增加了提交分数的功能

app.py

@@ -16,7 +16,7 @@ from datasets import Features, Image, Audio, Sequence
 from typing import List, Tuple, Callable
 
 from utils import ndarray_to_base64, clean_up_df, create_statistic, create_plot
-from comm_utils import save_to_file, send_msg_to_server
+from comm_utils import save_to_file, send_msg_to_server, save_score
 
 class AppError(RuntimeError):
     pass
@@ -250,6 +250,7 @@ with gr.Blocks() as demo:
     cp_error = gr.Markdown("", visible=False)
     cp_info = gr.Markdown("", visible=False)
     cp_result = gr.Markdown("", visible=False)
+    qusetion_id_box = gr.Textbox(visible=False)
     tot_samples = 0
     # 初始化Salesforce/lotsa_data数据集展示使用的组件
     # componets = []
@@ -269,12 +270,16 @@ with gr.Blocks() as demo:
     with gr.Row():
         user_input_box = gr.Textbox(label="question", interactive=False)
         user_output_box = gr.Textbox(label="answer", interactive=False)
-    # user_io_buttom = gr.Button("发送", interactive=True)
     # componets.append({"select_sample_box": select_sample_box,
     #                   "statistics_textbox": statistics_textbox,
     #                   "user_input_box": user_input_box,
     #                   "plot": plot})
-
+    score_slider = gr.Slider(1, 5, 1, label="Score for answer", interactive=True)
+    with gr.Row():
+        with gr.Column(scale=2):
+            user_submit_button = gr.Button("submit", interactive=True)
+        with gr.Column(scale=1):
+            submit_info_box = gr.Textbox(label="submit_info", interactive=False)
     with gr.Row():
         cp_page = gr.Textbox("1", label="Page", placeholder="1", visible=False)
         cp_goto_page = gr.Button("Go to page", visible=False)
@@ -299,6 +304,7 @@ with gr.Blocks() as demo:
                 ret[plot] = gr.update(value=create_plot(df_list, id_list))
             elif dataset == 'YY26/TS_DATASETS':
                 df, max_page, info = get_page(dataset, config, split, page)
+                ret[qusetion_id_box] = gr.update(value = df['num'][0])
                 # TODO: 修改lotsa_config的读取逻辑
                 lotsa_config, lotsa_split, lotsa_page = 'traffic_hourly', 'train', eval(df['ts_id'][0])
                 start_index, end_index = df['start_index'][0], df['end_index'][0]
@@ -308,6 +314,7 @@ with gr.Blocks() as demo:
                 ret[plot] = gr.update(value=create_plot(df_list, id_list, interval=[start_index, end_index]))
                 ret[user_input_box] = gr.update(value=df['question'][0])
                 ret[user_output_box] = gr.update(value=df['answer'][0])
+                ret[submit_info_box] = gr.update(value="")
             else:
                 markdown_result, max_page, info = get_page(dataset, config, split, page)
                 ret[cp_result] = gr.update(visible=True, value=markdown_result)
@@ -376,13 +383,15 @@ with gr.Blocks() as demo:
                 #    select_sample_box, select_subtarget_box,
                 #    select_buttom,
                    statistics_textbox, plot,
-                   user_input_box, user_output_box]
+                   qusetion_id_box,
+                   user_input_box, user_output_box,
+                   submit_info_box]
     cp_go.click(show_dataset, inputs=[cp_dataset], outputs=all_outputs)
     cp_config.change(show_dataset_at_config, inputs=[cp_dataset, cp_config], outputs=all_outputs)
     cp_split.change(show_dataset_at_config_and_split, inputs=[cp_dataset, cp_config, cp_split], outputs=all_outputs)
     cp_goto_page.click(show_dataset_at_config_and_split_and_page, inputs=[cp_dataset, cp_config, cp_split, cp_page], outputs=all_outputs)
     cp_goto_next_page.click(show_dataset_at_config_and_split_and_next_page, inputs=[cp_dataset, cp_config, cp_split, cp_page], outputs=all_outputs)
-    # user_io_buttom.click(send_msg_to_server, inputs=[user_input_box], outputs=[user_output_box])
+    user_submit_button.click(save_score, inputs=["none", qusetion_id_box, score_slider], outputs=[submit_info_box])
     # select_buttom.click(show_dataset_at_config_and_split_and_page, inputs=[cp_dataset, cp_config, cp_split, select_sample_box, select_subtarget_box], outputs=all_outputs)
 
 

comm_utils.py

@@ -1,5 +1,7 @@
+import json
+import os
 import requests
-
+import time
 
 API_URL = "http://127.0.0.1:5000/api/process"
 
@@ -7,6 +9,25 @@ def save_to_file(user_input):
     with open("user_input.txt", "w") as file:
         file.write(user_input)
 
+def save_score(user_id, question_id, score):
+    score_data = {
+        "user_id": user_id,
+        "question_id": question_id,
+        "score": score
+    }
+
+    if os.path.exists("score.json"):
+        with open("score.json", "r") as file:
+            data = json.load(file)
+    else:
+        data = []
+
+    data.append(score_data)
+
+    with open("score.json", "w") as file:
+        json.dump(data, file, indent=4)
+
+    return f'Time {time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())} UCT-8, score submitted successfully.'
 
 def send_msg_to_server(input_text):
     try:

score.json

@@ -0,0 +1,27 @@
+[
+    {
+        "user_id": 4.12,
+        "question_id": "1",
+        "score": 4.12
+    },
+    {
+        "user_id": 4.12,
+        "question_id": "1",
+        "score": 4.12
+    },
+    {
+        "user_id": 4.12,
+        "question_id": "1",
+        "score": 4.12
+    },
+    {
+        "user_id": 2.86,
+        "question_id": "1",
+        "score": 2.86
+    },
+    {
+        "user_id": 2.86,
+        "question_id": "2",
+        "score": 2.86
+    }
+]

utils.py

@@ -96,10 +96,10 @@ def create_statistic(dfs: list[pd.DataFrame], ids: list[str], interval:list[int,
             df = df.iloc[interval[0]:interval[1]]
         df_values = df.iloc[:, 1:]
         # 计算统计值
-        mean_values = df_values.mean().round(2)
-        std_values = df_values.std().round(2)
-        max_values = df_values.max().round(2)
-        min_values = df_values.min().round(2)
+        mean_values = df_values.mean()
+        std_values = df_values.std()
+        max_values = df_values.max()
+        min_values = df_values.min()
 
         # 将这些统计信息合并成一个新的DataFrame
         stats_df = pd.DataFrame({
@@ -114,6 +114,7 @@ def create_statistic(dfs: list[pd.DataFrame], ids: list[str], interval:list[int,
 
     # 合并所有统计信息DataFrame
     combined_stats_df = pd.concat(stats_list, ignore_index=True)
+    combined_stats_df = combined_stats_df.applymap(lambda x: round(x, 2) if isinstance(x, (int, float)) else x)
     return combined_stats_df
 
 def clean_up_df(df: pd.DataFrame, rows_to_include: list[int]) -> pd.DataFrame: