Update app.py

app.py

@@ -8,6 +8,8 @@ from neuralforecast.losses.pytorch import HuberMQLoss
 from neuralforecast.utils import AirPassengersDF
 import time
 from st_aggrid import AgGrid
+from nixtla import NixtlaClient
+
 
 @st.cache_resource
 def load_model(path, freq):
@@ -107,7 +109,7 @@ def select_model_based_on_frequency(freq, nhits_models, timesnet_models, lstm_mo
     else:
         raise ValueError(f"Unsupported frequency: {freq}")
 
-def select_model(horizon, model_type, max_steps=200):
+def select_model(horizon, model_type, max_steps=50):
     if model_type == 'NHITS':
         return NHITS(input_size=5 * horizon,
                      h=horizon,
@@ -304,17 +306,45 @@ def dynamic_forecasting():
     st.sidebar.subheader("Dynamic Model Selection and Forecasting")
     dynamic_model_choice = st.sidebar.selectbox("Select model for dynamic forecasting", ["NHITS", "TimesNet", "LSTM", "TFT"], key="dynamic_model_choice")
     dynamic_horizon = st.sidebar.number_input("Forecast horizon", value=18)
-    dynamic_max_steps = st.sidebar.number_input('Max steps', value=200)
+    dynamic_max_steps = st.sidebar.number_input('Max steps', value=10)
 
     if st.sidebar.button("Submit"):
         forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps,y_col)
 
+def timegpt():
+    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, index=columns.index('y') if 'y' in columns else 1
+
+
+        
+        
+    
+    
+    
+
 pg = st.navigation({
-    "Overview": [
+    "NeuralForecast": [
         # Load pages from functions
         st.Page(transfer_learning_forecasting, title="Transfer Learning Forecasting", default=True, icon=":material/query_stats:"),
         st.Page(dynamic_forecasting, title="Dynamic Forecasting", icon=":material/monitoring:"),
-    ]
+    ],
+        "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:")
+        ]
 })
 
 try: