app.py

# Import necessary libraries
import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np

# Create a Streamlit app
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"])

# 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