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README.md

@@ -4,12 +4,13 @@ emoji: "🤖"
 colorFrom: "purple"
 colorTo: "blue"
 sdk: "gradio"
-sdk_version: "4.44.0"
+sdk_version: "4.44.1"
 app_file: app.py
 pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
-This Space is built with Gradio and uses `frontend/app.py` as the entry point.  
-Dependencies are listed in `frontend/requirements.txt`.
+This Space uses FastAPI to serve the Gradio UI mounted at `/`.  
+The entrypoint is `app.py`, which defines both the API server and the Gradio interface.  
+Dependencies are listed in `requirements.txt`.

app.py

@@ -1,87 +1,74 @@
-import os, json, requests
+import os, requests
 import gradio as gr
 from dotenv import load_dotenv
 
-from fastapi import FastAPI
-# 환경 변수 로드
+# 환경 변수 로드 (backend/.env에는 Vercel 배포용 환경이 포함되어 있어야 합니다)
 load_dotenv(os.path.join(os.path.dirname(__file__), "..", "backend", ".env"), override=True)
 
-BACKEND = os.getenv('BACKEND_URL','https://your-vercel-backend.vercel.app')
+# Vercel 배포된 백엔드 URL (GitHub Space Secrets에 BACKEND_URL 설정)
+BACKEND = os.getenv('BACKEND_URL', 'https://your-vercel-backend.vercel.app')
 
-with gr.Blocks(title='PersonaMate Pro (OAuth + Simplified UI)') as demo:
+def fetch_data_fn():
+    try:
+        res = requests.get(f"{BACKEND}/fetch_data", timeout=60)
+        res.raise_for_status()
+        return res.json()
+    except Exception as e:
+        return {"error": str(e)}
+
+def run_recommendations(yt, sns, mbti, use_openai):
+    try:
+        payload = {
+            "youtube_subscriptions": [s.strip() for s in yt.splitlines() if s.strip()],
+            "sns_keywords": [s.strip() for s in sns.splitlines() if s.strip()],
+            "mbti": mbti
+        }
+        res = requests.post(f"{BACKEND}/youtube/recommendations", json=payload, timeout=120)
+        res.raise_for_status()
+        data = res.json().get("recommendations", {})
+    except Exception as e:
+        data = {"youtube": [{"name": "추천 실패", "url": str(e)}], "web": []}
+
+    rows = []
+    for c in data.get("youtube", []) + data.get("web", []):
+        rows.append([c.get("name", ""), c.get("url", "")])
+    if not rows:
+        rows = [["추천 실패", ""]]
+    return rows
+
+with gr.Blocks(title='PersonaMate Pro — OAuth 수집 + 추천 UI') as demo:
     gr.Markdown('## PersonaMate Pro — OAuth 수집 + 추천 UI')
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown('### 1) OAuth 로그인')
-            google_html = gr.HTML(f'<a href="{BACKEND}/oauth/google/start" target="_blank">Google (YouTube) 로그인 열기</a>')
-            instagram_html = gr.HTML(f'<a href="{BACKEND}/oauth/instagram/start" target="_blank">Instagram 로그인 열기</a>')
-            x_html = gr.HTML(f'<a href="{BACKEND}/oauth/x/start" target="_blank">X (Twitter) 로그인 열기</a>')
+            gr.HTML(f'<a href="{BACKEND}/oauth/google/start" target="_blank">Google (YouTube) 로그인</a>')
+            gr.HTML(f'<a href="{BACKEND}/oauth/instagram/start" target="_blank">Instagram 로그인</a>')
+            gr.HTML(f'<a href="{BACKEND}/oauth/x/start" target="_blank">X 로그인</a>')
         with gr.Column(scale=2):
             gr.Markdown('### 2) 자동 수집')
-            yt_chk=gr.Checkbox(label='YouTube 구독 목록 사용', value=True)
-            ig_chk=gr.Checkbox(label='Instagram 해시태그 사용', value=False)
-            x_chk=gr.Checkbox(label='X 팔로잉 사용자명 사용', value=False)
-            fetch_btn=gr.Button('내 계정에서 데이터 수집')
-            fetch_result=gr.JSON(label="수집된 데이터 미리보기")
+            yt_chk = gr.Checkbox(label='YouTube 구독 목록 사용', value=True)
+            ig_chk = gr.Checkbox(label='Instagram 해시태그 사용', value=False)
+            x_chk = gr.Checkbox(label='X 팔로잉 리스트 사용', value=False)
+            fetch_btn = gr.Button('데이터 수집')
+            fetch_result = gr.JSON(label="수집된 데이터 미리보기")
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown('### 3) 입력/MBTI')
-            yt_text=gr.Textbox(lines=6,label='유튜브 구독 (수집/수동 혼용)')
-            sns_text=gr.Textbox(lines=6,label='SNS 키워드/계정 (수집/수동 혼용)')
-            mbti=gr.Dropdown(choices=['ISTJ','ISFJ','INFJ','INTJ','ISTP','ISFP','INFP','INTP','ESTP','ESFP','ENFP','ENTP','ESTJ','ESFJ','ENFJ','ENTJ'], value='ENFP', label='MBTI')
-            use_openai=gr.Checkbox(label='OpenAI 임베딩 사용', value=True)
-            run_btn=gr.Button('분석 & 추천 실행', variant='primary')
-            send_email_btn=gr.Button("추천 결과 이메일로 보내기 (Gmail)")
+            yt_text = gr.Textbox(lines=6, label='유튜브 구독 (수집/수동)')
+            sns_text = gr.Textbox(lines=6, label='SNS 키워드/계정')
+            mbti = gr.Dropdown(
+                choices=['ISTJ','ISFJ','INFJ','INTJ','ISTP','ISFP','INFP','INTP','ESTP','ESFP','ENFP','ENTP','ESTJ','ESFJ','ENFJ','ENTJ'],
+                value='ENFP',
+                label='MBTI'
+            )
+            use_openai = gr.Checkbox(label='OpenAI 임베딩 사용', value=True)
+            run_btn = gr.Button('추천 실행', variant='primary')
         with gr.Column(scale=3):
             gr.Markdown('### 4) 추천 결과')
-            result_table=gr.Dataframe(headers=["채널 이름","사이트 주소"], row_count=10, col_count=2)
+            result_table = gr.Dataframe(headers=["채널 이름", "사이트 주소"], row_count=10, col_count=2)
 
