Recherche fonctinonelle acheteurs et titulaires #58
This commit is contained in:
Colin Maudry
+2
-1
@@ -16,7 +16,8 @@ dependencies = [
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"xlsxwriter",
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"plotly[express]",
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"httpx",
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"pandas" # utilisé pour la création de certains graphiques
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"pandas", # utilisé pour la création de certains graphiques
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"unidecode"
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]
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[project.optional-dependencies]
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+48
-9
@@ -1,6 +1,12 @@
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from dash import Input, Output, callback, dcc, html, register_page
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from dash import Input, Output, callback, dash_table, dcc, html, register_page
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from src.utils import meta_content
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from src.utils import (
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df_acheteurs,
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df_titulaires,
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meta_content,
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search_org,
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setup_table_columns,
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)
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name = "Recherche"
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@@ -31,11 +37,44 @@ layout = html.Div(
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)
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@callback(Output("search_results", "children"), Input("search", "value"))
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def search_results(query):
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if len(query) >= 3:
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# results = []
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# dff = dff.select("acheteur_id", "acheteur_nom")
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return html.Div([])
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@callback(
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Output("search_results", "children"),
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Input("search", "value"),
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prevent_initial_call=True,
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)
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def update_search_results(query):
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if len(query) >= 1:
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content = []
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for org_type in ["acheteur", "titulaire"]:
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if org_type == "acheteur":
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dff = df_acheteurs
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elif org_type == "titulaire":
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dff = df_titulaires
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else:
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raise ValueError(f"{org_type} is not supported")
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# Search acheteurs and titulaires using the same function
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results = search_org(dff, query, org_type=org_type)
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count = results.height
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# Format output
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columns, tooltip = setup_table_columns(results, hideable=False)
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org_content = [
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html.H3(f"{org_type.title()}s : {count}"),
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dash_table.DataTable(
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columns=columns,
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data=results.to_dicts(),
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page_size=5,
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style_table={"overflowX": "auto"},
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markdown_options={"html": True},
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)
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if count > 0
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else html.P(f"Aucun {org_type} trouvé."),
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]
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content.extend(org_content)
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return content
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else:
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return html.P("Tapez au moins 3 caractères")
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return html.P("Tapez au moins 1 caractère")
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+119
-26
@@ -8,6 +8,7 @@ import polars.selectors as cs
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from httpx import get
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from polars import Schema
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from polars.exceptions import ComputeError
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from unidecode import unidecode
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operators = [
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["s<", "<"],
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@@ -50,30 +51,47 @@ def add_resource_link(dff: pl.DataFrame) -> pl.DataFrame:
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return dff
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def add_links(dff: pl.DataFrame):
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dff = dff.with_columns(
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pl.when(pl.col("titulaire_typeIdentifiant") == "SIRET")
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.then(
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'<a href = "/titulaires/'
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+ pl.col("titulaire_id")
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+ '">'
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+ pl.col("titulaire_id")
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+ "</a>"
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)
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.otherwise(pl.col("titulaire_id"))
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.alias("titulaire_id")
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)
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for column, path in [("acheteur_id", "acheteurs"), ("uid", "marches")]:
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dff = dff.with_columns(
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(
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f'<a href = "/{path}/'
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+ pl.col(column)
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+ '" target="_blank">'
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+ pl.col(column)
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+ "</a>"
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).alias(column)
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)
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def add_links(dff: pl.DataFrame, target: str = "_blank"):
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for col in ["uid", "acheteur_nom", "titulaire_nom", "acheteur_id", "titulaire_id"]:
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if col in dff.columns:
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if col.startswith("titulaire_"):
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dff = dff.with_columns(
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pl.when(
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pl.Expr.or_(
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pl.col("titulaire_typeIdentifiant").is_null(),
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pl.col("titulaire_typeIdentifiant") == "SIRET",
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)
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)
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.then(
