Fix: Readme

streamlit_app.py

@@ -147,39 +147,15 @@ if st.session_state.authenticated:
                 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 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({
                         'Arrivals (Tonnes)': 'sum', 
                         'Modal Price (Rs./Quintal)': 'mean'
                     })
                     
-                    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))
-                    
                     # Create complete date range and fill gaps
                     date_range = pd.date_range(
                         start=df_grouped['Reported Date'].min(),
@@ -192,8 +168,6 @@ if st.session_state.authenticated:
                     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"📊 **Plotting {len(df_grouped)} data points after filling date gaps**")
-                    
                     st.subheader(f"📈 Trends for {selected_state} ({'Market: ' + selected_market if market_wise else 'State'})")
                     
                     if data_type == "Both":