app.py

import streamlit as st
import numpy as np
import tensorflow as tf
from PIL import Image  # Required for some Streamlit versions
import matplotlib.pyplot as plt

# Example: Import TensorFlow model
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions

# Example: Load ResNet50 model
model = ResNet50(weights='imagenet')

# Example: Function to preprocess image for ResNet50
def preprocess_image(img):
    img = tf.image.resize(img, (224, 224))  # Resize to model input size
    img = preprocess_input(img)  # Preprocess according to model requirements
    return img.numpy()

# Example: Function to perform deep learning-based analysis
def analyze_image(image):
    # Preprocess image
    processed_img = preprocess_image(image)

    # Predict using the model
    predictions = model.predict(np.expand_dims(processed_img, axis=0))
    label = decode_predictions(predictions, top=1)[0][0]

    return label

# Main Streamlit application
def main():
    st.title('Deep Learning-Based PET Image Analysis')

    # Sidebar for file upload and parameters
    uploaded_file = st.file_uploader('Upload PET Image', type=['png', 'jpg', 'dicom'])

    if uploaded_file is not None:
        # Display uploaded image
        image = np.array(Image.open(uploaded_file))
        st.subheader('Uploaded PET Image')
        st.image(image, caption='Uploaded PET Image', use_column_width=True)

        # Perform deep learning-based analysis
        if st.button('Analyze'):
            prediction = analyze_image(image)
            st.subheader('Analysis Result')
            st.write(f'Prediction: {prediction}')

if __name__ == '__main__':
    main()