Reorganized folders.

Added training graph.

app.py

@@ -14,12 +14,12 @@ examples_path = "examples"
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 correct_preds, wrong_preds = {}, {}
-condition_lst = pd.read_csv("feature.csv", header = "infer", sep = ",", encoding = "utf-8", dtype=str)
-D_LABITEMS = pd.read_csv("D_LABITEMS.csv", header = "infer", sep = ",", encoding = "utf-8", dtype=str)
+condition_lst = pd.read_csv("data/feature.csv", header = "infer", sep = ",", encoding = "utf-8", dtype=str)
+D_LABITEMS = pd.read_csv("data/D_LABITEMS.csv", header = "infer", sep = ",", encoding = "utf-8", dtype=str)
 
 
 def load_model():
-    path = r"final_model.pt"
+    path = r"models/final_model.pt"
     kwargs, state = torch.load(path, weights_only=False, map_location=device)
     model = VariationalGNN(**kwargs).to(device)
     model.load_state_dict(state)
@@ -236,7 +236,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
                 query_tbx.change(fn=query_input_change_event, inputs=[query_tbx, query_type], outputs=[query_btn, html])
                 query_type.change(fn=query_input_change_event, inputs=[query_tbx, query_type], outputs=[query_btn, html])
                 query_btn.click(fn=do_query, inputs=[query_tbx, query_type], outputs=html)
-            with gr.Accordion("More on technical details...", open=False):
+            with gr.Accordion("More on Technical Details...", open=False):
                 gr.Markdown(
                 """
                 - Paper: [Variationally Regularized Graph-based Representation Learning for Electronic Health Records (Zhu et al, 2021)](https://arxiv.org/abs/1912.03761)
@@ -250,9 +250,13 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
                   - Trained on NVIDIA A100 with PyTorch 2.4.0
                 """
             )
+            with gr.Accordion("More on Training...", open=False):
+                gr.HTML("""
+                        <img src="/file=images/AUPRC_Training_Graph.png" alt="">
+                        """)
     
     for partialFunc in resDispPartFuncs:
         partialFunc(outputs=[result_pred, result_label])
     
     
-demo.launch(debug=True)
+demo.launch(debug=True, allowed_paths=["images/."])