Upload app.py

app.py

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+import streamlit as st
+import tensorflow
+from tensorflow.keras.models import load_model
+import numpy as np
+from PIL import Image
+import pandas as pd
+import os
+
+# Load the saved TensorFlow model
+model = load_model('traffic-sign-detection-model3.h5')
+
+
+inputBasePath = 'D:\\traffic_Data\\'
+path = 'D:\\traffic_Data\\DATA'
+testingFolder = 'D:\\traffic_Data\\TEST'
+classes = pd.read_csv(os.path.join(inputBasePath,'labels.csv'))
+
+
+# Function to preprocess the image
+def preprocess_image(image):
+    # Preprocess the image as required for your model
+    # (e.g., resize, normalize pixel values)
+    resized_image = image.resize((100,100))
+    preprocessed_image = np.array(resized_image) / 255.0  # Normalize pixel values
+    return preprocessed_image
+
+# Function to make predictions
+def predict(image):
+    preprocessed_image = preprocess_image(image)
+    prediction = model.predict(np.expand_dims(preprocessed_image, axis=0))
+    return prediction
+
+# Streamlit app
+def main():
+    st.title('Traffic Sign Detection')
+
+    uploaded_image = st.file_uploader("Upload Image", type=['jpg', 'png', 'jpeg'])
+
+    if uploaded_image is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_image)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+       # Predict button
+        if st.button('Predict'):
+            # Make prediction
+            prediction = predict(image)
+            predicted_class = np.argmax(prediction, axis=1)
+            #st.write(predicted_class)
+            class_mapping = dict(zip(classes['ClassId'], classes['Name']))
+            predicted_label = class_mapping.get(predicted_class[0])
+            # st.write(predicted_label)
+            # st.write(predicted_class)
+            
+            # Display prediction result
+            
+            st.write('Prediction:', predicted_label)
+
+if __name__ == '__main__':
+    main()