Update app.py

app.py

@@ -1,63 +1,63 @@
-import streamlit as st
-import numpy as np
-import tensorflow as tf
-from PIL import Image
-import io
-
-# Load your pre-trained model
-model = tf.keras.models.load_model('model_2.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
-    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")
-
-# Upload image
-uploaded_file = st.file_uploader("Upload a Chest X-ray image...", type=["jpg", "jpeg", "png"])
-
-# Create two columns
-col1, col2 = st.columns(2)
-
-if uploaded_file is not None:
-    # Read and display the image
-    image = Image.open(uploaded_file)
-    with col1:
-        st.image(image, caption='Uploaded Image', use_column_width=True)
-    
-    # Preprocess the image
-    preprocessed_image = preprocess_image(image)
-    
-    # Make predictions
-    predictions = model.predict(preprocessed_image)[0]
-    
-    # Get top 3 predictions with probability greater than 0.5
-    top_predictions = [(label, prob) for label, prob in zip(class_labels, predictions) if prob > 0.5]
-    top_predictions = sorted(top_predictions, key=lambda x: x[1], reverse=True)[:3]
-    
-    with col2:
-        # Display results
-        if not top_predictions:
-            st.write("No any diseases found with probability greater than 50%.")
-        else:
-            st.write("Predicted Disease(s):")
-            for label, prob in top_predictions:
-                st.write(f"{label}: {prob*100:.2f}%")
-                percentage = int(prob * 100)
+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import io
+
+# Load your pre-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
+    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")
+
+# Upload image
+uploaded_file = st.file_uploader("Upload a Chest X-ray image...", type=["jpg", "jpeg", "png"])
+
+# Create two columns
+col1, col2 = st.columns(2)
+
+if uploaded_file is not None:
+    # Read and display the image
+    image = Image.open(uploaded_file)
+    with col1:
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Preprocess the image
+    preprocessed_image = preprocess_image(image)
+    
+    # Make predictions
+    predictions = model.predict(preprocessed_image)[0]
+    
+    # Get top 3 predictions with probability greater than 0.5
+    top_predictions = [(label, prob) for label, prob in zip(class_labels, predictions) if prob > 0.5]
+    top_predictions = sorted(top_predictions, key=lambda x: x[1], reverse=True)[:3]
+    
+    with col2:
+        # Display results
+        if not top_predictions:
+            st.write("No any diseases found with probability greater than 50%.")
+        else:
+            st.write("Predicted Disease(s):")
+            for label, prob in top_predictions:
+                st.write(f"{label}: {prob*100:.2f}%")
+                percentage = int(prob * 100)
                 st.progress(percentage)