Fix: Readme

streamlit_app.py

@@ -157,12 +157,27 @@ if st.session_state.authenticated:
                 df = get_filtered_data(collection, state_param, market_param, st.session_state.selected_period)
                 
                 if not df.empty:
-                    df_grouped = df.groupby('Reported Date', as_index=False).agg({'Arrivals (Tonnes)': 'sum', 'Modal Price (Rs./Quintal)': 'mean'})
-                    date_range = pd.date_range(start=df_grouped['Reported Date'].min(), end=df_grouped['Reported Date'].max())
-                    df_grouped = df_grouped.set_index('Reported Date').reindex(date_range).rename_axis('Reported Date').reset_index()
-                    df_grouped['Arrivals (Tonnes)'] = df_grouped['Arrivals (Tonnes)'].ffill().bfill()
-                    df_grouped['Modal Price (Rs./Quintal)'] = df_grouped['Modal Price (Rs./Quintal)'].ffill().bfill()
+                    # Debug: Show data info
+                    st.write(f"📊 Data fetched: {len(df)} rows")
+                    
+                    # Group by date and aggregate
+                    df_grouped = df.groupby('Reported Date', as_index=False).agg({
+                        'Arrivals (Tonnes)': 'sum', 
+                        'Modal Price (Rs./Quintal)': 'mean'
+                    })
+                    
+                    st.write(f"📊 Unique dates after grouping: {len(df_grouped)}")
+                    st.write(f"💰 Price range: {df_grouped['Modal Price (Rs./Quintal)'].min():.2f} - {df_grouped['Modal Price (Rs./Quintal)'].max():.2f}")
+                    
+                    # Only fill gaps if there are multiple data points
+                    if len(df_grouped) > 1:
+                        date_range = pd.date_range(start=df_grouped['Reported Date'].min(), end=df_grouped['Reported Date'].max(), freq='D')
+                        df_grouped = df_grouped.set_index('Reported Date').reindex(date_range).rename_axis('Reported Date').reset_index()
+                        df_grouped['Arrivals (Tonnes)'] = df_grouped['Arrivals (Tonnes)'].ffill().bfill()
+                        df_grouped['Modal Price (Rs./Quintal)'] = df_grouped['Modal Price (Rs./Quintal)'].ffill().bfill()
+                    
                     st.subheader(f"📈 Trends for {selected_state} ({'Market: ' + selected_market if market_wise else 'State'})")
+                    
                     if data_type == "Both":
                         scaler = MinMaxScaler()
                         df_grouped[['Scaled Price', 'Scaled Arrivals']] = scaler.fit_transform(df_grouped[['Modal Price (Rs./Quintal)', 'Arrivals (Tonnes)']])