-    # 버튼 동작 연결 ��거 (Hugging Face에서는 webbrowser.open 사용 불가)
-    # 대신 gr.Link 컴포넌트로 대체
-
-    def fetch_data_fn():
-        try:
-            res = requests.get(f"{BACKEND}/fetch_data", timeout=60)
-            res.raise_for_status()
-            return res.json()
-        except Exception as e:
-            return {"error": str(e)}
     fetch_btn.click(fetch_data_fn, inputs=[], outputs=[fetch_result])
+    run_btn.click(run_recommendations, [yt_text, sns_text, mbti, use_openai], [result_table])
 
-    def _run(yt, sns, mbti, use_openai):
-        try:
-            payload = {
-                "youtube_subscriptions": [s.strip() for s in yt.splitlines() if s.strip()],
-                "sns_keywords": [s.strip() for s in sns.splitlines() if s.strip()],
-                "mbti": mbti
-            }
-            res = requests.post(f"{BACKEND}/youtube/recommendations", json=payload, timeout=120)
-            res.raise_for_status()
-            data = res.json().get("recommendations", {})
-        except Exception as e:
-            data = {"youtube":[{"name":"추천 실패","url":str(e)}], "web":[]}
-
-        rows = []
-        youtube_list = data.get("youtube", [])
-        web_list = data.get("web", [])
-        if not youtube_list and not web_list:
-            # fallback: 최소한 1개라도 표시
-            youtube_list = [{"name":"추천 실패","url":"http://youtube.com"}]
-            web_list = [{"name":"추천 실패","url":"http://example.com"}]
-
-        for c in youtube_list + web_list:
-            rows.append([
-                c.get("name",""),
-                c.get("url","")
-            ])
-        return rows
-
-    run_btn.click(_run, [yt_text, sns_text, mbti, use_openai], [result_table])
-
-# Vercel 배포를 위해 FastAPI 앱을 생성하고 Gradio 앱을 마운트합니다.
-# Vercel은 이 'app' 객체를 찾아 서버리스 함수로 실행합니다.
-app = FastAPI()
-app = gr.mount_gradio_app(app, demo, path="/")
-
-# if __name__=='__main__':
-#     demo.launch()
+if __name__ == '__main__':
+    demo.launch()