Update app.py

app.py

@@ -311,7 +311,7 @@ def dynamic_forecasting():
     if st.sidebar.button("Submit"):
         forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps,y_col)
 
-def timegpt():
+def timegpt_fcst():
     nixtla_token = os.environ.get("NIXTLA_API_KEY")
     nixtla_client = NixtlaClient(
     api_key = api_key
@@ -333,12 +333,63 @@ def timegpt():
         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)
 
+        df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
+
+        id_col = 'ts_test'
+        df['unique_id']=id_col
+        freq = determine_frequency(df)
+        
+        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():
+    nixtla_token = os.environ.get("NIXTLA_API_KEY")
+    nixtla_client = NixtlaClient(
+    api_key = api_key
+    )
+
+    
+    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
+        
+        # Column selection
+        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)
+
+        df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
+
+        id_col = 'ts_test'
+        df['unique_id']=id_col
+        freq = determine_frequency(df)
+        
+        forecast_df = nixtla_client.forecast(
+            df=df,
+            h=7,
+            freq=freq,
+            level=[90]
+        )
 
         nixtla_client.plot(
-            df,
-            id_col= y_col,
-            time_col=ds_col,
-            target_col=y_col,
+            forecast_df,
+            level=[90],
             max_insample_length=365
         )
         
@@ -354,8 +405,8 @@ pg = st.navigation({
     ],
         "TimeGPT": [
         # Load pages from functions
-        st.Page(timegpt, title="TimeGPT Forecast", icon=":material/smart_toy:"),
-        st.Page(timegpt, title="TimeGPT Anomalies Detection", icon=":material/detector_offline:")
+        st.Page(timegpt_fcst, title="TimeGPT Forecast", icon=":material/smart_toy:"),
+        st.Page(timegpt_anom, title="TimeGPT Anomalies Detection", icon=":material/detector_offline:")
         ]
 })