Update app.py

app.py

@@ -318,12 +318,65 @@ def dynamic_forecasting():
     if st.sidebar.button("Submit"):
         forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps,y_col)
 
+# def timegpt_fcst():
+#     nixtla_token = os.environ.get("NIXTLA_API_KEY")
+#     nixtla_client = NixtlaClient(
+#     api_key = nixtla_token
+#     )
+
+    
+#     st.title("TimeGPT 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
+#         else:
+#             df = load_default()
+#             st.session_state.df = df
+        
+#         columns = df.columns.tolist()  # Convert Index to list
+#         opt = []
+#         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)
+
+#         df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
+
+#         st.write(df)
+#         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)
+
+#         if st.sidebar.button("Submit"):
+#             forecast_df = nixtla_client.forecast(
+#                 df=df,
+#                 h=7,
+#                 time_col='ds',
+#                 target_col='y',
+#                 freq=freq,
+#                 level=[90]
+#             )
+
+#             nixtla_client.plot(
+#                 forecast_df,
+#                 level=[90],
+#                 max_insample_length=365
+#             )
+
 def timegpt_fcst():
     nixtla_token = os.environ.get("NIXTLA_API_KEY")
-    nixtla_client = NixtlaClient(
-    api_key = nixtla_token
-    )
-
+    nixtla_client = NixtlaClient(api_key=nixtla_token)
     
     st.title("TimeGPT Forecasting")
     with st.sidebar.expander("Upload and Configure Dataset", expanded=True):
@@ -335,44 +388,34 @@ def timegpt_fcst():
             df = load_default()
             st.session_state.df = df
         
-        columns = df.columns.tolist()  # Convert Index to list
-        opt = []
+        columns = df.columns.tolist()
+        st.write("Columns in the uploaded dataset:", columns)  # Debug statement
+        
         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)
-
+        y_col = st.selectbox("Select Target column", options=[col for col in columns if col != ds_col], index=0)
+        
         df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
+        st.write("Renamed DataFrame columns:", df.columns)  # Debug statement
 
-        st.write(df)
         id_col = 'ts_test'
-        df['unique_id']=id_col
-        df = df[['unique_id','ds','y']]
-        st.session_state.df = df
+        df['unique_id'] = id_col
+        freq = determine_frequency(df)
         
-        st.session_state.ds_col = ds_col
-        st.session_state.y_col = y_col
+        st.write("DataFrame after renaming and adding 'unique_id':", df.head())  # Debug statement
         
+        forecast_df = nixtla_client.forecast(
+            df=df,
+            h=7,
+            freq=freq,
+            level=[90]
+        )
 
-        freq = determine_frequency(df)
-
-        if st.sidebar.button("Submit"):
-            forecast_df = nixtla_client.forecast(
-                df=df,
-                h=7,
-                time_col='ds',
-                target_col='y',
-                freq=freq,
-                level=[90]
-            )
+        nixtla_client.plot(
+            forecast_df,
+            level=[90],
+            max_insample_length=365
+        )
 
-            nixtla_client.plot(
-                forecast_df,
-                level=[90],
-                max_insample_length=365
-            )
 
 def timegpt_anom():
     nixtla_token = os.environ.get("NIXTLA_API_KEY")