Hotfix: Add monkeypatch for HfFolder ImportError

app.py

@@ -1,3 +1,15 @@
+# [몽키 패치] Gradio와 huggingface_hub 버전 갈등 해결
+import huggingface_hub
+if not hasattr(huggingface_hub, "HfFolder"):
+    class MockHfFolder:
+        @staticmethod
+        def get_token(): return None
+        @staticmethod
+        def save_token(token): pass
+        @staticmethod
+        def delete_token(): pass
+    huggingface_hub.HfFolder = MockHfFolder
+
 import gradio as gr
 import pandas as pd
 import numpy as np
@@ -14,14 +26,9 @@ from llm.prompt import QA_PROMPT
 from predictors.score_prediction import predictor
 from config import EMBEDDING_MODEL, FAISS_PATH, RETRIEVER_K
 
-# ==========================================
-# 1. 모델 설정 (Google Gemini API 사용)
-# ==========================================
-# Google AI Studio API Key 설정
-# 실제 배포 시에는 Spaces Settings -> Secrets에 GOOGLE_API_KEY를 등록하세요.
+# API 키 설정
 os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY", "AIzaSyDRlDaMDFTVM4I8uNZdmuYRLMG38U_Ks6g")
 
-# KCB 피처 한글 매핑 (UI용)
 FEATURES = {
     'C1Z001386': '1년내 카드 총 이용금액 (만원)',
     'C1M210000': '신용카드 건수',
@@ -42,10 +49,7 @@ FEATURE_ORDER = [
 
 class CreditRAGConsultant:
     def __init__(self):
-        # 1. 임베딩 로드
         self.embedding_model = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
-        
-        # 2. 벡터스토어 로드
         if os.path.exists(FAISS_PATH):
             self.vectorstore = FAISS.load_local(
                 FAISS_PATH, self.embedding_model, allow_dangerous_deserialization=True
@@ -55,7 +59,6 @@ class CreditRAGConsultant:
             self.vectorstore = None
             self.retriever = None
 
-        # 3. LLM: Gemini 1.5 Flash (매우 빠르고 한국어 성능 우수)
         self.llm = ChatGoogleGenerativeAI(
             model="gemini-1.5-flash",
             temperature=0.7,
@@ -70,38 +73,23 @@ class CreditRAGConsultant:
         if history and history[-1].get("content") == "생각 중...":
             history = history[:-1]
 
-        # 새 답변 자리 확보
         history = history + [{"role": "assistant", "content": ""}]
         t0 = time.time()
 
-        # R: 검색 단계
-        history[-1]["content"] = "📂 **[R] 분석 문서 검색 중...**"
-        yield history, ""
         docs = self.retriever.invoke(user_message) if self.retriever else []
         t_retrieve = time.time() - t0
-        context = "\n\n".join([f"[참고문서 {i+1}]\n{doc.page_content}" for i, doc in enumerate(docs)])
+        context = "\n\n".join([f"[문서 {i+1}]\n{doc.page_content}" for i, doc in enumerate(docs)])
 
-        # A: 프롬프트 구성 단계
-        history[-1]["content"] = f"📂 **[R] 완료** ({t_retrieve:.1f}s) | 🔗 **[A] 상담 문맥 구성 중...**"
-        yield history, ""
-        
         if analysis_report:
             score_val = int(analysis_report["score"])
             features_text = "\n".join([f"- {FEATURES.get(k, k)}: {v}" for k, v in analysis_report['features'].items()])
             query_text = f"■ 고객 분석 결과: {score_val}점\n{features_text}\n\n■ 질문: {user_message}"
         else:
-            query_text = f"■ 질문: {user_message} (현재 분석된 지표 데이터 없음)"
+            query_text = f"■ 질문: {user_message}"
 
         prompt_text = QA_PROMPT.format(context=context, query=query_text)
         t_augment = time.time() - t0
 
-        # G: 답변 생성 단계 (스트리밍 지원)
-        history[-1]["content"] = (
-            f"📂 **[R] 완료** ({t_retrieve:.1f}s) | 🔗 **[A] 완료** ({t_augment-t_retrieve:.2f}s)\n\n"
-            f"💬 **[G] 답변 생성 중...**"
-        )
-        yield history, ""
-        
         answer_buffer = ""
         try:
             for chunk in self.llm.stream(prompt_text):
@@ -112,23 +100,21 @@ class CreditRAGConsultant:
                 )
                 yield history, ""
         except Exception as e:
-            history[-1]["content"] = f"⚠️ Gemini API 에러: {str(e)}"
+            history[-1]["content"] = f"⚠️ 에러: {str(e)}"
             yield history, ""
 
 consultant = CreditRAGConsultant()
 
-with gr.Blocks(title="KCB AI Credit Consultant", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="KCB Credit AI", theme=gr.themes.Soft()) as demo:
     analysis_report = gr.State(None)
     pending_msg = gr.State("")
 
-    gr.Markdown("# 🛡️ KCB AI 신용 점수 상담 시스템 (Gemini Edition)")
+    gr.Markdown("# 🛡️ KCB AI 신용 점수 상담 서비스")
     
     with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown("### 📊 금융 지표 입력")
             input_list = []
-            ui_input_keys = [k for k in FEATURE_ORDER if k in FEATURES]
-            for key in ui_input_keys:
+            for key in [k for k in FEATURE_ORDER if k in FEATURES]:
                 if key == 'PERF1':
                     input_list.append(gr.Checkbox(label=FEATURES[key], value=False))
                 else:
@@ -136,11 +122,11 @@ with gr.Blocks(title="KCB AI Credit Consultant", theme=gr.themes.Soft()) as demo
             predict_btn = gr.Button("📈 점수 분석하기", variant="primary")
             
         with gr.Column(scale=2):
-            result_display = gr.Label(label="예측 신용 점수", num_top_classes=1)
-            chatbot = gr.Chatbot(label="실시간 상담 챗봇", height=550, type="messages", bubble_full_width=False)
+            result_display = gr.Label(label="예측 신용 점수")
+            chatbot = gr.Chatbot(label="실시간 상담", height=550, type="messages")
             
             with gr.Row():
-                msg = gr.Textbox(placeholder="신용 점수 관리 비법을 물어보세요...", scale=8, container=False)
+                msg = gr.Textbox(placeholder="질문을 입력하세요...", scale=8, container=False)
                 submit_btn = gr.Button("상담하기", variant="primary", scale=1)
 
     def handle_predict(*args):
@@ -149,7 +135,6 @@ with gr.Blocks(title="KCB AI Credit Consultant", theme=gr.themes.Soft()) as demo
         for i, key in enumerate(ui_input_keys):
             if key == 'PERF1': features_dict[key] = int(args[i])
             else: features_dict[key] = float(args[i]) if args[i] else 0.0
-        
         score = predictor.predict(features_dict)
         display_score = int(min(max(round(score), 0), 1000))
         return {"features": features_dict, "score": display_score}, display_score