Upload 3 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+my_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,49 @@
+#import library
+import pandas as pd
+import numpy as np
+import streamlit as st
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import tensorflow_hub as hub
+
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+#import pickle
+import pickle
+
+#load model
+def run():
+    file = st.file_uploader("Upload an image", type=["jpg", "png"])
+
+    model = load_model('my_model.keras', custom_objects={'KerasLayer': hub.KerasLayer})
+    target_size=(224, 224)
+
+    def import_and_predict(image_data, model):
+        image = load_img(image_data, target_size=(224, 224))
+        img_array = img_to_array(image)
+        img_array = tf.expand_dims(img_array, 0)  # Create a batch
+
+        # Normalize the image
+        img_array = img_array / 255.0
+
+        # Make prediction
+        predictions = model.predict(img_array)
+
+        # Get the class with the highest probability
+        idx = np.where(predictions >= 0.5, 1, 0).item()
+        # predicted_class = np.argmax(predictions)
+
+        jenis = ['Brain Tumor', 'Healthy']
+        result = f"Prediction: {jenis[idx]}"
+
+        return result
+
+    if file is None:
+        st.text("Please upload an image file")
+    else:
+        result = import_and_predict(file, model)
+        st.image(file)
+        st.write(result)
+        
+if __name__ == "__main__":
+    run()

my_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68cf6196d2a11321bfaa734b9b8b1f7b08fa4e59df83b63669c973f7961a033e
+size 27066361

requirements.txt

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+streamlit
+pandas
+numpy
+scikit.learn==1.3.0
+tensorflow