render_dl_execution_tab

src/gradio/pages/dl_execution_tab.py

@@ -34,7 +34,7 @@ def render_dl_execution_tab(
         df_no_exec = df.copy()
 
     has_name_col = "exercise_name" in df_no_exec.columns
-    exercice_choices = (
+    exercice_choices_all = (
         sorted(df_no_exec["exercise_name"].dropna().unique().tolist())
         if has_name_col
         else []
@@ -49,11 +49,18 @@ def render_dl_execution_tab(
 
         gr.Markdown("### Program details")
 
+        # ---- Zone de recherche + dropdown filtrée ----
+        with gr.Row():
+            search_box = gr.Textbox(
+                label="Filter programs (by keyword)",
+                placeholder="Type part of the program name (e.g. squat, push, arms)...",
+            )
+
         with gr.Row():
             exercice_selector = gr.Dropdown(
                 label="Select a program without execution",
-                choices=exercice_choices,
-                value=exercice_choices[0] if exercice_choices else None,
+                choices=[],   # on remplit dynamiquement
+                value=None,
             )
 
         details_md = gr.Markdown(
@@ -120,12 +127,37 @@ def render_dl_execution_tab(
             label="Metrics",
         )
 
-        # Callback de mise à jour details + tableau + prompt
+        # ---------- Callbacks ----------
+
+        # 1) Filtrer la liste d'exos en fonction de la recherche
+        def _filter_programs(query: str):
+            if not exercice_choices_all:
+                return gr.update(choices=[], value=None)
+
+            if not query:
+                # si aucune recherche → on ne propose que les 30 premiers
+                subset = exercice_choices_all[:30]
+            else:
+                q = query.lower().strip()
+                subset = [
+                    name for name in exercice_choices_all
+                    if q in name.lower()
+                ][:50]  # on limite à 50 résultats
+
+            if not subset:
+                subset = ["No match found"]
+                value = "No match found"
+            else:
+                value = subset[0]
+
+            return gr.update(choices=subset, value=value)
+
+        # 2) Mise à jour details + tableau + prompt
         def _format_details_exec(ex_name: str):
             empty_df = pd.DataFrame(columns=selected_cols)
             empty_prompt = ""
 
-            if not ex_name:
+            if not ex_name or ex_name == "No match found":
                 return (
                     "Select a program **without execution description** "
                     "to see its details.",
@@ -165,8 +197,23 @@ def render_dl_execution_tab(
             df_summary, df_model_df, df_training_df, df_metrics_df = get_dl_execution_model_report_components()
             return df_summary, df_model_df, df_training_df, df_metrics_df
 
+        # ---------- Wiring des événements ----------
+
+        # mise à jour des choix de la dropdown quand on tape dans la recherche
+        search_box.change(
+            _filter_programs,
+            inputs=search_box,
+            outputs=exercice_selector,
+        )
 
+        # Optionnel : pré-remplir au chargement du tab avec les 30 premiers
+        tab_dl_exec.select(
+            lambda: _filter_programs(""),
+            inputs=None,
+            outputs=exercice_selector,
+        )
 
+        # mise à jour des détails quand on choisit un programme
         exercice_selector.change(
             _format_details_exec,
             inputs=exercice_selector,
@@ -180,9 +227,10 @@ def render_dl_execution_tab(
             outputs=generated_exec,
         )
 
+        # Rapport modèle DL au select du tab
         tab_dl_exec.select(
             _update_exec_report,
-            inputs=None,  # ou [] mais None évite le warning
+            inputs=None,
             outputs=[dl_summary, dl_model, dl_training, dl_metrics],
         )