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'<a href = "/titulaires/'
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+ pl.col("titulaire_id")
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+ f'" target="{target}">'
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+ pl.col(col)
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+ "</a>"
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)
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.otherwise(pl.col(col))
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.alias(col)
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)
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if col.startswith("acheteur_"):
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dff = dff.with_columns(
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(
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'<a href = "/acheteurs/'
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+ pl.col("acheteur_id")
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+ f'" target="{target}">'
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+ pl.col(col)
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+ "</a>"
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).alias(col)
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)
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if col == "uid":
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dff = dff.with_columns(
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(
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'<a href = "/marches/'
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+ pl.col("uid")
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+ f'" target="{target}">'
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+ pl.col("uid")
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+ "</a>"
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).alias("uid")
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)
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return dff
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@@ -211,11 +229,12 @@ def get_decp_data() -> pl.DataFrame:
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def get_org_data(dff: pl.DataFrame, org_type: str) -> pl.DataFrame:
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lff = dff.lazy()
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lff = lff.select(
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"uid",
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cs.starts_with(org_type).exclude(
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f"{org_type}_latitude", f"{org_type}_longitude"
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)
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),
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)
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lff = lff.unique(f"{org_type}_id")
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lff = lff.group_by(cs.starts_with(org_type)).len("len_uid")
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return lff.collect()
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@@ -375,6 +394,80 @@ def get_data_schema() -> dict:
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return new_schema
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def search_org(dff: pl.DataFrame, query: str, org_type: str) -> pl.DataFrame:
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"""
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Search in either 'acheteur' or 'titulaire' DataFrame.
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:param dff: Polars DataFrame with acheteur or titulaire columns
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:param query: User search string
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:param org_type: 'acheteur' or 'titulaire'
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:return: Filtered DataFrame with 'matches' column
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"""
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if not query.strip():
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return dff.select(pl.lit(False).alias("matches"))
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# Normalize query
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normalized_query = unidecode(query.strip()).upper()
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tokens = [" " + t.strip() for t in normalized_query.split() if t.strip()]
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print(tokens)
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# Define columns based on entity type
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cols = [
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f"{org_type}_id",
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f"{org_type}_nom",
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f"{org_type}_departement_nom",
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f"{org_type}_departement_code",
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f"{org_type}_commune_nom",
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]
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# Concatenate all fields into one string per row
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org_str = pl.concat_str(pl.lit(" "), pl.col(cols), separator=" ")
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# For each token, create a boolean column: True if token is found
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token_matches = []
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for token in tokens:
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token_match = org_str.str.contains(token).alias(f"token_{token}")
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token_matches.append(token_match)
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# Count how many tokens match per row
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match_score = pl.sum_horizontal(token_matches).alias("match_score")
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# For each token, create a boolean column: True if token is found
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token_matches = []
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for token in tokens:
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token_match = org_str.str.contains(token).alias(f"token_{token}")
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token_matches.append(token_match)
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# Sélection des colonnes
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if org_type == "acheteur":
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dff = dff.select(cols + ["len_uid"])
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if org_type == "titulaire":
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dff = dff.select(cols + ["len_uid", "titulaire_typeIdentifiant"])
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# Apply and filter
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dff = (
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dff.with_columns(token_matches + [match_score])
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.filter(pl.col("match_score") == len(tokens))
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.sort("len_uid", descending=True)
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.drop([f"token_{token}" for token in tokens])
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)
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# Format result
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dff = add_links(dff, target="")
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dff = dff.with_columns(
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pl.concat_str(
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pl.col(f"{org_type}_departement_nom"),
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pl.lit(" ("),
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pl.col(f"{org_type}_departement_code"),
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pl.lit(")"),
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).alias("Département")
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)
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dff = dff.select(f"{org_type}_id", f"{org_type}_nom", "Département")
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return dff
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df: pl.DataFrame = get_decp_data()
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df_acheteurs = get_org_data(df, "acheteur")
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df_titulaires = get_org_data(df, "titulaire")
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