Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,49 @@
-import altair as alt
-import numpy as np
+#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
+from tensorflow_hub.keras_layer import KerasLayer 
+
+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': 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
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    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()