| # import streamlit as st | |
| # import tensorflow as tf | |
| # from tensorflow.keras.preprocessing import image | |
| # import numpy as np | |
| # from PIL import Image | |
| # import base64 | |
| # hide_streamlit_style = """ | |
| # <style> | |
| # div[data-testid="stToolbar"] { | |
| # visibility: hidden; | |
| # height: 0%; | |
| # position: fixed; | |
| # } | |
| # div[data-testid="stDecoration"] { | |
| # visibility: hidden; | |
| # height: 0%; | |
| # position: fixed; | |
| # } | |
| # div[data-testid="stStatusWidget"] { | |
| # visibility: hidden; | |
| # height: 0%; | |
| # position: fixed; | |
| # } | |
| # #MainMenu { | |
| # visibility: hidden; | |
| # height: 0%; | |
| # } | |
| # header { | |
| # visibility: hidden; | |
| # height: 0%; | |
| # } | |
| # footer { | |
| # visibility: hidden; | |
| # height: 0%; | |
| # } | |
| # </style> | |
| # """ | |
| # st.markdown(hide_streamlit_style, unsafe_allow_html=True) | |
| # # Load the pre-trained model | |
| # model = tf.keras.models.load_model('model.h5') | |
| # # Define the target size for the model | |
| # img_size = (224, 224) | |
| # # Function to preprocess the image | |
| # def preprocess_image(img): | |
| # img = image.load_img(img, target_size=img_size) | |
| # img_array = image.img_to_array(img) | |
| # img_array = img_array / 255.0 # Normalize pixel values to between 0 and 1 | |
| # img_array = np.expand_dims(img_array, axis=0) | |
| # return img_array | |
| # # Function to make predictions | |
| # def predict_image(img): | |
| # img_array = preprocess_image(img) | |
| # prediction = model.predict(img_array) | |
| # prediction = np.squeeze(prediction, axis=0) | |
| # return prediction | |
| # # Function to display and provide a download link for an image | |
| # def display_image_with_download(image_path, caption, download_text): | |
| # image = Image.open(image_path) | |
| # st.image(image, caption=caption, use_column_width=True) | |
| # # Generate a download link | |
| # with open(image_path, 'rb') as f: | |
| # data = f.read() | |
| # base64_data = base64.b64encode(data).decode('utf-8') | |
| # href = f'<a href="data:application/octet-stream;base64,{base64_data}" download="{download_text}.jpg">Download {download_text}</a>' | |
| # st.markdown(href, unsafe_allow_html=True) | |
| # # Streamlit app | |
| # def main(): | |
| # st.title("Pneumonia Detection") | |
| # # Allow user to upload an image | |
| # uploaded_file = st.file_uploader("Upload a chest X-ray image in JPG format...", type="jpg") | |
| # # Example instructions | |
| # st.markdown(""" | |
| # Example Instructions: | |
| # - Upload a chest X-ray image in JPG format. | |
| # - Or, download sample images below and check the predictions. | |
| # """) | |
| # # Provide links to download sample images | |
| # st.write("**Download Sample Images:**") | |
| # pneumonic_download = st.button("Download Pneumonic Image") | |
| # normal_download = st.button("Download Normal Image") | |
| # if pneumonic_download: | |
| # pneumonic_image_path = "test-pneumonia_028.jpg" # Replace with actual path | |
| # display_image_with_download(pneumonic_image_path, "Pneumonic Image", "Pneumonic Image") | |
| # if normal_download: | |
| # normal_image_path = "test-normal_001.jpg" # Replace with actual path | |
| # display_image_with_download(normal_image_path, "Normal Image", "Normal Image") | |
| # if uploaded_file is not None: | |
| # st.image(uploaded_file, caption="Uploaded Image", use_column_width=True) | |
| # # Make predictions | |
| # prediction = predict_image(uploaded_file) | |
| # # Display the results | |
| # st.write("**Prediction:**") | |
| # class_label = "Pneumonia" if prediction > 0.5 else "Normal" | |
| # st.write(f"The image is classified as **{class_label}**.") | |
| # if __name__ == "__main__": | |
| # main() | |
| import streamlit as st | |
| import tensorflow as tf | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| from PIL import Image | |
| import base64 | |
| # Hide Streamlit menu and footer | |
| hide_streamlit_style = """ | |
