app.py

app.py

@@ -6,7 +6,6 @@ import warnings
 import re
 import json
 import os
-import zipfile
 import numpy as np
 import pandas as pd
 from tqdm import tqdm
@@ -19,15 +18,6 @@ import itertools
 import sys
 import time
 import logging
-import subprocess
-
-result = subprocess.run(['git', 'lfs', 'pull'], capture_output=True, text=True)
-
-# 명령어 실행 결과 출력 (선택 사항)
-if result.returncode == 0:
-    print("LFS 파일이 성공적으로 다운로드되었습니다.")
-else:
-    print(f"오류 발생: {result.stderr}")
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger()
@@ -39,7 +29,7 @@ MAX_PATCHES = 512
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Paths to the models
-ko_deplot_model_path = './deplot_model_ver_kor_24.7.25_refinetuning_epoch1.bin'
+ko_deplot_model_path = './deplot_model_ver_kor_24.7.25_refinetuning_epoch3.bin'
 aihub_deplot_model_path='./deplot_k.pt'
 t5_model_path = './ke_t5.pt'
 
@@ -649,7 +639,7 @@ class Highlighter:
 
 # 1. 데이터 로드
 aihub_deplot_result_df = pd.read_csv('./aihub_deplot_result.csv')
-ko_deplot_result= './ko-deplot-base-pred-epoch3-refinetuning.json'
+ko_deplot_result= './ko-deplot-base-pred-epoch1-refinetuning.json'
 unichart_result='./unichart_results.json'
 
 # 2. 체크해야 하는 이미지 파일 로드
@@ -662,12 +652,11 @@ def load_image_checklist(file):
 # 3. 현재 인덱스를 추적하기 위한 변수
 current_index = 0
 image_names = []
-
 def show_image(current_idx):
     image_name=image_names[current_idx]
-    image_path = f"./top_20_percent_images/{image_name}.jpg"
+    image_path = f"./images/{image_name}.jpg"
     if not os.path.exists(image_path):
-        image_path = f"./bottom_20_percent_images/{image_name}.jpg"
+        raise FileNotFoundError(f"Image file not found: {image_path}")
     return Image.open(image_path)
 
 # 4. 버튼 클릭 이벤트 핸들러
@@ -938,7 +927,7 @@ def file_selector(selector):
         return gr.File("./new_bottom_20_percent_images.txt")
     elif selector == "high score 차트":
         return gr.File("./new_top_20_percent_images.txt")
-
+'''
 def update_results(model_type):
     if "ko_deplot" == model_type:
         return gr.update(visible=True),gr.update(visible=True),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=True),gr.update(visible=False),gr.update(visible=False)
@@ -948,6 +937,34 @@ def update_results(model_type):
         return gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=True),gr.update(visible=True),gr.update(visible=False),gr.update(visible=False),gr.update(visible=True)
     else:
         return gr.update(visible=True), gr.update(visible=True),gr.update(visible=True),gr.update(visible=True),gr.update(visible=True),gr.update(visible=True),gr.update(visible=True),gr.update(visible=True),gr.update(visible=True)
+'''
+
+def update_results(selected_models):
+    # Create a visibility list initialized to False for all components
+    visibility = [False] * 9
+
+    # Update visibility based on the selected models
+    if "ko_deplot" in selected_models:
+        visibility[0] = True  # ko_deplot_generated_table
+        visibility[1] = True  # ko_deplot_score_table
+        visibility[6] = True  # ko_deplot_label_table
+
+    if "aihub_deplot" in selected_models:
+        visibility[2] = True  # aihub_deplot_generated_table
+        visibility[3] = True  # aihub_deplot_score_table
+        visibility[7] = True  # aihub_deplot_label_table
+
+    if "unichart" in selected_models:
+        visibility[4] = True  # unichart_generated_table
+        visibility[5] = True  # unichart_score_table
+        visibility[8] = True  # unichart_label_table
+
+    if "all" in selected_models:
+        visibility=[True]*9
+        
+    # Return gr.update for each component with the corresponding visibility status
+    return tuple(gr.update(visible=v) for v in visibility)
+
 
 def display_image(image_file):
     image=Image.open(image_file)
@@ -1000,7 +1017,11 @@ def handle_chart_type_change(chart_type,all_file_path):
     image=show_image(current_index)
     return image,image_names[current_index]
 
