updated app.py

.idea/.name

@@ -0,0 +1 @@
+app.py

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12 (1. Two Sum)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (CNN_Pneumonia_detection)" project-jdk-type="Python SDK" />
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app.py

@@ -1,5 +1,8 @@
 import streamlit as st
 import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+import tensorflow.keras.applications.resnet_v2 as resnet_v2
 import torch
 import torch.nn as nn
 import numpy as np
@@ -51,12 +54,10 @@ if uploaded_file is not None:
 
     else:  # TensorFlow
         # Convert Image to NumPy and Normalize
-        img_resized = image.resize((image_size, image_size))
-        image_array = np.array(img_resized) / 255.0
-        image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+        image_preprocessed = resnet_v2.preprocess_input(image)
 
         # Make Prediction
-        prediction = tf_model.predict(image_array)[0][0]  # Extract single value
+        prediction = tf_model.predict(image_preprocessed)[0][0]  # Extract single value
 
     # Display Prediction
     st.header(
@@ -64,5 +65,5 @@ if uploaded_file is not None:
         divider='blue'
     )
     # Display Uploaded Image
-    st.image(image, caption="Uploaded X-ray", use_container_width=True)
+    st.image(image, caption="Uploaded image", use_container_width=True)