feat<update>: 결과물 반환 불가

app.py

@@ -64,57 +64,69 @@ from huggingface_hub import upload_file
 from src.envs import RESULTS_REPO
 from src.grader import grade   # grader.py 안에 채점 + 리포팅 함수
 
-def grade_csv(file):
-    try:
-        # 제출 CSV 로드
-        submission_df = pd.read_csv(file.name)
-
-        # 채점 및 리포트 생성
-        score_df, report_dir = grade(submission_df, team_id="team")
 
-        # 점수 CSV 저장
-        output_file = "graded_results.csv"
-        score_df.to_csv(output_file, index=False)
+def grade_csv(file):
+    submission_df = pd.read_csv(file.name)
+
+    # 채점 및 리포트 생성
+    score_df, report_dir = grade(submission_df, team_id="team")
+
+    # 점수 CSV 저장
+    output_file = "graded_results.csv"
+    score_df.to_csv(output_file, index=False)
+
+    # Hugging Face Hub 업로드
+    upload_file(
+        path_or_fileobj=output_file,
+        path_in_repo=output_file,
+        repo_id=RESULTS_REPO,
+        repo_type="dataset",
+        token=os.environ.get("HF_TOKEN"),
+    )
 
-        # Hugging Face Hub 업로드 (선택적)
-        upload_file(
-            path_or_fileobj=output_file,
-            path_in_repo=output_file,
-            repo_id=RESULTS_REPO,
-            repo_type="dataset",
-            token=os.environ.get("HF_TOKEN"),
-        )
+    # 리포트 ZIP 생성
+    report_zip = "report.zip"
+    shutil.make_archive("report", 'zip', report_dir)
 
-        # 리포트 ZIP 압축
-        report_zip = "report.zip"
-        shutil.make_archive("report", 'zip', report_dir)
+    # 그래프 이미지 파일 목록 (report_dir 안에 *.png)
+    image_files = [os.path.join(report_dir, f) for f in os.listdir(report_dir) if f.endswith(".png")]
 
-        return score_df, output_file, report_zip
+    return score_df, report_zip, image_files
 
-    except Exception as e:
-        return pd.DataFrame([{"Error": str(e)}]), None, None
 
 
 with gr.Blocks() as demo:
     gr.Markdown("## Hackathon CSV 채점기 + 리포트 생성기")
 
-    with gr.Row():
-        csv_input = gr.File(label="CSV 업로드", file_types=[".csv"])
-
-    with gr.Row():
-        df_output = gr.Dataframe(label="평가 지표 결과")
-
-    with gr.Row():
-        file_output = gr.File(label="점수 CSV 다운로드")
-        report_output = gr.File(label="리포트 ZIP 다운로드")
-
-    # 업로드 → 채점 실행
-    csv_input.change(
-        fn=grade_csv,
-        inputs=csv_input,
-        outputs=[df_output, file_output, report_output]
-    )
+    with gr.Tabs():
+        with gr.Tab("평가 요청"):
+            with gr.Row():
+                with gr.Row():
+                    csv_input = gr.File(label="CSV 업로드", file_types=[".csv"])
+                    password = gr.Textbox(label="Jupyter Notebook 비밀번호")
+                    submit_button = gr.Button("평가 요청")
+                    df_output = gr.Dataframe(label="평가 지표 결과")
+
+                with gr.Row():
+                    report_output = gr.File(label="리포트 ZIP 다운로드")
+                    image_gallery = gr.Gallery(
+                        label="Plant별 비교 그래프",
+                        show_label=True,
+                        height="auto"  # 높이는 자동
+                    )
+
+            # 업로드 → 채점 실행
+            submit_button.click(
+                fn=grade_csv,
+                inputs=csv_input,
+                outputs=[df_output, report_output, image_gallery]
+            )
+
+        with gr.Tab("리더보드"):
+            with gr.Row():
+                gr.Textbox("ASDF")
 
 if __name__ == "__main__":
-    demo.launch()
+    # demo.launch()
+    demo.launch(debug=True, show_error=True)
 

graded_results.csv

@@ -1,2 +1,2 @@
-RMSE_AC,RMSE_AC_SCALED
-493.28674800238497,0.35831099772441877
+TEAM_ID,RMSE_AC,RMSE_AC_SCALED,NMAE_RANGE,NMAE_MEAN
+13,493.28674800238497,0.35831099772441877,0.0,0.0

report.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f693bdb169bee6213e9c82af4ba8f46f33bf5498c1eb63931c72156c0b04cf10
+oid sha256:108cf615dc3b4efc523a07f7568036674027ba1d31170af8c6455612e2e71d35
 size 175825

src/grader.py

@@ -18,6 +18,8 @@ def grade(submission_df: pd.DataFrame, team_id: str = "submission"):
         'AC_POWER': 'ANS_AC_POWER',
         'DAILY_YIELD': 'ANS_DAILY_YIELD'
     })
+    if 'SOURCE_KEY' in submission_df.columns:
+        submission_df = submission_df.rename(columns={"SOURCE_KEY": "INVERTER_ID"})
 
     # merge
     merged_df = pd.merge(
@@ -26,6 +28,7 @@ def grade(submission_df: pd.DataFrame, team_id: str = "submission"):
         how='left'
     ).sort_values(by=['DATE_TIME', 'PLANT_ID', 'INVERTER_ID'])
 
+
     # scaler
     scaler = MinMaxScaler()
     merged_df['AC_POWER_SCALED'] = scaler.fit_transform(merged_df[['AC_POWER']])
@@ -37,7 +40,9 @@ def grade(submission_df: pd.DataFrame, team_id: str = "submission"):
 
     metrics = {
         "RMSE_AC": rmse_ac,
-        "RMSE_AC_SCALED": rmse_ac_scaled
+        "RMSE_AC_SCALED": rmse_ac_scaled,
+        "NMAE_RANGE": 0.0,
+        "NMAE_MEAN": 0.0,
     }
 
     if 'DAILY_YIELD' in merged_df.columns: