Update app.py

app.py

@@ -187,6 +187,7 @@ def load_default():
 def transfer_learning_forecasting():
     st.title("Transfer Learning Forecasting")
 
+    nhits_model, timesnet_model, lstm_model, tft_model = load_all_models()
     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:
@@ -200,11 +201,10 @@ def transfer_learning_forecasting():
         columns = df.columns.tolist()  # Convert Index to list
         ds_col = st.selectbox("Select Date/Time column", options=columns, index=columns.index('ds') if 'ds' in columns else 0)
         y_col = st.selectbox("Select Target column", options=columns, index=columns.index('y') if 'y' in columns else 1)
-        unique_id_col = st.text_input("Unique ID column (default: '1')", value="1")
+        # unique_id_col = st.text_input("Unique ID column (default: '1')", value="1")
 
     st.session_state.ds_col = ds_col
     st.session_state.y_col = y_col
-    st.session_state.unique_id_col = unique_id_col
 
     # Model selection and forecasting
     st.sidebar.subheader("Model Selection and Forecasting")
@@ -212,6 +212,7 @@ def transfer_learning_forecasting():
     horizon = st.sidebar.number_input("Forecast horizon", value=18)
 
     df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
+    df['unique_id']=1
     st.session_state.df = df
 
     # Determine frequency of data
@@ -255,11 +256,11 @@ def dynamic_forecasting():
         columns = df.columns.tolist()  # Convert Index to list
         ds_col = st.selectbox("Select Date/Time column", options=columns, index=columns.index('ds') if 'ds' in columns else 0)
         y_col = st.selectbox("Select Target column", options=columns, index=columns.index('y') if 'y' in columns else 1)
-        unique_id_col = st.text_input("Unique ID column (default: '1')", value="1")
+        # unique_id_col = st.text_input("Unique ID column (default: '1')", value="1")
 
+    df['unique_id']=1
     st.session_state.ds_col = ds_col
     st.session_state.y_col = y_col
-    st.session_state.unique_id_col = unique_id_col
 
     # Dynamic forecasting
     st.sidebar.subheader("Dynamic Model Selection and Forecasting")