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| import streamlit as st | |
| import pandas as pd | |
| ######################### | |
| ## STREAMLIT STRUCTURE ## | |
| ######################### | |
| st.set_page_config(layout="wide") | |
| # Title of the page | |
| st.title("Dashboard") | |
| st.write("Summary of SKUs where stock days are limited and reorder date is near") | |
| ############### | |
| ## FUNCTIONS ## | |
| ############### | |
| average_days_per_month = 30.437 | |
| # we need the following so that streamlit will only run the following once | |
| # in this case, it will cache our df and not rerun it everytime we change something on the app | |
| # https://discuss.streamlit.io/t/not-to-run-entire-script-when-a-widget-value-is-changed/502/5 | |
| def read_df_combined(): | |
| df_combined = pd.read_csv('https://generalassemblydsi32.s3.ap-southeast-1.amazonaws.com/ABC_Analysis-221125/df_combined.csv') | |
| # df_combined = pd.read_csv("df_combined.csv") | |
| df_combined["stock_month_2dp"] = df_combined["stock_month"].round(2) | |
| df_combined["stock_days_2dp"] = (df_combined["stock_month"]*average_days_per_month).round(2) | |
| df_combined["current_stock_formatted"] = df_combined["current_stock"].apply(lambda d: f"{int(d):,}") | |
| df_combined["avg_shipped_qty_2dp"] = df_combined["avg_shipped_qty"].round(2) | |
| df_combined["rop_date"] = pd.to_datetime(df_combined["rop_date"], format=r"%Y-%m-%d").dt.date | |
| st.session_state.df = df_combined | |
| return df_combined | |
| if "df" not in st.session_state: | |
| st.session_state.df = read_df_combined() | |
| read_df_combined() | |
| def display_stock_warn_reorder(hub): | |
| stock_days_keys = "stock_days"+hub | |
| reorder_days_keys = "reorder_days"+hub | |
| # Stock Warning | |
| st.subheader(f"Stock Warning for {hub}") | |
| st.caption("Display stocks where remaining stock days is less than specified") | |
| st.slider("Stock Warning", 1, 90, 30, label_visibility = "collapsed", help = "Specify the stock days to filter", key = stock_days_keys) | |
| df_stock_warning = st.session_state.df[["sku","current_stock_formatted","avg_shipped_qty_2dp", "stock_days_2dp"]][(st.session_state.df["hub"]==hub) & (st.session_state.df["stock_days_2dp"] <= st.session_state[stock_days_keys])].groupby("sku").min().reset_index() | |
| df_stock_warning.columns = ["SKU","Stocks on hand", "Average monthly demand", "Stock days"] | |
| st.dataframe(df_stock_warning.sort_values(by=["Stock days"]), use_container_width = True) | |
| # Reorder stocks. For CONSOLE hub only, remove the column "console_support" | |
| if hub == "R.HUB": | |
| st.subheader(f"Upcoming Reorder for {hub}") | |
| st.caption("Display stocks where the reorder date is within the specified time period") | |
| st.caption("Recommended transport mode would always be based on the lowest estimated handling cost. If we have reorder date is marked as 0 (meaning we have already pass the optimum reorder date), recommended transport mode would be based on the shortest lead time") | |
| st.slider("Stock Warning", 1, 90, 30, label_visibility = "collapsed", help = "Specify the time period to filter upcoming reorder point", key = reorder_days_keys) | |
| df_reorder = st.session_state.df[["sku","current_stock_formatted", "avg_shipped_qty_2dp", "stock_days_2dp", "rop", "rop_date", "recommended_mode"]][(st.session_state.df["hub"]==hub) & (st.session_state.df["days_to_rop"] <= st.session_state[reorder_days_keys])].groupby("sku").min().reset_index() | |
| df_reorder.columns = ["SKU","Stocks on hand", "Average monthly demand", "Stock days", "Reorder Point", "Reorder Date", "Recommended Transport Mode"] | |
| st.dataframe(df_reorder.sort_values(by=["Reorder Date", "Stock days"]), use_container_width = True) | |
| else: | |
| st.subheader(f"Upcoming Reorder for {hub}") | |
| st.caption("Display stocks where the reorder date is within the specified time period") | |
| st.caption("Recommended transport mode would always be based on the lowest estimated handling cost. If we have reorder date is marked as 0 (meaning we have already pass the optimum reorder date), recommended transport mode would be based on the shortest lead time") | |
| st.slider("Stock Warning", 1, 90, 30, label_visibility = "collapsed", help = "Specify the time period to filter upcoming reorder point", key = reorder_days_keys) | |
| df_reorder = st.session_state.df[["sku","current_stock_formatted", "avg_shipped_qty_2dp", "stock_days_2dp", "rop","rop_date", "recommended_mode", "console_support"]][(st.session_state.df["hub"]==hub) & (st.session_state.df["days_to_rop"] <= st.session_state[reorder_days_keys])].groupby("sku").min().reset_index() | |
| df_reorder.columns = ["SKU","Stocks on hand", "Average monthly demand", "Stock days", "Reorder Point", "Reorder Date", "Recommended Transport Mode", "Support from R.Hub"] | |
| st.dataframe(df_reorder.sort_values(by=["Reorder Date", "Stock days"]), use_container_width = True) | |
| ############### | |
| ## STREAMLIT ## | |
| ############### | |
| rhub_tab, cvg_tab, dub_tab, sin_tab, slc_tab = st.tabs(["R.HUB", "CVG", "DUB", "SIN", "SLC"]) | |
| with rhub_tab: | |
| display_stock_warn_reorder("R.HUB") | |
| with cvg_tab: | |
| display_stock_warn_reorder("CVG") | |
| with dub_tab: | |
| display_stock_warn_reorder("DUB") | |
| with sin_tab: | |
| display_stock_warn_reorder("SIN") | |
| with slc_tab: | |
| display_stock_warn_reorder("SLC") | |