-with gr.Blocks() as iface:
+css = """
+#warning span {font-size: 24px !important}
+"""
+
+with gr.Blocks(css=css) as iface:
     mode=gr.State("image_upload")
     with gr.Row():
         with gr.Column():
@@ -1016,7 +1037,7 @@ with gr.Blocks() as iface:
             file_uploader= gr.File(file_count="single", file_types=[".txt"], visible=False)
             file_upload_option=gr.Radio(choices=["low score 차트","high score 차트"],label="파일 업로드 옵션",visible=False)
             chart_type = gr.Dropdown(["일반 가로 막대형","누적 가로 막대형","100% 기준 누적 가로 막대형", "일반 세로 막대형","누적 세로 막대형","100% 기준 누적 세로 막대형","선형", "원형", "기타 방사형", "기타 혼합형", "전체"], label="Chart Type", value="all")
-            model_type=gr.Dropdown(["ko_deplot","aihub_deplot","unichart","all"],label="model")
+            model_type=gr.Dropdown(["ko_deplot","aihub_deplot","unichart","all"],label="model",multiselect=True)
             image_displayer=gr.Image(visible=True)
             with gr.Row():
                 pre_button=gr.Button("이전",interactive="False")
@@ -1026,17 +1047,17 @@ with gr.Blocks() as iface:
             #file_button.click(interface_selector, inputs=gr.State("파일 업로드"), outputs=[image_uploader, file_uploader,mode,mode_label,image_name])
             inference_button=gr.Button("추론")
         with gr.Column():
-            ko_deplot_generated_table=gr.DataFrame(visible=False,label="ko-deplot 추론 결과")
-            aihub_deplot_generated_table=gr.DataFrame(visible=False,label="aihub-deplot 추론 결과")
-            unichart_generated_table=gr.DataFrame(visible=False,label="unichart 추론 결과")
+            ko_deplot_generated_table=gr.DataFrame(visible=False,label="ko-deplot 추론 결과",elem_id="warning")
+            aihub_deplot_generated_table=gr.DataFrame(visible=False,label="aihub-deplot 추론 결과",elem_id="warning")
+            unichart_generated_table=gr.DataFrame(visible=False,label="unichart 추론 결과",elem_id="warning")
         with gr.Column():
-            ko_deplot_label_table=gr.DataFrame(visible=False,label="ko-deplot 정답테이블")
-            aihub_deplot_label_table=gr.DataFrame(visible=False,label="aihub-deplot 정답테이블")
-            unichart_label_table=gr.DataFrame(visible=False,label="unichart 정답테이블")
+            ko_deplot_label_table=gr.DataFrame(visible=False,label="ko-deplot 정답테이블",elem_id="warning")
+            aihub_deplot_label_table=gr.DataFrame(visible=False,label="aihub-deplot 정답테이블",elem_id="warning")
+            unichart_label_table=gr.DataFrame(visible=False,label="unichart 정답테이블",elem_id="warning")
         with gr.Column():
-            ko_deplot_score_table=gr.DataFrame(visible=False,label="ko_deplot 점수")
-            aihub_deplot_score_table=gr.DataFrame(visible=False,label="aihub_deplot 점수")
-            unichart_score_table=gr.DataFrame(visible=False,label="unichart 점수")
+            ko_deplot_score_table=gr.DataFrame(visible=False,label="ko_deplot 점수",elem_id="warning")
+            aihub_deplot_score_table=gr.DataFrame(visible=False,label="aihub_deplot 점수",elem_id="warning")
+            unichart_score_table=gr.DataFrame(visible=False,label="unichart 점수",elem_id="warning")
     model_type.change(
                         update_results,
                         inputs=[model_type],
@@ -1066,6 +1087,7 @@ if __name__ == "__main__":
     print("Launching Gradio interface...")
     sys.stdout.flush()  # stdout 버퍼를 비웁니다.
     iface.launch(share=True)
+    #iface.launch(share=False,server_name="115.145.230.14",server_port=8080)
     time.sleep(2)  # Gradio URL이 출력될 때까지 잠시 기다립니다.
     sys.stdout.flush()  # 다시 stdout 버퍼를 비웁니다.
         # Gradio가 제공하는 URLs을 파일에 기록합니다.