Update app.py

app.py

@@ -318,65 +318,12 @@ 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):
@@ -388,33 +335,87 @@ def timegpt_fcst():
             df = load_default()
             st.session_state.df = df
         
-        columns = df.columns.tolist()
-        st.write("Columns in the uploaded dataset:", columns)  # Debug statement
-        
+        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)
-        y_col = st.selectbox("Select Target column", options=[col for col in columns if col != ds_col], index=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("Renamed DataFrame columns:", df.columns)  # Debug statement
 
+        
         id_col = 'ts_test'
-        df['unique_id'] = id_col
+        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)
+
+        st.write(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)
+    
+#     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
         
-        st.write("DataFrame after renaming and adding 'unique_id':", df.head())  # Debug statement
+#         columns = df.columns.tolist()
+#         st.write("Columns in the uploaded dataset:", columns)  # Debug statement
         
-        forecast_df = nixtla_client.forecast(
-            df=df,
-            h=7,
-            freq=freq,
-            level=[90]
-        )
+#         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=[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
 
-        nixtla_client.plot(
-            forecast_df,
-            level=[90],
-            max_insample_length=365
-        )
+#         id_col = 'ts_test'
+#         df['unique_id'] = id_col
+#         freq = determine_frequency(df)
+        
+#         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]
+#         )
+
+#         nixtla_client.plot(
+#             forecast_df,
+#             level=[90],
+#             max_insample_length=365
+#         )
 
 
 def timegpt_anom():