Upload 4 files

app.py

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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")

glaucoma_classifier_m_EfficientNetB0.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c59c6367916ea5a8759831cc81d6abca20af4f81dc7bd58c41b8381d776955a6
+size 51169808

glaucoma_classifier_m_custom_built.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:81eebdc96578f8cc03fac6e6177dd302a24c2c034117a76931a5436b5a498f9b
+size 134113064

requirements.txt

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+streamlit
+tensorflow
+Pillow