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app.py

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+# 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
+iface.launch()

requirements.txt

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+pandas
+numpy
+PIL
+scikit-learn
+keras