Upload app.py

app.py

@@ -5,7 +5,7 @@ from PIL import Image
 import io
 
 # Load your pre-trained model
-model = tf.keras.models.load_model('model.keras')
+model = tf.keras.models.load_model('model_2.keras')
 
 # Define the image preprocessing function
 # Define the image preprocessing function
@@ -27,17 +27,19 @@ class_labels = ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusi
 # class_labels = ["Class1", "Class2", "Class3", "Class4", "Class5"]  # Update with actual class names
 
 # Streamlit app
-st.title("Image Classification with Grad-CAM")
-
-st.write("Upload an image to get predictions:")
+st.title("Chest X-ray Classification")
 
 # Upload image
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+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)
-    st.image(image, caption='Uploaded Image', use_column_width=True)
+    with col1:
+        st.image(image, caption='Uploaded Image', use_column_width=True)
     
     # Preprocess the image
     preprocessed_image = preprocess_image(image)
@@ -49,13 +51,13 @@ if uploaded_file is not None:
     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]
     
-    # Display results
-    st.write("Predictions with probability greater than 0.5:")
-    for label, prob in top_predictions:
-        st.write(f"{label}: {prob*100:.2f}%")
-        percentage = int(prob * 100)
-        # st.progress(prob)
-        st.progress(percentage)
-
-    if not top_predictions:
-        st.write("No predictions with probability greater than 0.5.")
+    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)