Fix: Readme

streamlit_app.py

@@ -152,11 +152,26 @@ if st.session_state.authenticated:
                 # Load data
                 state_param = selected_state if selected_state != 'India' else None
                 market_param = selected_market if market_wise else None
+                
+                # Debug: Show query parameters
+                st.write(f"🔍 **Debug Info:**")
+                st.write(f"- State: {state_param}")
+                st.write(f"- Market: {market_param}")
+                st.write(f"- Days: {st.session_state.selected_period}")
+                st.write(f"- Current time: {datetime.now()}")
+                st.write(f"- Query start date: {datetime.now() - timedelta(days=st.session_state.selected_period)}")
+                
                 df = get_filtered_data(collection, state_param, market_param, st.session_state.selected_period)
                 
                 if not df.empty:
-                    # Debug: Show data info
-                    st.write(f"📊 Data fetched: {len(df)} rows")
+                    # Debug: Show raw data info
+                    st.write(f"📊 **Raw data fetched: {len(df)} rows**")
+                    st.write(f"- Date range in raw data: {df['Reported Date'].min()} to {df['Reported Date'].max()}")
+                    st.write(f"- Unique dates in raw data: {df['Reported Date'].nunique()}")
+                    
+                    # Show first few rows
+                    with st.expander("View first 10 rows of raw data"):
+                        st.dataframe(df.head(10))
                     
                     # Group by date and aggregate
                     df_grouped = df.groupby('Reported Date', as_index=False).agg({
@@ -164,15 +179,27 @@ if st.session_state.authenticated:
                         '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}")
+                    st.write(f"📊 **After grouping: {len(df_grouped)} unique dates**")
+                    st.write(f"💰 **Price range: {df_grouped['Modal Price (Rs./Quintal)'].min():.2f} - {df_grouped['Modal Price (Rs./Quintal)'].max():.2f}**")
+                    
+                    # Show grouped data
+                    with st.expander("View grouped data (first 10 rows)"):
+                        st.dataframe(df_grouped.head(10))
                     
                     # Only fill gaps if there are multiple data points
                     if len(df_grouped) > 1:
+                        st.write(f"🔧 **Filling date gaps...**")
+                        original_len = len(df_grouped)
                         date_range = pd.date_range(start=df_grouped['Reported Date'].min(), end=df_grouped['Reported Date'].max(), freq='D')
+                        st.write(f"- Total days in range: {len(date_range)}")
                         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.write(f"- Rows after filling: {len(df_grouped)} (was {original_len})")
+                    
+                    # Show final data before plotting
+                    with st.expander("View final plot data (first 20 rows)"):
+                        st.dataframe(df_grouped.head(20))
                     
                     st.subheader(f"📈 Trends for {selected_state} ({'Market: ' + selected_market if market_wise else 'State'})")