Upload app.py

app.py

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+# Copyright (C) 2023, Xu Sun.
+
+# This program is licensed under the Apache License version 2.
+# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.
+
+import torch
+import numpy as np
+
+import matplotlib.pyplot as plt
+import streamlit as st
+
+from PIL import Image
+from lib.glaucoma import GlaucomaModel
+
+run_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+def main():
+    # Wide mode
+    st.set_page_config(layout="wide")
+
+    # Designing the interface
+    st.title("Glaucoma Screening from Retinal Fundus Images")
+    # For newline
+    st.write('\n')
+    # Author info
+    st.write('Developed by X. Sun. Find more info about me: https://pamixsun.github.io')
+    # For newline
+    st.write('\n')
+    # Instructions
+    st.markdown("*Hint: click on the top-right corner of an image to enlarge it!*")
+    # Set the columns
+    cols = st.beta_columns((1, 1))
+    cols[0].subheader("Input image")
+    cols[1].subheader("Class activation map")
+    
+    # set the visualization figure
+    fig, ax = plt.subplots()
+
+    # Sidebar
+    # File selection
+    st.sidebar.title("Image selection")
+    # Disabling warning
+    st.set_option('deprecation.showfileUploaderEncoding', False)
+    # Choose your own image
+    uploaded_file = st.sidebar.file_uploader("Upload image", type=['png', 'jpeg', 'jpg'])
+    if uploaded_file is not None:
+        # read the upload image
+        image = Image.open(uploaded_file).convert('RGB')
+        image = np.array(image).astype(np.uint8)
+        # page_idx = 0
+        ax.imshow(image)
+        ax.axis('off')
+        cols[0].pyplot(fig)
+
+    # For newline
+    st.sidebar.write('\n')
+
+    # actions
+    if st.sidebar.button("Analyze image"):
+
+        if uploaded_file is None:
+            st.sidebar.write("Please upload an image")
+
+        else:
+            with st.spinner('Loading model...'):
+                # load model
+                model = GlaucomaModel(device=run_device)
+
+            with st.spinner('Analyzing...'):
+                # Forward the image to the model and get results
+                disease_idx, cam = model.process(image)
+                
+                # visualize results
+                # fig, ax = plt.subplots()
+
+                # plot the stitched image
+                ax.imshow(cam)
+                ax.axis('off')
+                cols[1].pyplot(fig)
+
+                # Display JSON
+                st.subheader("  Screening results:")
+                st.write('\n')
+                st.markdown(f"{model.id2label[disease_idx]}")
+
+
+if __name__ == '__main__':
+    main()