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import streamlit as st
import pandas as pd
"""
Result table of the Multi Project Matching
"""
def show_multi_table(p1_df, p2_df):
"""
p1_df & p2_df from functions/multi_project_matching
"""
st.write("------------------")
p1_df = p1_df.reset_index(drop=True)
p2_df = p2_df.reset_index(drop=True)
actual_ind = 0
# Loop to displaye every matching pair from p1 and p2 dfs
for i in range(len(p1_df) - 1, -1, -2): # stepsize 2 to not display duplicates
actual_ind += 1
match_df = pd.DataFrame()
row_from_p1 = p1_df.iloc[[i]]
row_from_p2 = p2_df.iloc[[i]]
# INTEGRATE IN PREPROCESSING !!!
# transform strings to list
"""
Add this to preprocessing
- flag url
- crs code lists
"""
try:
row_from_p1["crs_3_code_list"] = [row_from_p1['crs_3_name'].item().split(";")[:-1]]
row_from_p2["crs_3_code_list"] = [row_from_p2['crs_3_name'].item().split(";")[:-1]]
except:
row_from_p1["crs_3_code_list"] = [""]
row_from_p2["crs_3_code_list"] = [""]
try:
row_from_p1["crs_5_code_list"] = [row_from_p1['crs_5_name'].item().split(";")[:-1]]
row_from_p2["crs_5_code_list"] = [row_from_p2['crs_5_name'].item().split(";")[:-1]]
except:
row_from_p1["crs_5_code_list"] = [""]
row_from_p2["crs_5_code_list"] = [""]
row_from_p1["sdg_list"] = [row_from_p1['sgd_pred_code'].item()]
row_from_p2["sdg_list"] = [row_from_p2['sgd_pred_code'].item()]
try:
row_from_p1["flag"] = f"https://flagicons.lipis.dev/flags/4x3/{row_from_p1['country'].item()[:2].lower()}.svg"
row_from_p2["flag"] = f"https://flagicons.lipis.dev/flags/4x3/{row_from_p2['country'].item()[:2].lower()}.svg"
except:
row_from_p1["flag"] = "https://flagicons.lipis.dev/flags/4x3/xx.svg"
row_from_p2["flag"] = "https://flagicons.lipis.dev/flags/4x3/xx.svg"
# concat p1_df and p2_df rows
match_df = pd.concat([row_from_p1, row_from_p2], ignore_index=True)
col1, col2 = st.columns([1, 12])
# MATCHING INFOS
with col1:
# remove arrow from standart st.metric()
st.write(
"""
<style>
[data-testid="stMetricDelta"] svg {
display: none;
}
</style>
""",
unsafe_allow_html=True,
)
st.metric(label="Match", value=f"{actual_ind}", delta=f"~ {str(round(row_from_p1['similarity'].item(), 5) * 100)[:4]} %")
# MATCHING Project Informations as table
with col2:
st.write(" ")
st.dataframe(
match_df[["iati_id", "title_main", "orga_abbreviation", "client", "description_main", "country_name", "flag", "sdg_list", "crs_3_code_list", "crs_5_code_list"]],
use_container_width = True,
height = 35 + 35 * len(match_df),
column_config={
"iati_id": st.column_config.TextColumn(
"IATI ID",
help="IATI Project ID",
disabled=True,
width="small"
),
"orga_abbreviation": st.column_config.TextColumn(
"Organization",
help="If description not in English, description in other language provided",
disabled=True,
width="small"
),
"client": st.column_config.TextColumn(
"Client",
help="Client organization of customer",
disabled=True,
width="small"
),
"title_main": st.column_config.TextColumn(
"Title",
help="If title not in English, title in other language provided",
disabled=True,
width="large"
),
"description_main": st.column_config.TextColumn(
"Description",
help="If description not in English, description in other language provided",
disabled=True,
width="large"
),
"country_name": st.column_config.TextColumn(
"Country",
help="Country of project",
disabled=True,
width="small"
),
"flag": st.column_config.ImageColumn(
"Flag",
help="country flag",
width="small"
),
"sdg_list": st.column_config.ListColumn(
"SDG Prediction",
help="Prediction of SDG's",
width="small"
),
"crs_3_code_list": st.column_config.ListColumn(
"CRS 3",
help="CRS 3 code given by organization",
width="medium"
),
"crs_5_code_list": st.column_config.ListColumn(
"CRS 5",
help="CRS 5 code given by organization",
width="medium"
),
},
hide_index=True,
)
st.write("------------------") |