Update app.py

app.py

@@ -209,7 +209,7 @@ def transfer_learning_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)
+            df = pd.read_csv(uploaded_file, index_col=0)
             st.session_state.df = df
         else:
             df = load_default()  
@@ -377,53 +377,13 @@ def timegpt_fcst():
             )
             st.write(forecast_df)
 
-            # 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
-        
-#         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)
-#         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
-
-#         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(
+                df,
+                forecast_df,
+                level=[90],
+                max_insample_length=365
+            )
 
-#         nixtla_client.plot(
-#             forecast_df,
-#             level=[90],
-#             max_insample_length=365
-#         )
 
 
 def timegpt_anom():
@@ -462,6 +422,8 @@ def timegpt_anom():
         st.session_state.ds_col = ds_col
         st.session_state.y_col = y_col
 
+        freq = determine_frequency(df)
+        
         if st.sidebar.button("Submit"):
             anom_df = nixtla_client.detect_anomalies(
                 df=df,