Update app.py

app.py

@@ -4,34 +4,44 @@ from PIL import Image
 import numpy as np
 
 
-# Laden Sie Ihr benutzerdefiniertes Regressionsmodell
-model = tf.keras.models.load_model('Task_Pokemon.keras')
-
 # Klassennamen, sollten Ihrem Dataset entsprechen
-class_names = ['Aerodactyl', 'Charizard', 'Victreebel']
+class_names = ['Apple__black_rot', 'Apple__healthy', 'Apple__rust', 'Apple__scab']
+
+def predict_figure(uploaded_file):
+    if uploaded_file is None:
+        return "No file uploaded.", None, "No prediction"
+
+    model = tf.keras.models.load_model('apple_classification.keras')
 
-def classify_image(image):
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
-    img = image.resize((224, 224))  # Ändern Sie die Größe auf 224x224
-    img_array = tf.keras.preprocessing.image.img_to_array(img)
-    img_array = tf.expand_dims(img_array, 0)  # Erstelle einen Batch
-    predictions = model.predict(img_array)
-    predicted_class = class_names[np.argmax(predictions[0])]
-    confidence = np.max(predictions[0])
-    return {predicted_class: float(confidence)}
+    # Load the image from the file path
+    with Image.open(uploaded_file).convert('RGB') as img:
+        img = img.resize((150, 150))
+        img_array = np.array(img)
 
+        prediction = model.predict(np.expand_dims(img_array, axis=0))
 
-image_input = gr.Image()
-label = gr.Label(num_top_classes=3)
+        # Identify the most confident prediction
+        confidences = {class_names[i]: np.round(float(prediction[0][i]), 2) for i in range(len(class_names))}
+        return img, confidences
 
+# Define example images
+examples = [
+    ["images/Apple__black_rot.jpg"],
+    ["images/Apple__healthy.png"],
+    ["images/Apple__rust.png"],
+    ["images/Apple__scab.png"]
+]
+
+
+# Define the Gradio interface
 iface = gr.Interface(
-    fn=classify_image,
-    inputs=image_input,
-    outputs=label,
-    title='Pokémon-Bildklassifizierer',
-    description='Lade ein Bild von Aerodactyl, Charizard oder Victreebel hoch. Unser Klassifizierer wird das Pokémon identifizieren und das Vertrauensniveau der Vorhersage anzeigen.'
+    fn=predict_figure,  # Function to process the input
+    inputs=gr.File(label="Upload File"),  # File upload widget
+    outputs=["image", "text"],  # Output types for image and text
+    title="Leaf Disease Classifier",  # Title of the interface
+    description="Upload a picture of a Apple Leaf to see condition health it is and the model's confidence level.",  # Description of the interface
+    examples=examples  # Example images
 )
-
 iface.launch()