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import streamlit as st
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np
import os
# Set page config
st.set_page_config(
page_title="Brain Tumor Detection",
page_icon="🧠",
layout="centered"
)
# Load the trained model
try:
MODEL_PATH = 'model.h5'
if not os.path.exists(MODEL_PATH):
st.error("Model file not found. Please ensure model.h5 exists in the 'models' directory")
st.stop()
model = load_model(MODEL_PATH)
except Exception as e:
st.error(f"Error loading model: {str(e)}")
st.stop()
# Class labels
class_labels = ['pituitary', 'glioma', 'notumor', 'meningioma']
# Helper function to predict tumor type
def predict_tumor(image):
IMAGE_SIZE = 128
img = load_img(image, target_size=(IMAGE_SIZE, IMAGE_SIZE))
img_array = img_to_array(img) / 255.0 # Normalize pixel values
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
predictions = model.predict(img_array)
predicted_class_index = np.argmax(predictions, axis=1)[0]
confidence_score = np.max(predictions, axis=1)[0]
if class_labels[predicted_class_index] == 'notumor':
return "No Tumor", confidence_score
else:
return f"Tumor: {class_labels[predicted_class_index]}", confidence_score
# Main UI
st.title("Brain Tumor Detection")
st.write("Upload an MRI scan to detect the presence and type of brain tumor")
# File uploader
uploaded_file = st.file_uploader("Choose an MRI image file", type=['jpg', 'jpeg', 'png'])
if uploaded_file is not None:
# Display the uploaded image
st.image(uploaded_file, caption="Uploaded MRI Scan", use_container_width=True)
# Add a predict button
if st.button("Predict"):
with st.spinner("Analyzing image..."):
# Make prediction
result, confidence = predict_tumor(uploaded_file)
# Display results
st.success("Analysis Complete!")
st.write(f"**Prediction:** {result}")
st.write(f"**Confidence:** {confidence*100:.2f}%")
# Display additional information based on the result
if "No Tumor" not in result:
st.warning("Please consult with a healthcare professional for proper medical advice.")
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