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import streamlit as st |
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import pandas as pd |
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def show_table(p1_df, p2_df): |
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st.write("------------------") |
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p1_df = p1_df.reset_index(drop=True) |
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p2_df = p2_df.reset_index(drop=True) |
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for i in range(len(p1_df) - 1, -1, -2): |
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match_df = pd.DataFrame() |
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row_from_p1 = p1_df.iloc[[i]] |
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row_from_p2 = p2_df.iloc[[i]] |
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match_df = pd.concat([row_from_p1, row_from_p2], ignore_index=True) |
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st.dataframe( |
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match_df[["similarity", "iati_id", "title_main", "orga_abbreviation", "client", "description_main", "country", "sgd_pred_code", "crs_3_code", "crs_5_code"]], |
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use_container_width = True, |
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height = 35 + 35 * len(match_df), |
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column_config={ |
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"similarity": st.column_config.TextColumn( |
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"Similarity", |
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help="simialrity", |
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disabled=True |
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), |
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"iati_id": st.column_config.TextColumn( |
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"IATI ID", |
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help="IATI Project ID", |
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disabled=True |
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), |
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"orga_abbreviation": st.column_config.TextColumn( |
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"Organization", |
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help="If description not in English, description in other language provided", |
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disabled=True |
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), |
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"client": st.column_config.TextColumn( |
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"Client", |
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help="Client organization of customer", |
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disabled=True |
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), |
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"title_main": st.column_config.TextColumn( |
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"Title", |
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help="If title not in English, title in other language provided", |
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disabled=True |
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), |
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"description_main": st.column_config.TextColumn( |
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"Description", |
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help="If description not in English, description in other language provided", |
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disabled=True |
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), |
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"country": st.column_config.TextColumn( |
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"Country", |
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help="Country of project", |
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disabled=True |
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), |
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"sgd_pred_code": st.column_config.TextColumn( |
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"SDG Prediction", |
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help="Prediction of SDG's", |
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disabled=True |
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), |
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"crs_3_code": st.column_config.TextColumn( |
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"CRS 3", |
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help="CRS 3 code given by organization", |
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disabled=True |
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), |
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"crs_5_code": st.column_config.TextColumn( |
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"CRS 5", |
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help="CRS 5 code given by organization", |
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disabled=True |
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), |
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}, |
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hide_index=True, |
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) |
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st.write("------------------") |