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# 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()
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