Debug 20

.vscode/launch.json

@@ -0,0 +1,15 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "app.py",
+            "console": "integratedTerminal"
+        }
+    ]
+}

app.py

@@ -1,78 +1,94 @@
 from typing import Dict
 from pathlib import Path
-import pandas as pd
-from io import StringIO
+import pickle, logging, sys
+from typing import Tuple, List, Dict
 
+import numpy as np
+import numpy.typing as npt
+from scipy.sparse import csr_matrix
+import torch
 import gradio as gr
+import pandas as pd
 
-# def predict(text: str) -> Dict:
-#     return {"alive": 0.9, "death": 0.1}
-    
-# example_list = [[1.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
-
-# # Create title, description and article strings
-# title = "This is title."
-# description = "This is description."
-# article = "This is article."
-
-# default_csv = "Phase,Activity,Start date,End date\n\"Mapping the Field\",\"Literature review\",2024-01-01,2024-01-31"
-
-# def process_csv_text(temp_file):
-#     if isinstance(temp_file, str):
-#         print("1")
-#         df = pd.read_csv(temp_file, header = "infer", sep = ",", encoding = "utf-8")
-#     else:
-#         print("2")
-#         df = pd.read_csv(temp_file.name)
-#     print("***")
-#     print(df)
-#     print("***")
-#     return df
-
-# with gr.Blocks() as demo:
-#     upload_button = gr.UploadButton(label="Upload Timetable", file_types = ['.csv'], file_count = "single")    
-#     table = gr.Dataframe(headers=["Phase", "Activity", "Start date", "End date"], type="pandas", col_count=4)
-#     upload_button.upload(fn=process_csv_text, inputs=upload_button, outputs=table, api_name="upload_csv")
-
-# demo.launch(debug=True)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = torch.jit.load("final_model.pth")
 
-def predict():
+def predict(patient_id: int):
+    print(f"predict patient {patient_id}")
+    df = pd.read_csv(f"Patient{patient_id}.csv", 
+                     header="infer", 
+                     sep=",", 
+                     encoding="utf-8", 
+                     dtype={'condition': 'str', 'user_key': 'float32'}, 
+                     keep_default_na=False)
+    
     return {"Death": 0.9, "Alive": 0.1}
 
-def download_patient(patient_id: str) -> str:
-    my_file = Path(f"Patient{patient_id}.csv")
-    print(f"File to download [{str(my_file)}].")
-    if not my_file.is_file():
-        raise Exception(f"[{my_file}] not found.")
-    print(f"Downloading file [{str(my_file)}].")
-    return str(my_file)
 
-with gr.Blocks() as demo:
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
     with gr.Row():
         with gr.Column():
+            gr.Markdown(
+                """
+                ## Input:
+                (See examples for file structure)
+                """
+            )
             patient_upload_file = gr.File(label="Upload A Patient", 
                                           file_types = ['.csv'],
-                                          file_count = "single")
+                                          file_count = "single",
+                                          height=100)
+            gr.Markdown(
+                """
+                ## Examples - Correct Prediction:
+                """
+            )
             with gr.Row():
-                with gr.Column(min_width=100):
-                    patient_1_input_btn = gr.Button("Patient 1")
-                    patient_1_download_btn = gr.DownloadButton(label="Download 1", value="Patient1.csv")
-                with gr.Column(min_width=100):
-                    patient_2_input_btn = gr.Button("Patient 2")
-                    patient_2_download_btn = gr.DownloadButton(label="Download 2", value="Patient2.csv")
-                with gr.Column(min_width=100):
-                    patient_3_input_btn = gr.Button("Patient 3")
-                    patient_3_download_btn = gr.DownloadButton(label="Download 3", value="Patient3.csv")
+                with gr.Column(variant='panel', min_width=100):
+                    patient_1_input_btn = gr.Button("Patient 1", size="sm")
+                    patient_1_download_btn = gr.DownloadButton(label="Download", value="Patient1.csv", size="sm")
+                    patient_id_1 = gr.Number(value=1, visible=False)
+                with gr.Column(variant='panel', min_width=100):
+                    patient_2_input_btn = gr.Button("Patient 2", size="sm")
+                    patient_2_download_btn = gr.DownloadButton(label="Download", value="Patient2.csv", size="sm")
+                    patient_id_2 = gr.Number(value=2, visible=False)
+                with gr.Column(variant='panel', min_width=100):
+                    patient_3_input_btn = gr.Button("Patient 3", size="sm")
+                    patient_3_download_btn = gr.DownloadButton(label="Download", value="Patient3.csv", size="sm")
+                    patient_id_3 = gr.Number(value=3, visible=False)
+            gr.Markdown(
+                """
+                ## Examples - Wrong Prediction:
+                """
+            )
+            with gr.Row():
+                with gr.Column(variant='panel', min_width=100):
+                    patient_4_input_btn = gr.Button("Patient 4", size="sm")
+                    patient_4_download_btn = gr.DownloadButton(label="Download", value="Patient4.csv", size="sm")
+                    patient_id_4 = gr.Number(value=4, visible=False)
+                with gr.Column(variant='panel', min_width=100):
+                    patient_5_input_btn = gr.Button("Patient 5", size="sm")
+                    patient_5_download_btn = gr.DownloadButton(label="Download", value="Patient5.csv", size="sm")
+                    patient_id_5 = gr.Number(value=5, visible=False)
+                with gr.Column(variant='panel', min_width=100):
+                    patient_6_input_btn = gr.Button("Patient 6", size="sm")
+                    patient_6_download_btn = gr.DownloadButton(label="Download", value="Patient6.csv", size="sm")
+                    patient_id_6 = gr.Number(value=6, visible=False)
         with gr.Column():
+            gr.Markdown(
+                """
+                ## Mortality Prediction:
+                In 24 hours after ICU admission.
+                """
+            )
             result = gr.Label(num_top_classes=2, label="Predictions")
     
     # Choose a patient to predict.        
-    patient_1_input_btn.click(fn=predict, inputs=None, outputs=result, api_name="predict")
+    patient_1_input_btn.click(fn=predict, inputs=patient_id_1, outputs=result, api_name="predict")
+    patient_2_input_btn.click(fn=predict, inputs=patient_id_2, outputs=result, api_name="predict")
+    patient_3_input_btn.click(fn=predict, inputs=patient_id_3, outputs=result, api_name="predict")
     
-    # Download a patient ehr profile.
-    # patient_1_download_btn.click(fn=download_patient, inputs=patient_1_download_btn, outputs=patient_1_download_btn)
-    # patient_2_download_btn.click(fn=download_patient, inputs=patient_2_download_btn, outputs=patient_2_download_btn)
-    # patient_3_download_btn.click(fn=download_patient, inputs=patient_3_download_btn, outputs=patient_3_download_btn)
     
     
 demo.launch(debug=True)
+

final_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:731461d450bb0ec5f011c3751c98621171fb9abd017331da48d1f6fb3dec184d
+size 61002953