Update app.py

app.py

@@ -60,9 +60,9 @@ def generate_forecast(model, df,tag=False):
         forecast_df = model.predict(df=df)
     return forecast_df
 
-def determine_frequency(df, ds_col):
-    df[ds_col] = pd.to_datetime(df[ds_col])
-    df = df.set_index(ds_col)
+def determine_frequency(df):
+    df['ds'] = pd.to_datetime(df['ds'])
+    df = df.set_index('ds')
     freq = pd.infer_freq(df.index)
     return freq
 
@@ -158,19 +158,19 @@ def select_model(horizon, model_type, max_steps=200):
     else:
         raise ValueError(f"Unsupported model type: {model_type}")
 
-def model_train(df,model, ds_col, freq):
+def model_train(df,model, freq):
     nf = NeuralForecast(models=[model], freq=freq)
-    df[ds_col] = pd.to_datetime(df[ds_col])
+    df['ds'] = pd.to_datetime(df['ds'])
     nf.fit(df)
     return nf
 
-def forecast_time_series(df, model_type, horizon, max_steps=200, ds_col='ds',y_col):
+def forecast_time_series(df, model_type, horizon, max_steps=200,y_col):
     start_time = time.time()  # Start timing
-    freq = determine_frequency(df, ds_col)
+    freq = determine_frequency(df)
     st.sidebar.write(f"Data frequency: {freq}")
     
     selected_model = select_model(horizon, model_type, max_steps)
-    model = model_train(df, selected_model, ds_col,freq)
+    model = model_train(df, selected_model,freq)
     
     forecast_results = {}
     st.sidebar.write(f"Generating forecast using {model_type} model...")
@@ -265,6 +265,7 @@ def dynamic_forecasting():
         # unique_id_col = st.text_input("Unique ID column (default: '1')", value="1")
 
     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
@@ -279,7 +280,7 @@ def dynamic_forecasting():
     dynamic_max_steps = st.sidebar.number_input('Max steps', value=200)
 
     
-    forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps, ds_col='ds',y_col)
+    forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps,y_col)
 
 pg = st.navigation({
     "Overview": [