Update app.py

app.py

@@ -209,7 +209,7 @@ def load_default():
     return df
 
 def transfer_learning_forecasting():
-    st.title("Transfer Learning Forecasting")
+    st.title("Zero-shot Forecasting")
     st.markdown("""
     Instant time series forecasting and visualization by using various pre-trained deep neural network-based model trained on M4 data.
     """)
@@ -217,14 +217,30 @@ def transfer_learning_forecasting():
     nhits_models, timesnet_models, lstm_models, tft_models = load_all_models()
     
     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
+        if 'uploaded_file' not in st.session_state:
+            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
+                st.session_state.uploaded_file = uploaded_file
+            else:
+                df = load_default()  
+                st.session_state.df = df
+
         else:
-            df = load_default()  
-            st.session_state.df = df
-        
+            if st.checkbox("Upload a new file(CSV)"):
+                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
+                    st.session_state.uploaded_file = uploaded_file
+                else:
+                    df = load_default()  
+                    st.session_state.df = df
+            else:
+                uploaded_file = st.session_state.uploaded_file
+            
+            
         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)
@@ -467,9 +483,9 @@ def timegpt_anom():
     
 
 pg = st.navigation({
-    "NeuralForecast": [
+    "Neuralforecast": [
         # Load pages from functions
-        st.Page(transfer_learning_forecasting, title="Transfer Learning Forecasting", default=True, icon=":material/query_stats:"),
+        st.Page(transfer_learning_forecasting, title="Zero-shot Forecasting", default=True, icon=":material/query_stats:"),
         st.Page(dynamic_forecasting, title="Dynamic Forecasting", icon=":material/monitoring:"),
     ],
         "TimeGPT": [