Réarrangements résultats #58

src/assets/style.css

@@ -146,7 +146,7 @@ td[data-dash-column="objet"],td[data-dash-column="titulaire_nom"],td[data-dash-c
 /* Page de recherche */
 
 #search {
-  margin: auto;
+  margin: 50px auto 0px auto;
   width: 450px;
   font-size: 18px;
   height: 30px;
@@ -162,6 +162,16 @@ td[data-dash-column="objet"],td[data-dash-column="titulaire_nom"],td[data-dash-c
   margin-right: 12px;
 }
 
+.results_acheteur {
+  grid-column: 1;
+  grid-row: 1;
+}
+
+.results_titulaire {
+  grid-column: 2;
+  grid-row: 1;
+}
+
 /* Menu de navigation */
 
 .navbar {
@@ -191,7 +201,7 @@ summary > h3 {
   padding-top: 28px;
 }
 
-/* Vue acheteur/titulaire    */
+/* Vue acheteur/titulaire/recherche    */
 .wrapper {
   display: grid;
   grid-gap: 10px;
@@ -199,9 +209,6 @@ summary > h3 {
   justify-content: space-between;
 }
 
-.wrapper > div {
-}
-
 .org_title {
   grid-column: 1 / 3;
   grid-row: 1;

src/pages/recherche.py

@@ -32,7 +32,7 @@ layout = html.Div(
         #     className="search_options",
         #     children=[dcc.RadioItems(options=["Acheteur(s)"])],
         # ),
-        html.Div(id="search_results"),
+        html.Div(id="search_results", className="wrapper"),
     ],
 )
 
@@ -62,13 +62,37 @@ def update_search_results(query):
             columns, tooltip = setup_table_columns(results, hideable=False)
 
             org_content = [
-                html.H3(f"{org_type.title()}s : {count}"),
+                html.Div(
-                dash_table.DataTable(
+                    className=f"results_{org_type}",
-                    columns=columns,
+                    children=[
-                    data=results.to_dicts(),
+                        html.H3(f"{org_type.title()}s : {count}"),
-                    page_size=5,
+                        dash_table.DataTable(
-                    style_table={"overflowX": "auto"},
+                            columns=columns,
-                    markdown_options={"html": True},
+                            data=results.to_dicts(),
+                            page_size=10,
+                            # style_table={"overflowX": "auto"},
+                            markdown_options={"html": True},
+                            cell_selectable=False,
+                            style_cell_conditional=[
+                                {
+                                    "if": {"column_id": "acheteur_nom"},
+                                    "maxWidth": "250px",
+                                    "textAlign": "left",
+                                    "overflow": "hidden",
+                                    "lineHeight": "18px",
+                                    "whiteSpace": "normal",
+                                },
+                                {
+                                    "if": {"column_id": "titulaire_nom"},
+                                    "maxWidth": "250px",
+                                    "textAlign": "left",
+                                    "overflow": "hidden",
+                                    "lineHeight": "18px",
+                                    "whiteSpace": "normal",
+                                },
+                            ],
+                        ),
+                    ],
                 )
                 if count > 0
                 else html.P(f"Aucun {org_type} trouvé."),

src/utils.py

@@ -234,7 +234,7 @@ def get_org_data(dff: pl.DataFrame, org_type: str) -> pl.DataFrame:
             f"{org_type}_latitude", f"{org_type}_longitude"
         ),
     )
-    lff = lff.group_by(cs.starts_with(org_type)).len("len_uid")
+    lff = lff.group_by(cs.starts_with(org_type)).len("Marchés")
     return lff.collect()
 
 
@@ -406,10 +406,11 @@ def search_org(dff: pl.DataFrame, query: str, org_type: str) -> pl.DataFrame:
     if not query.strip():
         return dff.select(pl.lit(False).alias("matches"))
 
+    sleep(0.2)
+
     # Normalize query
     normalized_query = unidecode(query.strip()).upper()
     tokens = [" " + t.strip() for t in normalized_query.split() if t.strip()]
-    print(tokens)
 
     # Define columns based on entity type
     cols = [
@@ -440,15 +441,15 @@ def search_org(dff: pl.DataFrame, query: str, org_type: str) -> pl.DataFrame:
 
     # Sélection des colonnes
     if org_type == "acheteur":
-        dff = dff.select(cols + ["len_uid"])
+        dff = dff.select(cols + ["Marchés"])
     if org_type == "titulaire":
-        dff = dff.select(cols + ["len_uid", "titulaire_typeIdentifiant"])
+        dff = dff.select(cols + ["Marchés", "titulaire_typeIdentifiant"])
 
     # Apply and filter
     dff = (
         dff.with_columns(token_matches + [match_score])
         .filter(pl.col("match_score") == len(tokens))
-        .sort("len_uid", descending=True)
+        .sort("Marchés", descending=True)
         .drop([f"token_{token}" for token in tokens])
     )
 
@@ -463,7 +464,7 @@ def search_org(dff: pl.DataFrame, query: str, org_type: str) -> pl.DataFrame:
         ).alias("Département")
     )
 
-    dff = dff.select(f"{org_type}_id", f"{org_type}_nom", "Département")
+    dff = dff.select(f"{org_type}_id", f"{org_type}_nom", "Département", "Marchés")
 
     return dff