score 추가

app.py

@@ -7,10 +7,10 @@ with gr.Blocks() as demo:
     with gr.Row():
         gr.Markdown("# Distributed Evaluation Parallel 😎")
     with gr.Row():
-        upload = gr.UploadButton(label="Upload a file")
+        file = gr.File(label="Upload a file")
         prev = gr.Button(value="Previous")
         next = gr.Button(value="Next")
-        download = gr.Button(value="Download")
+        download = gr.File(label="Download a file")
     with gr.Row():
         with gr.Column():
             question = gr.Textbox(label="Question")
@@ -18,7 +18,7 @@ with gr.Blocks() as demo:
             ground_truth = gr.Textbox(label="GT")
         with gr.Column():
             prediction = gr.Textbox(label="Prediction")
-            score = gr.Radio(["Incorrect", "Correct"], label="Score")
+            score = gr.Radio(choices=["Incorrect", "Correct"], label="Score")
     with gr.Row():
         todos = gr.DataFrame()
         done = gr.DataFrame()
@@ -26,25 +26,38 @@ with gr.Blocks() as demo:
 
     def csv2df(file):
         df = pd.read_csv(file.name)
-        dataset_df.update(dict(df=df))
+        df['score'] = None
+        df_dict = df.to_dict('records')
+        dataset_df.update(dict(df=df, df_dict=df_dict))
         return update()
 
-    def prev_func():
+    def prev_func(score):
+        df_dict = dataset_df['df_dict']
         state.value = max(state.value - 1, 0)
-        return update()
+        score = df_dict[state.value]['score']
+        gr.Info(f"총 {len(dataset_df['df'])}개 중에 {state.value + 1}번째 데이터입니다.")
+        return [*update(), score]
     
-    def next_func():
+    def next_func(score):
+        df_dict = dataset_df['df_dict']
+        df_dict[state.value]['score'] = score
         state.value = min(state.value + 1, len(dataset_df['df']) - 1)
-        return update()
+        score = df_dict[state.value]['score']
+        gr.Info(f"총 {len(dataset_df['df'])}개 중에 {state.value + 1}번째 데이터입니다.")
+        return [*update(), score]
 
     def update():
-        q = dataset_df['df'].question.to_list()[state.value]
-        g = dataset_df['df'].answer.to_list()[state.value]
-        p = dataset_df['df'].prediction.to_list()[state.value]
-        return q, g, p, dataset_df['df'], dataset_df['df']
-    upload.upload(csv2df, upload, [question, ground_truth, prediction, todos, done])
-    prev.click(prev_func, None, [question, ground_truth, prediction, todos, done])
-    next.click(next_func, None, [question, ground_truth, prediction, todos, done])
+        df_dict = dataset_df['df_dict']
+        q = df_dict[state.value]['question']
+        g = df_dict[state.value]['answer']
+        p = df_dict[state.value]['prediction']
+        df = pd.DataFrame(df_dict)
+        todos = df[df.score.isna()]
+        done = df[df.score.isna() == False]
+        return q, g, p, todos, done, "data_backup.csv"
+    file.upload(csv2df, file, [question, ground_truth, prediction, todos, done, download])
+    prev.click(prev_func, [score], [question, ground_truth, prediction, todos, done, download, score])
+    next.click(next_func, [score], [question, ground_truth, prediction, todos, done, download, score])
 
 demo.queue()
 demo.launch()