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app.py

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+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()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3dd424537b91fcf72a6272ec4b4007e045a9076cd863f3c8dbea448caf72e222
+size 777579104

requirements.txt

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+streamlit
+pillow
+numpy
+tensorflow