Update app.py

app.py

@@ -1,121 +1,121 @@
-# Import required libraries
-import os
-import keras
-import gradio as gr
-import numpy as np
-import pandas as pd
-from PIL import Image
-
-# Function to safely load the models
-def load_model_safely(path: str):
-    if not os.path.isfile(path) or not path.endswith('.keras'):
-        raise FileNotFoundError(f"The file '{path}' does not exist or is not a .keras file.")
-    return keras.saving.load_model(path)
-
-# Retrieve the current directory and specify model paths
-current_dir = os.getcwd()  # Ensure correct initial directory
-model_paths = {
-    'CNN': os.path.join(current_dir, 'Project_7_Traffic_Sign_Detection', 'models', 'cnn_model.keras'),
-    'VGG19': os.path.join(current_dir, 'Project_7_Traffic_Sign_Detection', 'models', 'vgg19_model.keras'),
-    'ResNet50': os.path.join(current_dir, 'Project_7_Traffic_Sign_Detection', 'models', 'resnet50_model.keras'),
-}
-
-# Load models and handle potential exceptions
-models = {}
-for name, path in model_paths.items():
-    try:
-        models[name] = load_model_safely(path)
-    except Exception as e:
-        print(f"Error loading model {name} from {path}: {str(e)}")
-
-# Define the class labels
-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:'Veh > 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 veh > 3.5 tons' }
-
-# Function to import and resize example images
-def get_example_images(images_dir:str, size=(50, 50)) -> list:
-    images = []
-    image_list = os.listdir(images_dir)
-    for image in image_list:
-        if image.lower().endswith('.png'):
-            image_path = os.path.join(images_dir, image)
-            img = Image.open(image_path)
-            img = img.resize(size)
-            images.append(img)
-    return images
-
-# Function to preprocess the image and predict the class
-def preprocess_and_predict(image: Image.Image, size=(50, 50)) -> pd.DataFrame:
-    img_resized = image.resize(size)
-    img_array = np.array(img_resized).astype(np.float32) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)  # Shape (1, 50, 50, 3)
-
-    predictions = []
-    for name, model in models.items():
-        predicted_class_index = np.argmax(model.predict(img_array), axis=-1)[0]
-        predictions.append({'Model': name, 'Predicted Label': classes[predicted_class_index]})
-
-    return pd.DataFrame(predictions)
-
-# Directory for example images
-images_dir = os.path.join(current_dir, 'Project_7_Traffic_Sign_Detection', 'images')
-
-# Check if the images directory exists
-if not os.path.exists(images_dir):
-    print(f"The images directory does not exist: {images_dir}")
-else:
-    example_images = get_example_images(images_dir, (50, 50))
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=preprocess_and_predict,
-    inputs=gr.Image(type='pil'),  # Changed to 'pil' for direct use with PIL
-    outputs="dataframe",  # Correct the output type
-    examples=example_images,
-    title="Traffic Sign Recognition",
-    description="Upload a traffic sign image or choose an example to get the recognition result."
-)
-
-# Launch the Gradio app
+# Import required libraries
+import os
+import keras
+import gradio as gr
+import numpy as np
+import pandas as pd
+from PIL import Image
+
+# Function to safely load the models
+def load_model_safely(path: str):
+    if not os.path.isfile(path) or not path.endswith('.keras'):
+        raise FileNotFoundError(f"The file '{path}' does not exist or is not a .keras file.")
+    return keras.saving.load_model(path)
+
+# Retrieve the current directory and specify model paths
+current_dir = os.getcwd()  # Ensure correct initial directory
+model_paths = {
+    'CNN': os.path.join(current_dir, 'Project_7_Traffic_Sign_Detection', 'models', 'cnn_model.keras'),
+    'VGG19': os.path.join(current_dir, 'Project_7_Traffic_Sign_Detection', 'models', 'vgg19_model.keras'),
+    'ResNet50': os.path.join(current_dir, 'Project_7_Traffic_Sign_Detection', 'models', 'resnet50_model.keras'),
+}
+
+# Load models and handle potential exceptions
+models = {}
+for name, path in model_paths.items():
+    try:
+        models[name] = load_model_safely(path)
+    except Exception as e:
+        print(f"Error loading model {name} from {path}: {str(e)}")
+
+# Define the class labels
+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:'Veh > 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 veh > 3.5 tons' }
+
+# Function to import and resize example images
+def get_example_images(images_dir:str, size=(50, 50)) -> list:
+    images = []
+    image_list = os.listdir(images_dir)
+    for image in image_list:
+        if image.lower().endswith('.png'):
+            image_path = os.path.join(images_dir, image)
+            img = Image.open(image_path)
+            img = img.resize(size)
+            images.append(img)
+    return images
+
+# Function to preprocess the image and predict the class
+def preprocess_and_predict(image: Image.Image, size=(50, 50)) -> pd.DataFrame:
+    img_resized = image.resize(size)
+    img_array = np.array(img_resized).astype(np.float32) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)  # Shape (1, 50, 50, 3)
+
+    predictions = []
+    for name, model in models.items():
+        predicted_class_index = np.argmax(model.predict(img_array), axis=-1)[0]
+        predictions.append({'Model': name, 'Predicted Label': classes[predicted_class_index]})
+
+    return pd.DataFrame(predictions)
+
+# Directory for example images
+images_dir = os.path.join(current_dir, 'Project_7_Traffic_Sign_Detection', 'images')
+
+# Check if the images directory exists
+if not os.path.exists(images_dir):
+    print(f"The images directory does not exist: {images_dir}")
+else:
+    examples = get_example_images(images_dir, (50, 50))
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=preprocess_and_predict,
+    inputs=gr.Image(type='pil'),  # Changed to 'pil' for direct use with PIL
+    outputs="dataframe",  # Correct the output type
+    examples=examples,
+    title="Traffic Sign Recognition",
+    description="Upload a traffic sign image or choose an example to get the recognition result."
+)
+
+# Launch the Gradio app
 iface.launch()