Update app.py

app.py

@@ -1,12 +1,10 @@
 !pip install tensorflow
+
+# Import necessary libraries
 import streamlit as st
 import tensorflow as tf
 from PIL import Image
 import numpy as np
-import keras
-
-# Load the TensorFlow model from the .h5 file
-model = tf.keras.models.load_model("model.h5")
 
 # Create a Streamlit app
 st.title("Brain Tumor Detection")
@@ -14,22 +12,23 @@ st.title("Brain Tumor Detection")
 # Upload an image
 image = st.file_uploader("Upload an MRI image of a brain with a tumor", type=["jpg", "jpeg", "png"])
 
-# Button to make predictions
-if image is not None:
-    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")
+# Check if TensorFlow is available
+if 'tensorflow' not in sys.modules:
+    st.warning("TensorFlow is not available in this environment. Please ensure that you have the correct environment activated.")
+
+else:
+    # Load the TensorFlow model from the .h5 file
+    model = tf.keras.models.load_model("model.h5")
+
+    # Button to make predictions
+    if image is not None:
+        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