Update pages/2_🏋️‍♀️_Model_training.py

pages/2_🏋️‍♀️_Model_training.py

@@ -1,51 +1,51 @@
-import streamlit as st
-import pandas as pd
-from persist import persist, load_widget_state
-import numpy as np
-import matplotlib.pyplot as plt
-global variable_output
-
-def main():
-  
-    cs_body()
-
-def convert_csv():
-    d = {'col1': [], 'col2': []}
-    df = pd.DataFrame(data=d, columns=['Age', 'Sex'])
-    return df.to_csv().encode("utf-8")
-
-def cs_body():
-    
-    st.header('Training Data and Methodology')
-    st.write("Provide an overview of the Training Data and Training Procedure for this model")
-    st.markdown('##### Training dataset')
-    left, right = st.columns(2)
-    left.number_input("Training set size",value=100)
-    right.number_input("Validation set size",value=20)
-    st.text("Demographical and clinical characteristics")
-    left, right = st.columns(2, vertical_alignment ="center")
-    left.download_button("Download Template", data=convert_csv(), file_name='file.csv')
-    demo = right.file_uploader("Load template",type=['csv'])
-    if demo is not None:
-        left, right = st.columns(2, vertical_alignment ="center")
-        
-        fig, ax = plt.subplots()
-        ax.set_title("Age distribution")
-        ax.hist(np.random.normal(size=500))
-        left.pyplot(fig)
-
-        fig, ax = plt.subplots()
-        ax.pie([45,55],labels=["Men","Women"])
-        right.pyplot(fig)
-    st.text_input("Source",placeholder="Brats challenge/ Clinic ...")
-    st.text("Acquisition date")
-    left, right = st.columns(2)
-    left.date_input("From")
-    right.date_input("To")
-   
-   
-    
-
-if __name__ == '__main__':
-    load_widget_state()
+import streamlit as st
+import pandas as pd
+from persist import persist, load_widget_state
+import numpy as np
+import matplotlib.pyplot as plt
+global variable_output
+
+def main():
+  
+    cs_body()
+
+def convert_csv():
+    d = {'col1': [], 'col2': []}
+    df = pd.DataFrame(data=d, columns=['Age', 'Sex'])
+    return df.to_csv().encode("utf-8")
+
+def cs_body():
+    
+    st.header('Training Data and Methodology')
+    st.write("Provide an overview of the Training Data and Training Procedure for this model")
+    st.markdown('##### Training dataset')
+    left, right = st.columns(2)
+    left.number_input("Training set size",value=100)
+    right.number_input("Validation set size",value=20)
+    st.text("Demographical and clinical characteristics")
+    left, right = st.columns(2)#, vertical_alignment ="center")
+    left.download_button("Download Template", data=convert_csv(), file_name='file.csv')
+    demo = right.file_uploader("Load template",type=['csv'])
+    if demo is not None:
+        left, right = st.columns(2)#, vertical_alignment ="center")
+        
+        fig, ax = plt.subplots()
+        ax.set_title("Age distribution")
+        ax.hist(np.random.normal(size=500))
+        left.pyplot(fig)
+
+        fig, ax = plt.subplots()
+        ax.pie([45,55],labels=["Men","Women"])
+        right.pyplot(fig)
+    st.text_input("Source",placeholder="Brats challenge/ Clinic ...")
+    st.text("Acquisition date")
+    left, right = st.columns(2)
+    left.date_input("From")
+    right.date_input("To")
+   
+   
+    
+
+if __name__ == '__main__':
+    load_widget_state()
     main()