Update app.py

app.py

@@ -238,7 +238,7 @@ def transfer_learning_forecasting():
             else:
                 df = st.session_state.df
 
-        columns = st.session_state.df.columns.tolist()
+        columns = df.columns.tolist()
         ds_col = st.selectbox("Select Date/Time column", options=columns, index=columns.index('ds') if 'ds' in columns else 0)
         target_columns = [col for col in columns if col != ds_col]
         y_col = st.selectbox("Select Target column", options=target_columns, index=0)
@@ -254,7 +254,6 @@ def transfer_learning_forecasting():
     df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
     df['unique_id']=1
     df = df[['unique_id','ds','y']]
-    st.session_state.df = df
 
     # Determine frequency of data
     frequency = determine_frequency(df)
@@ -340,24 +339,20 @@ def dynamic_forecasting():
                     df = st.session_state.df
             else:
                 df = st.session_state.df
-        
-        columns = df.columns.tolist()  # Convert Index to list
-        opt = []
+
+        columns = df.columns.tolist()
         ds_col = st.selectbox("Select Date/Time column", options=columns, index=columns.index('ds') if 'ds' in columns else 0)
-        if 'ds' in columns and 'unique_id' in columns:
-            columns.pop(columns.index('ds'))
-            columns.pop(columns.index('unique_id'))
-        opt = columns
-        y_col = st.selectbox("Select Target column", options=opt, index=0)
+        target_columns = [col for col in columns if col != ds_col]
+        y_col = st.selectbox("Select Target column", options=target_columns, index=0)
+
+        st.session_state.ds_col = ds_col
+        st.session_state.y_col = y_col
 
     df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
     
     df['unique_id']=1
     df = df[['unique_id','ds','y']]
-    st.session_state.df = df
     
-    st.session_state.ds_col = ds_col
-    st.session_state.y_col = y_col
 
     # Dynamic forecasting
     st.sidebar.subheader("Dynamic Model Selection and Forecasting")
@@ -400,15 +395,11 @@ def timegpt_fcst():
                     df = st.session_state.df
             else:
                 df = st.session_state.df
-        
-        columns = df.columns.tolist()  # Convert Index to list
-        opt = []
+
+        columns = df.columns.tolist()
         ds_col = st.selectbox("Select Date/Time column", options=columns, index=columns.index('ds') if 'ds' in columns else 0)
-        if 'ds' in columns and 'unique_id' in columns:
-            columns.pop(columns.index('ds'))
-            columns.pop(columns.index('unique_id'))
-        opt = columns
-        y_col = st.selectbox("Select Target column", options=opt, index=0)
+        target_columns = [col for col in columns if col != ds_col]
+        y_col = st.selectbox("Select Target column", options=target_columns, index=0)
         h = st.number_input("Forecast horizon", value=14)
 
         df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
@@ -417,10 +408,6 @@ def timegpt_fcst():
         id_col = 'ts_test'
         df['unique_id']=id_col
         df = df[['unique_id','ds','y']]
-        st.session_state.df = df
-        
-        st.session_state.ds_col = ds_col
-        st.session_state.y_col = y_col
         
 
         freq = determine_frequency(df)
@@ -475,25 +462,17 @@ def timegpt_anom():
                     df = st.session_state.df
             else:
                 df = st.session_state.df
-        
-        columns = df.columns.tolist()  # Convert Index to list
-        opt = []
+
+        columns = df.columns.tolist()
         ds_col = st.selectbox("Select Date/Time column", options=columns, index=columns.index('ds') if 'ds' in columns else 0)
-        if 'ds' in columns and 'unique_id' in columns:
-            columns.pop(columns.index('ds'))
-            columns.pop(columns.index('unique_id'))
-        opt = columns
-        y_col = st.selectbox("Select Target column", options=opt, index=0)
+        target_columns = [col for col in columns if col != ds_col]
+        y_col = st.selectbox("Select Target column", options=target_columns, index=0)
 
         df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
 
         id_col = 'ts_test'
         df['unique_id']=id_col
         df = df[['unique_id','ds','y']]
-        st.session_state.df = df
-        
-        st.session_state.ds_col = ds_col
-        st.session_state.y_col = y_col
 
         freq = determine_frequency(df)