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| import streamlit as st | |
| from tensorflow.keras.models import load_model | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| from PIL import Image | |
| model = load_model("glaucoma_classifier_m_EfficientNetB0.h5") | |
| st.title("🧠 Glaucoma Detection App") | |
| st.write("Upload a retinal image to check for signs of glaucoma.") | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"]) | |
| if uploaded_file is not None: | |
| img = Image.open(uploaded_file) | |
| st.image(img, caption='Uploaded Image', use_column_width=True) | |
| img = img.resize((224, 224)) | |
| img_array = image.img_to_array(img) | |
| img_array = np.expand_dims(img_array, axis=0) / 255.0 | |
| prediction = model.predict(img_array)[0][0] | |
| if prediction >= 0.5: | |
| st.error("⚠️ Glaucoma Detected") | |
| else: | |
| st.success("✅ No Glaucoma Detected") | |