Update app.py

app.py

@@ -19,17 +19,42 @@ import textwrap
 from datasets import load_dataset
 
 
-#Read data training data (g2).
-g2_x1 = pd.read_csv("g2_12m_data_train.csv", index_col = 0, low_memory = False)
-g2_x2 = pd.read_csv("g2_24m_data_train.csv", index_col = 0, low_memory = False)
-g2_x3 = pd.read_csv("g2_36m_data_train.csv", index_col = 0, low_memory = False)
-g2_x4 = pd.read_csv("g2_60m_data_train.csv", index_col = 0, low_memory = False)
-
-#Read validation data (g2).
-g2_x1_valid = pd.read_csv("g2_12m_data_valid.csv", index_col = 0, low_memory = False)
-g2_x2_valid = pd.read_csv("g2_24m_data_valid.csv", index_col = 0, low_memory = False)
-g2_x3_valid = pd.read_csv("g2_36m_data_valid.csv", index_col = 0, low_memory = False)
-g2_x4_valid = pd.read_csv("g2_60m_data_valid.csv", index_col = 0, low_memory = False)
+#Read data training data.
+
+g2_x1 = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g2_12m_data_train.csv", use_auth_token = HF_TOKEN)
+g2_x1 = pd.DataFrame(g2_x1['train'])
+g2_x1 = g2_x1.iloc[:, 1:]
+
+g2_x2 = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g2_24m_data_train.csv", use_auth_token = HF_TOKEN)
+g2_x2 = pd.DataFrame(g2_x2['train'])
+g2_x2 = g2_x2.iloc[:, 1:]
+
+g2_x3 = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g2_36m_data_train.csv", use_auth_token = HF_TOKEN)
+g2_x3 = pd.DataFrame(g2_x3['train'])
+g2_x3 = g2_x3.iloc[:, 1:]
+
+g2_x4 = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g2_60m_data_train.csv", use_auth_token = HF_TOKEN)
+g2_x4 = pd.DataFrame(g2_x4['train'])
+g2_x4 = g2_x4.iloc[:, 1:]
+
+#Read validation data.
+
+g2_x1_valid = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g2_12m_data_valid.csv", use_auth_token = HF_TOKEN)
+g2_x1_valid = pd.DataFrame(g2_x1_valid['train'])
+g2_x1_valid = g2_x1_valid.iloc[:, 1:]
+
+g2_x2_valid = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g2_24m_data_valid.csv", use_auth_token = HF_TOKEN)
+g2_x2_valid = pd.DataFrame(g2_x2_valid['train'])
+g2_x2_valid = g2_x2_valid.iloc[:, 1:]
+
+g2_x3_valid = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g2_36m_data_valid.csv", use_auth_token = HF_TOKEN)
+g2_x3_valid = pd.DataFrame(g2_x3_valid['train'])
+g2_x3_valid = g2_x3_valid.iloc[:, 1:]
+
+g2_x4_valid = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g2_60m_data_valid.csv", use_auth_token = HF_TOKEN)
+g2_x4_valid = pd.DataFrame(g2_x4_valid['train'])
+g2_x4_valid = g2_x4_valid.iloc[:, 1:]
+
 
 #Define feature names (g2).
 g2_f1_names = list(g2_x1.columns)
@@ -188,17 +213,41 @@ def g2_y4_predict(*args):
     output = g2_output_y4.format(prob * 100)
     return output
 
-#Read data training data (g3).
-g3_x1 = pd.read_csv("g3_12m_data_train.csv", index_col = 0, low_memory = False)
-g3_x2 = pd.read_csv("g3_24m_data_train.csv", index_col = 0, low_memory = False)
-g3_x3 = pd.read_csv("g3_36m_data_train.csv", index_col = 0, low_memory = False)
-g3_x4 = pd.read_csv("g3_60m_data_train.csv", index_col = 0, low_memory = False)
+#Read data training data.
+
+g3_x1 = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g3_12m_data_train.csv", use_auth_token = HF_TOKEN)
+g3_x1 = pd.DataFrame(g3_x1['train'])
+g3_x1 = g3_x1.iloc[:, 1:]
+
+g3_x2 = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g3_24m_data_train.csv", use_auth_token = HF_TOKEN)
+g3_x2 = pd.DataFrame(g3_x2['train'])
+g3_x2 = g3_x2.iloc[:, 1:]
+
+g3_x3 = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g3_36m_data_train.csv", use_auth_token = HF_TOKEN)
+g3_x3 = pd.DataFrame(g3_x3['train'])
+g3_x3 = g3_x3.iloc[:, 1:]
+
+g3_x4 = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g3_60m_data_train.csv", use_auth_token = HF_TOKEN)
+g3_x4 = pd.DataFrame(g3_x4['train'])
+g3_x4 = g3_x4.iloc[:, 1:]
+
+#Read validation data.
+
+g3_x1_valid = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g3_12m_data_valid.csv", use_auth_token = HF_TOKEN)
+g3_x1_valid = pd.DataFrame(g3_x1_valid['train'])
+g3_x1_valid = g3_x1_valid.iloc[:, 1:]
+
+g3_x2_valid = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g3_24m_data_valid.csv", use_auth_token = HF_TOKEN)
+g3_x2_valid = pd.DataFrame(g3_x2_valid['train'])
+g3_x2_valid = g3_x2_valid.iloc[:, 1:]
+
+g3_x3_valid = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g3_36m_data_valid.csv", use_auth_token = HF_TOKEN)
+g3_x3_valid = pd.DataFrame(g3_x3_valid['train'])
+g3_x3_valid = g3_x3_valid.iloc[:, 1:]
 
-#Read validation data (g3).
-g3_x1_valid = pd.read_csv("g3_12m_data_valid.csv", index_col = 0, low_memory = False)
-g3_x2_valid = pd.read_csv("g3_24m_data_valid.csv", index_col = 0, low_memory = False)
-g3_x3_valid = pd.read_csv("g3_36m_data_valid.csv", index_col = 0, low_memory = False)
-g3_x4_valid = pd.read_csv("g3_60m_data_valid.csv", index_col = 0, low_memory = False)
+g3_x4_valid = load_dataset("mertkarabacak/G2G3-Glioma", data_files="g3_60m_data_valid.csv", use_auth_token = HF_TOKEN)
+g3_x4_valid = pd.DataFrame(g3_x4_valid['train'])
+g3_x4_valid = g3_x4_valid.iloc[:, 1:]
 
 #Define feature names (g3).
 g3_f1_names = list(g3_x1.columns)
@@ -515,10 +564,12 @@ with gr.Blocks(title = "NCDB-G2G3 Glioma") as demo:
     gr.Markdown(
         """
     <br/>
+    <center><h2>NOT FOR CLINICAL USE.</h2><center>    
+    <br/>    
     <center><h1>Grade II and III Glioma Survival Outcomes</h1></center>
     <center><h2>Prediction Tool</h2></center>
     <br/>
-    <center><i>This web application should not be used to guide any clinical decisions.</i><center>
+    <center><h3>This web application should not be used to guide any clinical decisions.</h3><center>
         """
     )