Update app.py

app.py

@@ -1,23 +1,33 @@
 import streamlit as st
-import os
+import tensorflow as tf
+from PIL import Image
+import numpy as np
 
-# Load the model
-model = keras.models.load_model("./model.h5")
-
-# Create a function to make predictions
-def predict(image):
-    prediction = model.predict(image)
-    return prediction
+# Load the TensorFlow model from the .h5 file
+model = tf.keras.models.load_model("model.h5")
 
 # Create a Streamlit app
-st.title("Bone Fracture Detection")
+st.title("Brain Tumor Detection")
 
 # Upload an image
-image = st.file_uploader("Upload an image of a bone fracture")
+image = st.file_uploader("Upload an MRI image of a brain with a tumor", type=["jpg", "jpeg", "png"])
 
-# If an image is uploaded, make a prediction
+# Button to make predictions
 if image is not None:
-    image = image.read()
-    prediction = predict(image)
-    st.write("The prediction is:", prediction)
+    image = Image.open(image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess the image
+    image = image.resize((224, 224))  # Adjust the size according to your model's input requirements
+    image = np.array(image)
+    image = image / 255.0  # Normalize the image to [0, 1]
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+    # Make predictions
+    prediction = model.predict(image)
 
+    # Display prediction results
+    if prediction > 0.5:
+        st.write("Prediction: Tumor detected")
+    else:
+        st.write("Prediction: No tumor detected")