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Main #4

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fafdc6e
Commit 20240426
matyohan Apr 26, 2024
b88b5e4
Merge branch 'main' of https://github.com/Snowflake-Labs/sfguide-gett…
matyohan Apr 26, 2024
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168 changes: 89 additions & 79 deletions app/src/libraries/streamlit.py
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Original file line number Diff line number Diff line change
@@ -1,90 +1,100 @@
# Import libraries
from snowflake.snowpark.context import get_active_session
from snowflake.snowpark.functions import sum, col, when, max, lag
from snowflake.snowpark import Window
from datetime import timedelta
import altair as alt
import streamlit as st
import pandas as pd
import altair as alt
from snowflake.snowpark.context import get_active_session
from snowflake.snowpark.functions import count_distinct,col,sum
import snowflake.permissions as permission
from sys import exit
import snowflake.permissions as permissions

##import plotly.express as px
import datetime as dt

# Set page config
st.set_page_config(layout="wide")
# Get current session
session = get_active_session()

def load_app(orders_table,site_recovery_table):
with st.spinner("Loading lead time, order status, and supplier performance. Please wait..."):
df = session.sql(f"SELECT t1.order_id,t2.ship_order_id,t1.material_name,t1.supplier_name, t1.quantity, t1.cost, t2.status, t2.lat, t2.lon FROM {orders_table} as t1 INNER JOIN MFG_SHIPPING as t2 ON t2.ORDER_ID = t1.ORDER_ID ORDER BY t1.order_id")
df_order_status = df.group_by('status').agg(count_distinct('order_id').as_('TOTAL RECORDS')).order_by('status').to_pandas()
#TITRE
st.header("ACTIN TABLE COMPARISON")

df_cal_lead_time = session.sql(f"SELECT t1.order_id,t2.ship_order_id,t1.material_name,t1.supplier_name,t1.quantity,t1.cost,t2.status,t2.lat,t2.lon,cal_lead_time(t1.process_supply_day,t2.duration,t2.recovery_days) as lead_time FROM {orders_table} as t1 INNER JOIN (SELECT order_id, ship_order_id, status, duration, MFG_SHIPPING.lat, MFG_SHIPPING.lon, IFF(srt.recovery_weeks * 7::int = srt.recovery_weeks * 7,srt.recovery_weeks * 7,0) as recovery_days from MFG_SHIPPING LEFT OUTER JOIN {site_recovery_table} as srt ON MFG_SHIPPING.lon = srt.lon AND MFG_SHIPPING.lat = srt.lat) as t2 ON t2.ORDER_ID = t1.ORDER_ID ORDER BY t1.order_id")
df_supplier_perf = df_cal_lead_time.group_by('supplier_name').agg(sum(col('lead_time')).as_('TOTAL LEAD TIME')).sort('TOTAL LEAD TIME', ascending=True).limit(20).to_pandas()
df_lead_time = df_cal_lead_time.select('order_id','lead_time').sort('order_id', ascending=True).to_pandas()

with st.container():
col1,col2 = st.columns(2,gap='small')
with col1:
# Display Lead Time Status chart
st.subheader("Lead Time Status")
lead_time_base = alt.Chart(df_lead_time).encode(alt.X("ORDER_ID", title="ORDER ID", sort=None))
lead_time_base_bars = lead_time_base.mark_bar().encode(
color=alt.value("#249DC9"),
y=alt.Y("LEAD_TIME", title="LEAD TIME DAYS")
)
line = alt.Chart(pd.DataFrame({'y': [60]})).mark_rule(color='rgb(249,158,54)').encode(y='y')
lead_time_chart = alt.layer(lead_time_base_bars)
st.altair_chart(lead_time_chart + line, use_container_width=True)
st.subheader(":dart: COMPARE TABLES BETWEEN A SOURCE ENVIRONMENT AND A TARGET ENVIRONMENT")

st.write("----------------:one: Update settings file to set tables to compare")
st.write("----------------:two: Launch the tables comparison and save results in a new file")
st.write("----------------:three: Visualize and interpret results")


#PARTIE 1 MODIFIER LES TABLES A COMPARER EN INPUT
st.subheader(":wrench: 1 : SET THE LIST OF TABLES TO BE COMPARED")
st.write(" Informations required are the source (DATABASE_SOURCE, SCHEMA_SOURCE) and the target (DATABASE_CIBLE, SCHEMA_CIBLE) environments, as well as the table name (TABLE_NAME)")
st.write("DATE_SCOPE delimits the temporal scope with MIN_DATE and MAX_DATE bounds for both tables. If DATE_SCOPE is NULL, the entire table is used.")

#On affiche la table de paramétrage
table_list="TABLE_LIST"
table_list_df=session.table(table_list).to_pandas()

#On permet à l'utilisateur de modifier la table de paramétrage
with st.form("table write"):
df_edited=st.experimental_data_editor(table_list_df, use_container_width= True , num_rows="dynamic")
write_to_snowflake=st.form_submit_button("UPDATE")

#Mise à jour dans Snowflake
if write_to_snowflake:
with st.spinner("UPDATING..."):
try:
session.write_pandas(df_edited, table_list, overwrite = True)
session.sql('GRANT SELECT ON TABLE TABLE_LIST TO APPLICATION ROLE app_instance_role').collect()
except:
st.write('Error saving to table.')
st.success("The input table has been successfully updated in Snowflake!")

st.divider()


#PARTIE 2 LANCER LA COMPARAISON DES TABLES
st.subheader(":telescope: 2 : LAUNCH TABLE COMPARISON")


