Update ui.py

ui.py

@@ -4,39 +4,38 @@ from config import STATIONS
 from supabase_utils import get_supabase_client
 
 def create_ui(prediction_handler, chatbot_handler):
-    """Gradio UI를 생성하고 반환합니다."""
-    with gr.Blocks(title="통합 조위 예측 시스템", theme=gr.themes.Soft()) as demo:
-        gr.Markdown("# 🌊 통합 조위 예측 시스템 with Gemini")
+    """Gradio UI for Tide Prediction System."""
+    client = get_supabase_client()
+    supabase_status = "🟢 Connected" if client else "🔴 Disconnected (Check env variables)"
+    gemini_status = "🟢 Connected" if os.getenv("GEMINI_API_KEY") else "🔴 Disconnected (Check env variables)"
 
-        # 연결 상태 표시
-        client = get_supabase_client()
-        supabase_status = "🟢 연결됨" if client else "🔴 연결 안됨 (환경변수 확인 필요)"
-        gemini_status = "🟢 연결됨" if os.getenv("GEMINI_API_KEY") else "🔴 연결 안됨 (환경변수 확인 필요)"
-        gr.Markdown(f"**Supabase 상태**: {supabase_status} | **Gemini 상태**: {gemini_status}")
+    with gr.Blocks(title="Tide Prediction System", theme=gr.themes.Default()) as demo:
+        gr.Markdown("# 🌊 Tide Prediction System")
+        gr.Markdown(f"**Supabase**: {supabase_status} | **Gemini**: {gemini_status}")
 
         with gr.Tabs():
-            with gr.TabItem("통합 조위 예측"):
+            with gr.TabItem("Tide Prediction"):
                 with gr.Row():
                     with gr.Column(scale=1):
-                        station_id_input = gr.Dropdown(STATIONS, label="관측소 선택", value="DT_0001")
-                        input_csv = gr.File(label="과거 데이터 업로드 (.csv)")
-                        predict_btn = gr.Button("예측 실행", variant="primary")
+                        station = gr.Dropdown(choices=STATIONS, label="Station", value="DT_0001")
+                        csv_file = gr.File(label="Historical Data (.csv)")
+                        predict_btn = gr.Button("Run Prediction", variant="primary")
                     with gr.Column(scale=3):
-                        output_plot = gr.Plot(label="예측 결과 시각화")
-                        output_df = gr.DataFrame(label="예측 결과 데이터")
-                output_log = gr.Textbox(label="실행 로그", lines=5, interactive=False)
+                        plot_output = gr.Plot(label="Prediction Visualization")
+                        df_output = gr.DataFrame(label="Prediction Data")
+                log_output = gr.Textbox(label="Execution Log", interactive=False)
 
-            with gr.TabItem("AI 조위 챗봇"):
+            with gr.TabItem("AI Tide Chatbot"):
                 gr.ChatInterface(
                     fn=chatbot_handler,
-                    title="AI 조위 챗봇",
-                    description="조위에 대해 궁금한 점을 물어보세요. (예: '인천 오늘 현재 조위 알려줘')",
-                    #examples=[]
+                    title="AI Tide Chatbot",
+                    description="Ask about tide information (e.g., 'What's the current tide in Incheon?')"
                 )
 
         predict_btn.click(
             fn=prediction_handler,
-            inputs=[station_id_input, input_csv],
-            outputs=[output_plot, output_df, output_log]
+            inputs=[station, csv_file],
+            outputs=[plot_output, df_output, log_output]
         )
-    return demo
+
+    return demo