Update app.py

app.py

@@ -415,16 +415,50 @@ def match_pdf_to_athlete(pdfs_data, athlete_name):
     target = normalize_name(athlete_name)
     target_parts = set(target.split())
 
+    best_pdf = None
+    best_score = 0
+
     for pdf in pdfs_data:
-        pdf_name = normalize_name(pdf.get("athlete_name"))
-        pdf_parts = set(pdf_name.split())
+        pdf_name_raw = pdf.get("athlete_name")
+        pdf_name = normalize_name(pdf_name_raw)
 
-        # match si au moins 2 mots en commun
-        if len(target_parts.intersection(pdf_parts)) >= 2:
+        # 1) match exact
+        if pdf_name == target:
             return pdf
 
+        # 2) match par mots communs
+        pdf_parts = set(pdf_name.split())
+        common_parts = target_parts.intersection(pdf_parts)
+        score = len(common_parts)
+
+        if score > best_score:
+            best_score = score
+            best_pdf = pdf
+
+        # 3) fallback si un nom contient l'autre
+        if target and pdf_name:
+            if target in pdf_name or pdf_name in target:
+                best_pdf = pdf
+                best_score = max(best_score, 1)
+
+    # si on a au moins 1 mot en commun, on accepte
+    if best_score >= 1:
+        return best_pdf
+
+    # fallback final : s'il n'y a qu'un seul PDF uploadé, on le prend
+    if len(pdfs_data) == 1:
+        return pdfs_data[0]
+
     return None
 
+matched_pdf = match_pdf_to_athlete(pdfs_data, selected_athlete)
+
+st.write("Athlète CSV sélectionné :", selected_athlete)
+
+for i, pdf in enumerate(pdfs_data):
+    st.write(f"PDF {i+1} - athlete_name extrait :", pdf.get("athlete_name"))
+    st.write(f"PDF {i+1} - source_pdf :", pdf.get("source_pdf"))
+
 # =========================================================
 # ANALYSE V3
 # =========================================================