Update app.py

app.py

@@ -1,42 +1,48 @@
-import streamlit as st
-from PIL import Image
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.preprocessing import image
-
-# Function to preprocess the uploaded image
-def preprocess_uploaded_image(uploaded_image, target_size):
-    img = Image.open(uploaded_image)
-    img = img.resize(target_size)
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    return img_array
-
-# Function to load the model and make predictions
-def predict_image_class(model_path, uploaded_image, target_size):
-    loaded_model = tf.keras.models.load_model(model_path)
-    img = preprocess_uploaded_image(uploaded_image, target_size)
-    prediction = loaded_model.predict(img)
-    class_idx = np.argmax(prediction)
-    return class_idx
-
-def main():
-    st.title("Heart Disease Image Classifier")
-    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-    if uploaded_image is not None:
-        st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
-        st.write("")
-        with st.spinner("Classifying..."):
-            # Classify the uploaded image
-            class_idx = predict_image_class("model.h5", uploaded_image, target_size=(224, 224))
-
-        if class_idx == 0:
-            st.write("The patient doesn't have heart disease")
-        else:
-            st.write("The patient has heart disease")
-
-
-# Run the Streamlit app
-if __name__ == "__main__":
-    main()
+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+
+# Function to preprocess the uploaded image
+def preprocess_uploaded_image(uploaded_image, target_size):
+    img = Image.open(uploaded_image)
+    img = img.resize(target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# Function to load the model and make predictions
+def predict_image_class(model_path, uploaded_image, target_size):
+    try:
+        loaded_model = tf.keras.models.load_model(model_path)
+        img = preprocess_uploaded_image(uploaded_image, target_size)
+        prediction = loaded_model.predict(img)
+        class_idx = np.argmax(prediction)
+        return class_idx
+    except Exception as e:
+        st.error(f"Error loading the model: {e}")
+        return None
+
+def main():
+    st.title("Heart Disease Image Classifier")
+    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_image is not None:
+        st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+        st.write("")
+        with st.spinner("Classifying..."):
+            # Classify the uploaded image
+            class_idx = predict_image_class("model.h5", uploaded_image, target_size=(224, 224))
+
+        if class_idx is not None:
+            if class_idx == 0:
+                st.write("The patient doesn't have heart disease")
+            else:
+                st.write("The patient has heart disease")
+        else:
+            st.error("Failed to classify the image. Please try again.")
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    main()