Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Define a function to make predictions on a single image
+def predict_single_image(image, model):
+    # Resize and preprocess the image
+    img = image.resize((128, 128))
+    img = np.array(img) / 255.0  # Normalization
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+
+    # Make prediction using the provided model
+    prediction = model.predict(img)
+
+    # Thresholding prediction
+    threshold = 0.5
+    prediction_class = (prediction > threshold).astype(int)
+
+    # Interpret prediction
+    if prediction_class == 1:
+        return "With Mask"
+    else:
+        return "Without Mask"
+
+# Load the model from .h5 file
+@st.cache(allow_output_mutation=True)
+def load_model_from_h5():
+    return load_model('model.h5')
+
+# Streamlit app
+def main():
+    st.title("Mask Detection App")
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        # Load the image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+        # Button to make prediction
+        if st.button('Predict'):
+            # Load the model from .h5 file
+            model_h5 = load_model_from_h5()
+            # Make predictions using the provided model
+            prediction = predict_single_image(image, model_h5)
+            st.write("Prediction:", prediction)
+
+if __name__ == '__main__':
+    main()