| import streamlit as st | |
| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| def load_model(): | |
| model = tf.keras.models.load_model('cnn_tumor2.h5') | |
| return model | |
| model = load_model() | |
| def preprocess_image(image): | |
| image = image.resize((128,128)) | |
| image = np.array(image) / 255.0 | |
| image = np.expand_dims(image, axis=0) | |
| return image | |
| def predict_tumor(image): | |
| predictions = model.predict(image) | |
| if predictions[0] > 0.5: | |
| return "Tumorous" | |
| else: | |
| return "Non-tumorous" | |
| with open("styles.css") as f: | |
| st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True) | |
| st.title("Tumor Detection using CNN") | |
| uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| resized_image = image.resize((300, 200)) | |
| st.image(resized_image, caption="Uploaded Image") | |
| processed_image = preprocess_image(image) | |
| result = predict_tumor(processed_image) | |
| st.info(f"Prediction: {result}") | |