisis")
+            result_output = gr.Text(label="Prediction Result")
+            confidence_output = gr.Text(label="Confidence")
 
     submit_button.click(
         fn=classify,
         inputs=image_input,
-        outputs=label_output,
+        outputs=[result_output, confidence_output],
         api_name="predict"
     )