Update app.py

app.py

@@ -2,29 +2,42 @@ import streamlit as st
 import numpy as np
 import tensorflow as tf
 from PIL import Image
+import cv2 as cv
 import io
 
-# Load your pre-trained model
+# Load trained model
 model = tf.keras.models.load_model('model.keras')
 
 # Define the image preprocessing function
-# Define the image preprocessing function
-def preprocess_image(image):
-    # Convert image to RGB if it's grayscale
-    if image.mode != 'RGB':
-        image = image.convert('RGB')
-    
-    # Resize and preprocess the image
-    image = image.resize((224, 224))  # Adjust the size as per your model's requirement
-    image_array = np.array(image) / 255.0  # Normalize the image
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+def preprocess_image(image):    
+    # Convert to numpy array
+    image_array = np.array(image)
+    
+    # Apply Gaussian Blur
+    image_array = cv.GaussianBlur(image_array, (9, 9), 0)
+    
+    # Apply CLAHE
+    clahe = cv.createCLAHE(clipLimit=3, tileGridSize=(10, 10))
+    clahe_image = clahe.apply(image_array)
+    
+    # Convert CLAHE image to RGB
+    clahe_image = cv.cvtColor(clahe_image, cv.COLOR_GRAY2RGB)
+    
+    # Normalize image to [0, 1]
+    clahe_image = (clahe_image - clahe_image.min()) / (clahe_image.max() - clahe_image.min())
+    
+    # Resize the image to 224x224
+    image_resized = cv.resize(clahe_image, (224, 224))
+    
+    # Add batch dimension
+    image_array = np.expand_dims(image_resized, axis=0).astype(np.float32)
+    
     return image_array
 
 # Define the class labels
 class_labels = ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion', 
                  'Emphysema', 'Fibrosis', 'Infiltration', 'Mass', 
                  'Nodule', 'Pleural_Thickening', 'Pneumothorax']
-# class_labels = ["Class1", "Class2", "Class3", "Class4", "Class5"]  # Update with actual class names
 
 # Streamlit app
 st.title("Chest X-ray Classification")
@@ -54,7 +67,7 @@ if uploaded_file is not None:
     with col2:
         # Display results
         if not top_predictions:
-            st.write("No any diseases found with probability greater than 50%.")
+            st.write("No diseases found with probability greater than 50%.")
         else:
             st.write("Predicted Disease(s):")
             for label, prob in top_predictions: