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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):
try:
# 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"
except Exception as e:
st.error(f"Error occurred during prediction: {str(e)}")
# Load the model from .h5 file
@st.cache(allow_output_mutation=True)
def load_model_from_h5():
try:
return load_model('model.h5')
except Exception as e:
st.error(f"Error occurred while loading the model: {str(e)}")
# Streamlit app
def main():
# App title and description
st.title("Face Mask Detection App")
st.write("Upload an image and click 'Predict' to see the prediction.")
# Custom CSS for styling
custom_css = """
<style>
body {
background-color: #f0f2f6;
}
.stButton>button {
background-color: #4CAF50;
color: white;
padding: 10px 15px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: 16px;
margin: 4px 2px;
cursor: pointer;
border-radius: 8px;
border: none;
}
.stButton>button:hover {
background-color: #45a049;
}
.stTextInput>div>div>input {
border-radius: 8px;
}
.stTextInput>div>div>input:focus {
border-color: #4CAF50;
box-shadow: none;
}
</style>
"""
st.markdown(custom_css, unsafe_allow_html=True)
uploaded_file = st.file_uploader("", 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', key='predict_button'):
# Load the model from .h5 file
model_h5 = load_model_from_h5()
if model_h5:
# Make predictions using the provided model
prediction = predict_single_image(image, model_h5)
if prediction:
st.success(f"Prediction: {prediction}")
if __name__ == '__main__':
main()
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