Update app.py

app.py

@@ -6,114 +6,70 @@ from PIL import Image
 import base64
 
 st.set_page_config(
-        page_title="Pneumonia Detection App",
-        page_icon=":microscope:",
-        layout="wide"
-    )
+    page_title="Pneumonia Detection App",
+    page_icon=":microscope:",
+    layout="wide"
+)
 
 custom_style = """
-                <style>
-                .stApp {
-                    max-width: 1200px;  /* Set maximum width for the app content */
-                    margin: 0 auto;  /* Center the app content */
-                    padding: 20px;  /* Add padding for better readability */
-                }
-                body {
-                    color: white;
-                    background-color: #2C3E50;  
-                }
-                .stButton>button {
-                    background-color: #3498db !important;
-                    color: white !important;
-                    border-radius: 5px !important;
-                }
-                .download-buttons-container {
-                    display: flex;  /* Use flexbox for the container */
-                    gap: 10px;  /* Add space between buttons */
-                }
-                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>
-                """
+<style>
+    .stApp {
+        max-width: 1200px;
+        margin: 0 auto;
+        padding: 20px;
+    }
+    body {
+        color: white;
+        background-color: #2C3E50;
+    }
+    .stButton>button {
+        background-color: #3498db !important;
+        color: white !important;
+        border-radius: 5px !important;
+    }
+    .download-buttons-container {
+        display: flex;
+        gap: 10px;
+    }
+</style>
+"""
 st.markdown(custom_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
+model = tf.keras.models.load_model('model.h5')
 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 = image.img_to_array(img) / 255.0
     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)
+    prediction = np.squeeze(model.predict(img_array), 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:**")
     
     with st.markdown('<div class="download-buttons-container">', unsafe_allow_html=True):
@@ -121,152 +77,22 @@ def main():
         normal_download = st.button("Download Normal Image")
 
     if pneumonic_download:
-        pneumonic_image_path = "test-pneumonia_028.jpg"  # Replace with actual path
+        pneumonic_image_path = "test-pneumonia_028.jpg"
         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
+        normal_image_path = "test-normal_001.jpg"
         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)
         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()
-
-# import streamlit as st
-# import tensorflow as tf
-# from tensorflow.keras.preprocessing import image
-# import numpy as np
-# from PIL import Image
-# import base64
-
-# st.set_page_config(
-#     page_title="Pneumonia Detection App",
-#     page_icon=":microscope:",
-#     layout="wide"
-# )
-
-# custom_style = """
-#     <style>
-#         body {
-#             color: white;
-#             background-color: #2C3E50;  
-#         }
-#         .stButton>button {
-#             background-color: #3498db !important;
-#             color: white !important;
-#             border-radius: 5px !important;
-#         }
-#         .download-buttons-container {
-#             display: flex;  /* Use flexbox for the container */
-#             gap: 10px;  /* Add space between buttons */
-#             margin-bottom: 20px;  /* Add margin for better spacing */
-#         }
-#         div[data-testid="stToolbar"],
-#         div[data-testid="stDecoration"],
-#         div[data-testid="stStatusWidget"],
-#         #MainMenu,
-#         header,
-#         footer {
-#             visibility: hidden;
-#             height: 0%;
-#         }
-#     </style>
-# """
-# st.markdown(custom_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:**")
-
-#     # Container for download buttons
-#     col1, col2 = st.beta_columns(2)
-
-#     with col1:
-#         pneumonic_download = st.button("Download Pneumonic Image")
-
-#     with col2:
-#         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()