Update app.py

app.py

@@ -27,7 +27,8 @@ def process_resumes(files, candidate_id: str, additional_notes: str = ""):
     partial_records = []
     raw_texts = []
 
-    for path in files:  # ← 'filepath' なので文字列パス
+    # files は 'filepath' 前提
+    for path in files:
         filepath = str(path)
         filename = os.path.basename(filepath)
         with open(filepath, "rb") as fp:
@@ -35,7 +36,7 @@ def process_resumes(files, candidate_id: str, additional_notes: str = ""):
 
         filetype = detect_filetype(filename, raw_bytes)
 
-        # 1) テキスト抽出：画像/PDFはOpenAI Vision OCR、docx/txtは生文面＋OpenAI整形
+        # 1) 抽出
         if filetype in {"pdf", "image"}:
             text = extract_text_with_openai(raw_bytes, filename=filename, filetype=filetype)
         else:
@@ -44,7 +45,7 @@ def process_resumes(files, candidate_id: str, additional_notes: str = ""):
 
         raw_texts.append({"filename": filename, "text": text})
 
-        # 2) OpenAIでセクション構造化 → ルール正規化
+        # 2) 構造化 → 正規化
         structured = structure_with_openai(text)
         normalized = normalize_resume({
             "work_experience": structured.get("work_experience_raw", ""),
@@ -59,10 +60,10 @@ def process_resumes(files, candidate_id: str, additional_notes: str = ""):
             "normalized": normalized,
         })
 
-    # 3) 統合（複数ファイル→1候補者）
+    # 3) 統合
     merged = merge_normalized_records([r["normalized"] for r in partial_records])
 
-    # 4) スキル抽出（辞書/正規表現）
+    # 4) スキル抽出
     merged_text = "\n\n".join([r["text"] for r in partial_records])
     skills = extract_skills(merged_text, {
         "work_experience": merged.get("raw_sections", {}).get("work_experience", ""),
@@ -78,7 +79,7 @@ def process_resumes(files, candidate_id: str, additional_notes: str = ""):
     # 6) 品質スコア
     score = compute_quality_score(merged_text, merged)
 
-    # 7) 要約（300/100/1文）
+    # 7) 要約
     summaries = summarize_with_openai(merged_text)
 
     # 8) 構造化出力
@@ -111,7 +112,7 @@ def process_resumes(files, candidate_id: str, additional_notes: str = ""):
 
     return (
         json.dumps(result_json, ensure_ascii=False, indent=2),
-        skills,
+        json.dumps(skills, ensure_ascii=False, indent=2),  # ← gr.Code に渡すため文字列化
         json.dumps(score, ensure_ascii=False, indent=2),
         summaries["300chars"],
         summaries["100chars"],
@@ -129,7 +130,7 @@ with gr.Blocks(title=APP_TITLE) as demo:
             label="レジュメ類 (PDF/画像/Word/テキスト) 複数可",
             file_count="multiple",
             file_types=[".pdf", ".png", ".jpg", ".jpeg", ".tiff", ".bmp", ".docx", ".txt"],
-            type="filepath",  # ← 修正点
+            type="filepath",
         )
         candidate_id = gr.Textbox(label="候補者ID（任意。未入力なら自動生成）")
     notes = gr.Textbox(label="補足メモ（任意）", lines=3)
@@ -140,7 +141,7 @@ with gr.Blocks(title=APP_TITLE) as demo:
         out_json = gr.Code(label="統合出力 (JSON)")
 
     with gr.Tab("抽出スキル"):
-        out_skills = gr.JSON(label="スキル一覧")
+        out_skills = gr.Code(label="スキル一覧 (JSON)")  # ← gr.JSON から gr.Code に変更
 
     with gr.Tab("品質スコア"):
         out_score = gr.Code(label="品質評価")
@@ -164,4 +165,5 @@ with gr.Blocks(title=APP_TITLE) as demo:
 
 
 if __name__ == "__main__":
-    demo.launch()
+    # Spaces では localhost に直接アクセスできないケースがあるため share=True を明示
+    demo.launch(share=True)