Create app.py

app.py

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+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}")