Fix: Expand UI features to 14 to match prediction model and fix No API found error

app.py

@@ -1,4 +1,3 @@
-# [몽키 패치] Gradio와 huggingface_hub 버전 갈등 해결
 import huggingface_hub
 if not hasattr(huggingface_hub, "HfFolder"):
     class MockHfFolder:
@@ -21,68 +20,65 @@ from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
 
-# 프로젝트 설정 및 프롬프트 불러오기
+from config import EMBEDDING_MODEL, FAISS_PATH, RETRIEVER_K, GEMINI_API_KEY
 from llm.prompt import QA_PROMPT
 from predictors.score_prediction import predictor
-from config import EMBEDDING_MODEL, FAISS_PATH, RETRIEVER_K
 
 # API 키 설정
-os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY", "AIzaSyDRlDaMDFTVM4I8uNZdmuYRLMG38U_Ks6g")
+os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY", GEMINI_API_KEY)
 
-FEATURES = {
+# 14개 피처에 대한 상세 설명 매핑
+FEATURES_MAP = {
     'C1Z001386': '1년내 카드 총 이용금액 (만원)',
     'C1M210000': '신용카드 건수',
     'C18210000': '체크카드 건수',
     'C1L120001': '카드 총 한도금액 (만원)',
     'C1L120004': '카드 개설일수',
+    'L10210000': '은행업종 대출 건수',
+    'L90210100': '대부업종 대출 건수',
+    'L90210200': '저축은행 대출 건수',
+    'L10210B00': '주택담보 대출 건수',
+    'L10216000': '신용대출 총 잔액 (만원)',
+    'L10217000': '담보대출 총 잔액 (만원)',
     'D10110000': '연체 건수',
-    'D10113000': '연체 잔액 (만원)',
-    'E1L210000': '은행업종 대출 건수',
-    'E1L21B000': '담보 대출 건수',
+    'D10133000': '연체 잔액 (만원)',
     'PERF1': '90일 연체 여부'
 }
 
-FEATURE_ORDER = [
+# 모델이 요구하는 정확한 순서
+ALL_FEATURES_KEYS = [
     'C1Z001386', 'C1M210000', 'C18210000', 'C1L120001', 'C1L120004', 
-    'D10110000', 'D10113000', 'E1L210000', 'E1L21B000', 'PERF1'
+    'L10210000', 'L90210100', 'L90210200', 'L10210B00', 'L10216000', 
+    'L10217000', 'D10110000', 'D10133000', 'PERF1'
 ]
 
 class CreditRAGConsultant:
     def __init__(self):
         self.embedding_model = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
         if os.path.exists(FAISS_PATH):
-            self.vectorstore = FAISS.load_local(
-                FAISS_PATH, self.embedding_model, allow_dangerous_deserialization=True
-            )
+            self.vectorstore = FAISS.load_local(FAISS_PATH, self.embedding_model, allow_dangerous_deserialization=True)
             self.retriever = self.vectorstore.as_retriever(search_kwargs={"k": RETRIEVER_K})
         else:
-            self.vectorstore = None
-            self.retriever = None
+            self.vectorstore = self.retriever = None
 
-        self.llm = ChatGoogleGenerativeAI(
-            model="gemini-1.5-flash",
-            temperature=0.7,
-            max_output_tokens=1024
-        )
+        self.llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0.7)
 
     def generate_response(self, history, user_message, analysis_report):
-        if not user_message:
-            yield history, ""
-            return
-
-        if history and history[-1].get("content") == "생각 중...":
-            history = history[:-1]
-
+        if not user_message: yield history, ""; return
+        if history and history[-1].get("content") == "생각 중...": history = history[:-1]
+        
         history = history + [{"role": "assistant", "content": ""}]
         t0 = time.time()
-
+        
+        # R
         docs = self.retriever.invoke(user_message) if self.retriever else []
         t_retrieve = time.time() - t0
-        context = "\n\n".join([f"[문서 {i+1}]\n{doc.page_content}" for i, doc in enumerate(docs)])
-
+        context = "\n\n".join([doc.page_content for doc in docs])
+        
+        # A
         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()])
+            features_text = "\n".join([f"- {FEATURES_MAP.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}"
@@ -90,6 +86,7 @@ class CreditRAGConsultant:
         prompt_text = QA_PROMPT.format(context=context, query=query_text)
         t_augment = time.time() - t0
 
+        # G
         answer_buffer = ""
         try:
             for chunk in self.llm.stream(prompt_text):
@@ -109,16 +106,17 @@ 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 서비스")
+    gr.Markdown("# 🛡️ KCB AI 신용 점수 분석 시스템")
     
     with gr.Row():
         with gr.Column(scale=1):
+            gr.Markdown("### 📊 금융 지표 입력")
             input_list = []
-            for key in [k for k in FEATURE_ORDER if k in FEATURES]:
+            for key in ALL_FEATURES_KEYS:
                 if key == 'PERF1':
-                    input_list.append(gr.Checkbox(label=FEATURES[key], value=False))
+                    input_list.append(gr.Checkbox(label=FEATURES_MAP[key], value=False))
                 else:
-                    input_list.append(gr.Textbox(label=FEATURES[key], placeholder="0"))
+                    input_list.append(gr.Textbox(label=FEATURES_MAP[key], placeholder="0"))
             predict_btn = gr.Button("📈 점수 분석하기", variant="primary")
             
         with gr.Column(scale=2):
@@ -130,11 +128,15 @@ with gr.Blocks(title="KCB Credit AI", theme=gr.themes.Soft()) as demo:
                 submit_btn = gr.Button("상담하기", variant="primary", scale=1)
 
     def handle_predict(*args):
-        ui_input_keys = [k for k in FEATURE_ORDER if k in FEATURES]
         features_dict = {}
-        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
+        # 입력 리스트와 키 순서가 동일함
+        for i, key in enumerate(ALL_FEATURES_KEYS):
+            if key == 'PERF1':
+                features_dict[key] = int(args[i])
+            else:
+                val = args[i] if args[i] else "0"
+                features_dict[key] = float(val)
+        
         score = predictor.predict(features_dict)
         display_score = int(min(max(round(score), 0), 1000))
         return {"features": features_dict, "score": display_score}, display_score
@@ -144,8 +146,13 @@ with gr.Blocks(title="KCB Credit AI", theme=gr.themes.Soft()) as demo:
         return history + [{"role": "user", "content": user_message}, {"role": "assistant", "content": "생각 중..."}], "", user_message
 
     predict_btn.click(handle_predict, inputs=input_list, outputs=[analysis_report, result_display])
-    msg.submit(user_msg, [msg, chatbot], [chatbot, msg, pending_msg]).then(consultant.generate_response, [chatbot, pending_msg, analysis_report], [chatbot, msg])
-    submit_btn.click(user_msg, [msg, chatbot], [chatbot, msg, pending_msg]).then(consultant.generate_response, [chatbot, pending_msg, analysis_report], [chatbot, msg])
+    
+    msg.submit(user_msg, [msg, chatbot], [chatbot, msg, pending_msg]).then(
+        consultant.generate_response, [chatbot, pending_msg, analysis_report], [chatbot, msg]
+    )
+    submit_btn.click(user_msg, [msg, chatbot], [chatbot, msg, pending_msg]).then(
+        consultant.generate_response, [chatbot, pending_msg, analysis_report], [chatbot, msg]
+    )
 
 if __name__ == "__main__":
     demo.launch()