Update app.py

app.py

@@ -237,21 +237,14 @@ def transfer_learning_forecasting():
                     df = st.session_state.df
             else:
                 df = st.session_state.df
-            
-            
-        columns = df.columns.tolist()  # Convert Index to list
-        opt = []
+
+        columns = st.session_state.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
-        if 'ds' in opt:
-            opt.remove('ds')
-        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
+        st.session_state.ds_col = ds_col
+        st.session_state.y_col = y_col
 
     # Model selection and forecasting
     st.sidebar.subheader("Model Selection and Forecasting")
@@ -327,13 +320,26 @@ def dynamic_forecasting():
     """)
     
     with st.sidebar.expander("Upload and Configure Dataset", expanded=True):
-        uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
-        if uploaded_file:
-            df = pd.read_csv(uploaded_file)
-            st.session_state.df = df
+        if 'uploaded_file' not in st.session_state:
+            uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
+            if uploaded_file:
+                df = pd.read_csv(uploaded_file)
+                st.session_state.df = df
+                st.session_state.uploaded_file = uploaded_file
+            else:
+                df = load_default()
+                st.session_state.df = df
         else:
-            df = load_default()
-            st.session_state.df = df
+            if st.checkbox("Upload a new file (CSV)"):
+                uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
+                if uploaded_file:
+                    df = pd.read_csv(uploaded_file)
+                    st.session_state.df = df
+                    st.session_state.uploaded_file = uploaded_file
+                else:
+                    df = st.session_state.df
+            else:
+                df = st.session_state.df
         
         columns = df.columns.tolist()  # Convert Index to list
         opt = []
@@ -374,13 +380,26 @@ def timegpt_fcst():
     Instant time series forecasting and visualization by using the TimeGPT API provided by Nixtla.
     """)
     with st.sidebar.expander("Upload and Configure Dataset", expanded=True):
-        uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
-        if uploaded_file:
-            df = pd.read_csv(uploaded_file)
-            st.session_state.df = df
+        if 'uploaded_file' not in st.session_state:
+            uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
+            if uploaded_file:
+                df = pd.read_csv(uploaded_file)
+                st.session_state.df = df
+                st.session_state.uploaded_file = uploaded_file
+            else:
+                df = load_default()
+                st.session_state.df = df
         else:
-            df = load_default()
-            st.session_state.df = df
+            if st.checkbox("Upload a new file (CSV)"):
+                uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
+                if uploaded_file:
+                    df = pd.read_csv(uploaded_file)
+                    st.session_state.df = df
+                    st.session_state.uploaded_file = uploaded_file
+                else:
+                    df = st.session_state.df
+            else:
+                df = st.session_state.df
         
         columns = df.columns.tolist()  # Convert Index to list
         opt = []
@@ -436,13 +455,26 @@ def timegpt_anom():
     Instant time series anomaly detection and visualization by using the TimeGPT API provided by Nixtla.
     """)
     with st.sidebar.expander("Upload and Configure Dataset", expanded=True):
-        uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
-        if uploaded_file:
-            df = pd.read_csv(uploaded_file)
-            st.session_state.df = df
+        if 'uploaded_file' not in st.session_state:
+            uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
+            if uploaded_file:
+                df = pd.read_csv(uploaded_file)
+                st.session_state.df = df
+                st.session_state.uploaded_file = uploaded_file
+            else:
+                df = load_default()
+                st.session_state.df = df
         else:
-            df = load_default()
-            st.session_state.df = df
+            if st.checkbox("Upload a new file (CSV)"):
+                uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
+                if uploaded_file:
+                    df = pd.read_csv(uploaded_file)
+                    st.session_state.df = df
+                    st.session_state.uploaded_file = uploaded_file
+                else:
+                    df = st.session_state.df
+            else:
+                df = st.session_state.df
         
         columns = df.columns.tolist()  # Convert Index to list
         opt = []