| <style> | |
| #MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hide_streamlit_style, unsafe_allow_html=True) | |
| # Load the pre-trained model | |
| model = tf.keras.models.load_model('model.h5') | |
| # Define the target size for the model | |
| img_size = (224, 224) | |
| # Function to preprocess the image | |
| def preprocess_image(img): | |
| img = image.load_img(img, target_size=img_size) | |
| img_array = image.img_to_array(img) | |
| img_array = img_array / 255.0 # Normalize pixel values to between 0 and 1 | |
| img_array = np.expand_dims(img_array, axis=0) | |
| return img_array | |
| # Function to make predictions | |
| def predict_image(img): | |
| img_array = preprocess_image(img) | |
| prediction = model.predict(img_array) | |
| prediction = np.squeeze(prediction, axis=0) | |
| return prediction | |
| # Function to display and provide a download link for an image | |
| def display_image_with_download(image_path, caption, download_text): | |
| image = Image.open(image_path) | |
| st.image(image, caption=caption, use_column_width=True) | |
| # Generate a download link | |
| with open(image_path, 'rb') as f: | |
| data = f.read() | |
| base64_data = base64.b64encode(data).decode('utf-8') | |
| href = f'<a href="data:application/octet-stream;base64,{base64_data}" download="{download_text}.jpg">Download {download_text}</a>' | |
| st.markdown(href, unsafe_allow_html=True) | |
| # Streamlit app | |
| def main(): | |
| # Set app title and page icon | |
| st.set_page_config( | |
| page_title="Pneumonia Detection App", | |
| page_icon=":microscope:", | |
| layout="wide" | |
| ) | |
| # Add custom CSS for styling | |
| st.markdown(""" | |
| <style> | |
| body { | |
| background-color: #f5f5f5; | |
| } | |
| .st-bw { | |
| background-color: #ffffff; | |
| box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); | |
| border-radius: 10px; | |
| padding: 20px; | |
| margin-bottom: 20px; | |
| } | |
| .st-bw img { | |
| max-width: 100%; | |
| border-radius: 10px; | |
| } | |
| .st-bw a { | |
| color: #007bff; | |
| } | |
| .st-bw button { | |
| background-color: #007bff; | |
| color: #ffffff; | |
| border: none; | |
| padding: 10px 20px; | |
| font-size: 16px; | |
| border-radius: 5px; | |
| cursor: pointer; | |
| } | |
| .st-bw button:hover { | |
| background-color: #0056b3; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Display app title | |
| st.title("Pneumonia Detection App") | |
| # Allow user to upload an image | |
| uploaded_file = st.file_uploader("Upload a chest X-ray image in JPG format...", type="jpg", key="fileUploader") | |
| # Example instructions | |
| st.markdown(""" | |
| **Example Instructions:** | |
| - Upload a chest X-ray image in JPG format. | |
| - Or, download sample images below and check the predictions. | |
| """) | |
| # Provide links to download sample images | |
| st.write("**Download Sample Images:**") | |
| pneumonic_download = st.button("Download Pneumonic Image") | |
| normal_download = st.button("Download Normal Image") | |
| if pneumonic_download: | |
| pneumonic_image_path = "test-pneumonia_028.jpg" # Replace with actual path | |
| display_image_with_download(pneumonic_image_path, "Pneumonic Image", "Pneumonic Image") | |
| if normal_download: | |
| normal_image_path = "test-normal_001.jpg" # Replace with actual path | |
| display_image_with_download(normal_image_path, "Normal Image", "Normal Image") | |
| if uploaded_file is not None: | |
| # Display the uploaded image in a styled container | |
| st.markdown('<div class="st-bw">', unsafe_allow_html=True) | |
| st.image(uploaded_file, caption="Uploaded Image", use_column_width=True) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| # Make predictions | |
| prediction = predict_image(uploaded_file) | |
| # Display the results | |
| st.write("**Prediction:**") | |
| class_label = "Pneumonia" if prediction > 0.5 else "Normal" | |
| st.write(f"The image is classified as **{class_label}**.") | |
| if __name__ == "__main__": | |
| main() | |