app.py

import streamlit as st
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing.image import load_img, img_to_array

# Header
st.header('X-ray Chest Syptoms Classification')

# Input user
image_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if image_file is not None:
    # Preprocess input image
    def preprocess_image(image_file):
        image = load_img(image_file, target_size=(224, 224))
        image = img_to_array(image)
        image = preprocess_input(image)
        image = np.expand_dims(image, axis=0)
        return image

    preprocessed_image = preprocess_image(image_file)

    # Load model
    model = tf.keras.models.load_model('./model.hdf5')

    if preprocessed_image is not None:
        # Make prediction
        prediction = model.predict(preprocessed_image)
        predicted_class = np.argmax(prediction)
        class_labels = ['COVID19', 'NORMAL', 'PNEUMONIA', 'TURBERCULOSIS']
        # Display the prediction
        st.subheader("Prediction:")
        st.write(f"Predicted Class: {class_labels[predicted_class]}")
        st.write(f"Confidence: {prediction[0][predicted_class]:.2f}")