Update app.py

app.py

@@ -1,19 +1,45 @@
 import os
 import numpy as np
 import tensorflow as tf
+from tensorflow.keras import layers, models
 from PIL import Image
 import gradio as gr
 import pickle
+import json
 
-# Thông số
-IMG_HEIGHT = 64
-IMG_WIDTH = 64
+# Load config
+with open('model_config.json', 'r') as f:
+    config = json.load(f)
 
-# Load model và label encoder
-model = tf.keras.models.load_model('traffic_sign_model.keras')
+IMG_HEIGHT = config['img_height']
+IMG_WIDTH = config['img_width']
+num_classes = config['num_classes']
+
+# Load label encoder
 with open('label_encoder.pkl', 'rb') as f:
     le = pickle.load(f)
 
+# Rebuild model architecture
+model = models.Sequential([
+    layers.Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),
+    layers.MaxPooling2D((2, 2)),
+    layers.Flatten(),
+    layers.Dropout(0.5),
+    layers.Dense(num_classes, activation='softmax')
+])
+
+# Load weights
+model.load_weights('model_weights.h5')
+
+# Compile (cần thiết cho predict)
+model.compile(
+    optimizer='adam',
+    loss='sparse_categorical_crossentropy',
+    metrics=['accuracy']
+)
+
+print("✅ Model loaded successfully!")
+
 def predict_traffic_sign(image):
     """
     Hàm dự đoán biển báo giao thông từ ảnh đầu vào
@@ -58,10 +84,12 @@ demo = gr.Interface(
     """,
     examples=[
         # Thêm đường dẫn đến ảnh mẫu trong thư mục examples/
-        # ["examples/stop_sign.jpg"],
-        # ["examples/speed_limit.jpg"],
     ],
     theme=gr.themes.Soft(),
     allow_flagging="never",
     analytics_enabled=False
 )
+
+# Launch
+if __name__ == "__main__":
+    demo.launch()