add: app .py 파일 추가

chatbot_ver1/app.py

@@ -0,0 +1,175 @@
+import os
+import yaml
+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+import torch
+import shutil
+import tempfile
+
+# 파일 경로
+KNOWLEDGE_FILE = "company_knowledge.md"
+PERSONA_FILE = "persona.yaml"
+CHITCHAT_FILE = "chitchat.yaml"
+KEYWORD_MAP_FILE = "keyword_map.yaml"
+CEO_VIDEO_FILE = "ceo_video.mp4"
+CEO_IMG_FILE = "ceo.jpg" # 필요시 사용
+
+def load_yaml(file_path, default_data=None):
+    try:
+        with open(file_path, "r", encoding="utf-8") as f:
+            return yaml.safe_load(f)
+    except Exception:
+        return default_data if default_data is not None else []
+
+def parse_knowledge_base(file_path):
+    import re
+    faqs = []
+    if not os.path.exists(file_path):
+        return []
+    with open(file_path, encoding="utf-8") as f:
+        content = f.read()
+    # Q:\s*(...) \nA:\s*(...)\n{2,} 또는 끝
+    blocks = re.findall(r"Q:\s*(.*?)\nA:\s*(.*?)(?=(\n{2,}Q:|\Z))", content, re.DOTALL)
+    for q, a, _ in blocks:
+        faqs.append({"question": q.strip(), "answer": a.strip()})
+    return faqs
+
+# 데이터 로드
+persona = load_yaml(PERSONA_FILE, {})
+chitchat_map = load_yaml(CHITCHAT_FILE, [])
+keyword_map = load_yaml(KEYWORD_MAP_FILE, [])
+knowledge_base = parse_knowledge_base(KNOWLEDGE_FILE)
+kb_questions = [item['question'] for item in knowledge_base]
+kb_answers = [item['answer'] for item in knowledge_base]
+
+# 무료 임베딩 모델
+model = SentenceTransformer('distilbert-base-multilingual-cased')
+if kb_questions:
+    kb_embeddings = model.encode(kb_questions, convert_to_tensor=True)
+else:
+    kb_embeddings = None
+
+# 삭제선(취소선) 적용 함수
+def apply_strike(text, del_section="6000~6500만원, 성과급 1800~2400만원"):
+    # 급여 정보가 포함된 답변일 때만 strike-through  
+    if del_section in text:
+        return text.replace(
+            del_section,
+            f"<s>{del_section}</s>"
+        )
+    return text
+
+# Chitchat(인사 등) 매칭
+def find_chitchat(user_question):
+    uq = user_question.lower()
+    for chat in chitchat_map:
+        if any(kw in uq for kw in chat.get('keywords', [])):
+            return chat['answer']
+    return None
+
+# 키워드 기반 Q 매핑 (복지: 휴가 제도, 교육, 복리후생 등 강화)
+def map_user_question_to_knowledge(user_question):
+    uq = user_question.lower()
+    for item in keyword_map:
+        for kw in item.get('keywords', []):
+            if kw in uq:
+                return item['question']
+    return None
+
+def find_answer_by_question(q):
+    for item in knowledge_base:
+        if item['question'] == q:
+            return item['answer']
+    return None
+
+def find_answer_by_keywords(user_question):
+    uq = user_question.lower()
+    for item in knowledge_base:
+        for kw in item.get('keywords', []):
+            if kw in uq:
+                return item['answer']
+    return None
+
+def best_faq_answer(user_question):
+    uq = user_question.strip()
+    if not uq:
+        return "무엇이 궁금하신지 말씀해 주세요!"
+    chit = find_chitchat(uq)
+    if chit:
+        return chit
+    # (1) 키워드맵 우선 매핑 (복지/급여 각각 분리)
+    mapped_q = map_user_question_to_knowledge(uq)
+    if mapped_q:
+        answer = find_answer_by_question(mapped_q)
+        if answer:
+            # 복지 분야: '연봉 수준' 답변 아닌 경우에는 삭제선 없음
+            if "연봉" in mapped_q:
+                return apply_strike(answer)
+            return answer
+    # (2) knowledge_base 직접 키워드 매칭 (복지 관련 키워드 강화되어야 함!)
+    answer = find_answer_by_keywords(uq)
+    if answer:
+        return answer
+    # (3) 임베딩 유사도 기반 soft-matching
+    if kb_embeddings is not None and len(kb_answers) > 0:
+        q_emb = model.encode([uq], convert_to_tensor=True)
+        scores = util.cos_sim(q_emb, kb_embeddings)[0]
+        best_idx = int(torch.argmax(scores))
+        best_question = kb_questions[best_idx]
+        # 복지질문인데 연봉키워드 매칭되는 경우, 복지 우선 답변을 선택하도록
+        # 아래 if식은 실제 복지 답변 우선 코드
+        복지가능 = ["복지", "휴가", "교육", "행사", "동호회", "복리후생", "제도"]
+        연봉가능 = ["연봉", "급여", "월급", "임금", "보상", "봉급", "처우"]
+        if any(w in uq for w in 복지가능) and not any(w in best_question for w in 연봉가능):
+            return kb_answers[best_idx]
+        # 삭제선은 연봉 답변에만
+        if "연봉" in best_question or "급여" in best_question:
+            return apply_strike(kb_answers[best_idx])
+        return kb_answers[best_idx]
+    # (4) fallback
+    return persona.get('style', {}).get('unknown_answer', "아직 준비되지 않은 질문입니다. 다른 질문도 해주세요!")
+
+# 질문 받을 때마다 CEO 영상 복사본 임시파일로 생성 → autoplay 확실
+def get_temp_video_copy():
+    temp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    temp_filepath = temp_file.name
+    temp_file.close()
+    shutil.copyfile(CEO_VIDEO_FILE, temp_filepath)
+    return temp_filepath
+
+def chat_interface(message, history):
+    bot_response = best_faq_answer(message)
+    history.append((message, bot_response))
+    temp_video_path = get_temp_video_copy()
+    # 텍스트에 html가능하면 answer에 html삭제선 유지
+    return history, "", gr.update(value=temp_video_path, autoplay=True, interactive=False, elem_id="ceo-video-panel")
+
+with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
+    with gr.Row(elem_id="main-row"):
+        with gr.Column(scale=1, min_width=350):
+            video_player = gr.Video(
+                value=CEO_VIDEO_FILE,
+                autoplay=False, loop=False, interactive=False,
+                height=350, elem_id="ceo-video-panel"
+            )
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot(
+                label="",
+                height=350,
+                elem_id="chatbot-box",
+                show_copy_button=True
+            )
+            with gr.Row():
+                msg_input = gr.Textbox(placeholder="무엇이든 물어보세요.", scale=4, show_label=False)
+                send_btn = gr.Button("전송", scale=1, min_width=80)
+            gr.Examples(
+                examples=["복지 뭐 있어?", "휴가 제도 설명해줘", "연봉 알려줘", "동호회 행사?", "식사제공?", "주력제품", "조직문화"],
+                inputs=msg_input
+            )
+    # 연결
+    outputs_list = [chatbot, msg_input, video_player]
+    msg_input.submit(chat_interface, [msg_input, chatbot], outputs_list)
+    send_btn.click(chat_interface, [msg_input, chatbot], outputs_list)
+
+if __name__ == "__main__":
+    demo.launch()