Update app.py

app.py

@@ -10,23 +10,52 @@ def load_model():
 
 model = load_model()
 
-# Class labels
-class_names = [
-    'Speed limit (20km/h)', 'Speed limit (30km/h)', 'Speed limit (50km/h)', 
-    'Speed limit (60km/h)', 'Speed limit (70km/h)', 'Speed limit (80km/h)', 
-    'End of speed limit (80km/h)', 'Speed limit (100km/h)', 'Speed limit (120km/h)', 
-    'No passing', 'No passing for vehicles over 3.5 metric tons', 
-    'Right-of-way at the next intersection', 'Priority road', 'Yield', 'Stop', 
-    'No vehicles', 'Vehicles over 3.5 metric tons prohibited', 'No entry', 
-    'General caution', 'Dangerous curve to the left', 'Dangerous curve to the right', 
-    'Double curve', 'Bumpy road', 'Slippery road', 'Road narrows on the right', 
-    'Road work', 'Traffic signals', 'Pedestrians', 'Children crossing', 
-    'Bicycles crossing', 'Beware of ice/snow', 'Wild animals crossing', 
-    'End of all speed and passing limits', 'Turn right ahead', 'Turn left ahead', 
-    'Ahead only', 'Go straight or right', 'Go straight or left', 'Keep right', 
-    'Keep left', 'Roundabout mandatory', 'End of no passing', 
-    'End of no passing by vehicles over 3.5 metric tons'
-]
+# Class names
+classes = {
+    0:'Speed limit (20km/h)',
+    1:'Speed limit (30km/h)',
+    2:'Speed limit (50km/h)',
+    3:'Speed limit (60km/h)',
+    4:'Speed limit (70km/h)',
+    5:'Speed limit (80km/h)',
+    6:'End of speed limit (80km/h)',
+    7:'Speed limit (100km/h)',
+    8:'Speed limit (120km/h)',
+    9:'No passing',
+    10:'No passing veh over 3.5 tons',
+    11:'Right-of-way at intersection',
+    12:'Priority road',
+    13:'Yield',
+    14:'Stop',
+    15:'No vehicles',
+    16:'Vehicle > 3.5 tons prohibited',
+    17:'No entry',
+    18:'General caution',
+    19:'Dangerous curve left',
+    20:'Dangerous curve right',
+    21:'Double curve',
+    22:'Bumpy road',
+    23:'Slippery road',
+    24:'Road narrows on the right',
+    25:'Road work',
+    26:'Traffic signals',
+    27:'Pedestrians',
+    28:'Children crossing',
+    29:'Bicycles crossing',
+    30:'Beware of ice/snow',
+    31:'Wild animals crossing',
+    32:'End speed + passing limits',
+    33:'Turn right ahead',
+    34:'Turn left ahead',
+    35:'Ahead only',
+    36:'Go straight or right',
+    37:'Go straight or left',
+    38:'Keep right',
+    39:'Keep left',
+    40:'Roundabout mandatory',
+    41:'End of no passing',
+    42:'End no passing vehicle > 3.5 tons'
+}
 
 st.title('Traffic Sign Classifier')
 
@@ -38,14 +67,16 @@ if uploaded_file is not None:
     
     # Preprocess the image
     image = image.resize((30, 30))
-    image = np.expand_dims(image, axis=0)
+    image = image.convert('RGB')  # Convert to RGB
     image = np.array(image)
+    image = image / 255.0  # Normalize pixel values
+    image = np.expand_dims(image, axis=0)
     
     # Make prediction
     pred = model.predict(image)
     predicted_class = np.argmax(pred, axis=-1)[0]
     
-    st.write(f"Predicted traffic sign: {class_names[predicted_class]}")
+    st.write(f"Predicted traffic sign: {classes[predicted_class]}")
     st.write(f"Confidence: {pred[0][predicted_class]*100:.2f}%")
 
-st.write("Note: This application uses a pre-trained model. For best results, upload images that are 30x30 pixels and similar to those used in the training dataset.")
+st.write("Note: This application uses a pre-trained model. For best results, upload images that are similar to those used in the training dataset.")