Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import img_to_array
+import numpy as np
+
+path_to_model = ""
+
+# Load the trained model
+model = tf.keras.models.load_model(path_to_model)
+
+# Set the default height and width of the uploaded image
+IMG_HEIGHT = 180
+IMG_WIDTH = 180
+
+# Define a function to preprocess the image
+def preprocess_image(image):
+    # Resize the image to the expected input size of the model
+    img = image.resize((IMG_HEIGHT, IMG_WIDTH))
+    # Convert the image to a numpy array
+    img_array = img_to_array(img)
+    # Normalize the pixel values
+    img_array = img_array / 255.0
+    # Reshape the image array to match the input shape of the model
+    img_array = np.reshape(img_array, (1, IMG_HEIGHT, IMG_WIDTH, 3))
+    return img_array
+
+# Create the Streamlit app
+def main():
+    # Set the title and a brief description
+    st.title('Pneumonia Classification')
+    st.write('This app classifies an uploaded chest X-ray image as normal or pneumonia using a trained deep learning model.')
+    
+    # Add an image upload option
+    uploaded_image = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
+    
+    # If an image has been uploaded, display it and make a prediction
+    if uploaded_image is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_image)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        
+        # Preprocess the image for the model
+        input_image = preprocess_image(image)
+        
+        # Make a prediction using the model
+        prediction = model.predict(input_image)
+        
+        # Display the prediction
+        if prediction[0][0] > prediction[0][1]:
+            st.write('Prediction: Normal')
+        else:
+            st.write('Prediction: Pneumonia')
+
+if __name__ == '__main__':
+    main()