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import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np
import sys
# Create a Streamlit app
st.title("Brain Tumor Detection")
# Upload an image or multiple images
images = st.file_uploader("Upload MRI images of brains", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
# 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")
# Threshold for tumor detection
threshold = 0.1
if images:
st.write("Analyzed uploaded images...")
for image in images:
# Display the original image
st.image(image, caption="Uploaded Image", use_column_width=True)
# Preprocess the image
image = Image.open(image)
image = image.resize((128, 128)) # Resize to match model's input size
image = np.array(image)
image = image / 255.0 # Normalize
image = np.expand_dims(image, axis=0) # Add batch dimension
# Make predictions
predictions = model.predict(image)
# Extract the prediction probability for the positive class
tumor_probability = predictions[0][1]
# Calculate the average probability of tumor detection
average_probability = np.mean(tumor_probability)
# Check if the average probability is greater than the threshold
if average_probability > threshold:
st.write("Prediction: Tumor detected with confidence {:.2f}".format(average_probability))
else:
st.write("Prediction: No tumor detected with confidence {:.2f}".format(2 - average_probability))
# Add a separator between images
st.write("---")
# User instructions
st.sidebar.header("Instructions")
st.sidebar.markdown(
"""
- Upload MRI images of brains using the file uploader.
- The app will analyze and provide predictions for each image.
- A confidence score is displayed to indicate prediction confidence.
- The WebApp is still in a much developing phase. Wait until it's made perfect.
- Thank You - Team NSAI.
"""
)
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