#Création d'un suffixe datetime pour identifier la table résultats de manière unique
current_time = dt.datetime.now()
tb_result_default=str(current_time.year)+('0'+str(current_time.month))[-2:]+('0'+str(current_time.day))[-2:]+'_'+('0'+str(current_time.hour))[-2:]+('0'+str(current_time.minute))[-2:]+('0'+str(current_time.second))[-2:]

#On permet à l'utilisateur de changer le nom de la table résultats

col1, col2, col3 = st.columns(3)
with col1:
tb_results_name = st.text_input( 'CHANGE THE TABLE NAME SUFFIX TO RECORD THE VARIOUS RESULTS :',value=tb_result_default)
composed_query="""CREATE or REPLACE TABLE """+tb_results_name +""" AS SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) ORDER BY 1;"""
with st.form("Proc stockées"):
launch_proc_stock=st.form_submit_button("LAUNCH TABLE COMPARISON")



#Lancer les procedures stockées
#with st.form("Proc stockées"):
# launch_proc_stock=st.form_submit_button("LANCER LA COMPARAISON DES TABLES")

if launch_proc_stock:
with st.spinner("CALCULATION IN PROGRESS..."):

df = session.sql("""call GET_DICTIONNARY_SOURCE('TABLE_LIST')""").collect()
df = session.sql("CREATE or REPLACE TABLE SOURCE_DICTIONNARY AS SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) ORDER BY 1;").collect()

def color_lead_time(val):
return f'background-color: rgb(249,158,54)'
#df = session.sql("""call GET_STATISTICS('SOURCE_DICTIONNARY')""").collect()
#df = session.sql("CREATE or REPLACE TABLE SOURCE_STATISTICS AS SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) ORDER BY 1;").collect()


with col2:
# Underlying Data
st.subheader("Orders with Lead Time Status >= 60days")
df_lead_time_60_days = df_cal_lead_time.select('lead_time','order_id','ship_order_id','material_name','supplier_name','quantity','cost').filter(col('lead_time') > 60).sort('lead_time').to_pandas()
df_lead_time_60_days['LEAD_TIME'] = df_lead_time_60_days['LEAD_TIME'].astype('int')
st.dataframe(df_lead_time_60_days.style.applymap(color_lead_time, subset=['LEAD_TIME']))
#df = session.sql("""call GET_DICTIONNARY_CIBLE('TABLE_LIST','PC_ALTERYX_DB.INFORMATION_SCHEMA.COLUMNS')""").collect()
#df = session.sql("CREATE or REPLACE TABLE CIBLE_DICTIONNARY AS SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) ORDER BY 1;").collect()

with st.container():
col1,col2 = st.columns(2,gap='small')
with col1:
# Display Supplier Performance
st.subheader("Supplier Performance")
supplier_perf_base = alt.Chart(df_supplier_perf).encode(alt.X("SUPPLIER_NAME:N", title="SUPPLIER NAME", sort=None))
supplier_perf_base_bars = supplier_perf_base.mark_bar().encode(
color=alt.value("#249DC9"),
y=alt.Y("TOTAL LEAD TIME", title="TOTAL LEAD TIME")
)
supplier_perf_chart = alt.layer(supplier_perf_base_bars)
st.altair_chart(supplier_perf_chart, use_container_width=True)

# Underlying Data
# st.subheader("Underlying Data")
# st.dataframe(df_lead_time)
#df = session.sql("""call GET_STATISTICS('CIBLE_DICTIONNARY')""").collect()
#df = session.sql("CREATE or REPLACE TABLE CIBLE_STATISTICS AS SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID())) ORDER BY 1;").collect()

with col2:
# Display Purchase Order Status
st.subheader("Purchase Order Status")
order_status_base = alt.Chart(df_order_status).encode(alt.X("STATUS", sort=['Order_confirmed','Shipped','In_Transit','Out_for_delivery','Delivered']))
order_status_base_bars = order_status_base.mark_bar().encode(
color=alt.value("#249DC9"),
y=alt.Y("TOTAL RECORDS", title="TOTAL RECORDS")
)
order_status_chart = alt.layer(order_status_base_bars)
st.altair_chart(order_status_chart, use_container_width=True)

# Underlying Data
# st.subheader("Underlying Data")
# st.dataframe(df_order_status)

orders_reference_associations = permission.get_reference_associations("order_table")
if len(orders_reference_associations) == 0:
permission.request_reference("order_table")
exit(0)

site_recovery_reference_associations = permission.get_reference_associations("site_recovery_table")
if len(site_recovery_reference_associations) == 0:
permission.request_reference("site_recovery_table")
exit(0)

st.title("Where Are My Ski Goggles?")
orders_table = "reference('order_table')"
site_recovery_table = "reference('site_recovery_table')"
load_app(orders_table,site_recovery_table)
#df = session.sql("""call COMPARE_STATISTICS('CIBLE_STATISTICS', 'SOURCE_STATISTICS')""").collect()
#df = session.sql(composed_query).collect()

st.success("Successfully run in Snowflake! Statistics ready for viewing! :point_down:")

st.divider()
11 changes: 6 additions & 5 deletions app/src/manifest.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -15,20 +15,21 @@ artifacts:
#runtime configuration for this version
configuration:
log_level: debug
trace_level: off
trace_level: always


references:
- order_table:
label: "Orders Table"
description: "Select table"
label: "information_schema"
description: "Give the access to the information_schema.column table"
privileges:
- SELECT
object_type: Table
multi_valued: false
register_callback: app_instance_schema.update_reference
- site_recovery_table:
label: "Site Recovery Table"
description: "Select table"
label: "Prod_Table"
description: "Provide access to the prod table"
privileges:
- SELECT
object_type: Table
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