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app.py

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+# Import required libraries
+import os
+import keras
+import numpy as np
+import pandas as pd
+import streamlit as st
+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, 'models', 'cnn_model.keras'),
+    'VGG19': os.path.join(current_dir, 'models', 'vgg19_model.keras'),
+    'ResNet50': os.path.join(current_dir, '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:
+        st.error(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 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)
+
+# Import Example images
+images_dir = os.path.join(current_dir, 'images')
+
+if os.path.exists(images_dir):
+    # Create a list of images and their corresponding classes
+    image_list = [img for img in os.listdir(images_dir) if img.lower().endswith('.png')]
+    image_dict = {classes[int(img.split('.')[0])] : os.path.join(images_dir, img) for img in image_list}
+else:
+    st.error(f"The images directory does not exist: {images_dir}")
+
+# Streamlit UI setup
+st.set_page_config(page_title="Traffic Sign Detection App", page_icon="🚦", layout="wide")
+st.title("🚦 Traffic Sign Recognition using CNN, VGG19, ResNet50")
+st.markdown("Upload a traffic sign image or choose an example from below to get the recognition result.")
+st.markdown("---")
+
+# Sidebar for image upload and selection
+st.sidebar.header("Input Options")
+uploaded_file = st.sidebar.file_uploader("Upload an image (JPG, JPEG, PNG)", type=["jpg", "jpeg", "png"])
+
+# Select an example image
+selected_example = st.sidebar.selectbox("Or select an example image:", list(image_dict.keys()))
+if selected_example:
+    example_image_path = image_dict[selected_example]
+
+# Initialize a variable to hold the image for prediction
+image_to_predict = None
+
+# Check if user uploaded an image or selected an example image
+if uploaded_file is not None:
+    image_to_predict = Image.open(uploaded_file)
+    st.image(image_to_predict.resize((256, 256)), caption='Uploaded Image', use_container_width=False, output_format="auto")
+elif selected_example:
+    image_to_predict = Image.open(example_image_path)
+    st.image(image_to_predict.resize((256, 256)), caption='Example Image', use_container_width=False, output_format="auto")
+
+# Add a predict button
+if st.sidebar.button("🚀 Predict", key="predict_button") and image_to_predict is not None:
+    # Run prediction
+    st.write("Predicting ...")
+    results = preprocess_and_predict(image_to_predict)
+    
+    # Display results
+    st.write("### Prediction Results")
+
+    # Style the output dataframe
+    st.dataframe(results)
+
+# Add some custom CSS for better styling
+st.markdown("""
+<style>
+    .stButton > button:hover {
+        background-color: #0052cc; /* Darker blue on hover */
+    }
+    .stDataframe {
+        border: 1px solid #ddd;     /* Light border for clarity */
+        border-radius: 10px;        /* Rounded corners for the dataframe */
+    }
+    .stImage {
+        border: 2px solid #0066ff; /* Border for images */
+        border-radius: 10px;       /* Rounded corners */
+        box-shadow: 0 0 8px rgba(0, 0, 0, 0.2); /* Subtle shadow */
+    }
+</style>
+""", unsafe_allow_html=True)

requirements.txt

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+pandas
+numpy
+pillow
+scikit-learn
+keras