Update app.py

app.py

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+import streamlit as st
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+from transformers import AutoImageProcessor, Swinv2ForImageClassification
+
+# Page setup
+st.set_page_config(page_title="Glaucoma Detector", layout="centered")
+st.title("👁️ Glaucoma Detection App")
+st.write("Upload a **fundus image** to check for signs of glaucoma using a pre-trained deep learning model.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose a fundus image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    with st.spinner("🧠 Analyzing the image..."):
+        image_np = np.array(image)
+
+        processor = AutoImageProcessor.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification")
+        model = Swinv2ForImageClassification.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification")
+
+        inputs = processor(image_np, return_tensors="pt")
+        with torch.no_grad():
+            logits = model(**inputs).logits
+
+        predicted_label = logits.argmax(-1).item()
+        label = model.config.id2label[predicted_label]
+
+        st.success(f"✅ **Prediction:** {label}")