app.py

import streamlit as st
import pickle
from PIL import Image

# Load the pretrained model from the pickle file
model_filename = 'model.pkl'
with open(model_filename, 'rb') as file:
    model = pickle.load(file)

# Function to make predictions
def predict_pneumonia(image):
    # Preprocess the image (you may need to resize or normalize it)
    # preprocess_image(image)

    # Make predictions using the loaded model
    prediction = model.predict(image)

    return prediction

# Streamlit app
def main():
    # Set app title and layout
    st.title("Pneumonia Detection")
    st.markdown("---")

    # Add an image uploader
    st.header("Upload Chest X-ray Image")
    uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        # Display the uploaded image
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Image", use_column_width=True)

        # Make prediction when the user clicks the 'Predict' button
        if st.button("Predict"):
            # Perform prediction
            prediction = predict_pneumonia(image)

            # Display the prediction
            if prediction == 1:
                st.error("Prediction: Pneumonia detected")
            else:
                st.success("Prediction: No pneumonia detected")

# Run the app
if __name__ == '__main__':
    main()