Update app.py

app.py

@@ -1,119 +1,3 @@
-# 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
@@ -121,15 +5,40 @@ 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>
-    """
+                <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
@@ -164,62 +73,26 @@ def display_image_with_download(image_path, caption, download_text):
 
 # Streamlit app
 def main():
-    # Set app title and page icon
-    st.set_page_config(
+
+     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")
+        
+    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", key="fileUploader")
+    uploaded_file = st.file_uploader("Upload a chest X-ray image in JPG format...", type="jpg")
 
     # Example instructions
     st.markdown("""
-        **Example Instructions:**
+        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
+   # Provide links to download sample images
     st.write("**Download Sample Images:**")
     
     pneumonic_download = st.button("Download Pneumonic Image")
@@ -234,10 +107,7 @@ def main():
         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)
@@ -246,6 +116,7 @@ def main():
         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()
+