增加加载本地评价功能

mock_data.py

@@ -1,108 +0,0 @@
-import random
-import math
-import pandas as pd
-import gradio as gr
-import numpy as np
-from typing import List
-from llm import OpenAI3
-
-
-llm = OpenAI3()
-
-
-def _scores_recalculate(scores: List):
-    scores_array = np.array(scores)
-    scores_dis = abs(scores_array - scores_array.mean())
-    scores_count = np.ceil(1 / (scores_dis + scores_dis.mean()) * len(scores)).astype(int)
-    new_scores = []
-    for i in range(len(scores)):
-        new_scores.extend([scores[i]] * scores_count[i])
-    return new_scores
-
-
-def generate_scores_and_comments(standard_file_path, rewrite_prompt, topic, num=10):
-    df = pd.read_excel(standard_file_path)
-    pd.set_option('display.max_colwidth', None)
-    standard_index = df.columns.to_list()
-    standard_index.pop(standard_index.index('得分'))
-    standard_scores = list(tuple(df['得分'].to_list()))
-    scores_choice = _scores_recalculate(standard_scores)
-    assessments = []
-    for i in range(num):
-        assessment = dict()
-        scores = dict()
-        stand_comment = []
-        for index in standard_index:
-            score = random.choice(scores_choice)
-            scores[index] = score
-            content = df[df['得分'] == score][index].to_string(index=False)
-            stand_comment.append(content)
-        mean_score = np.array(list(scores.values()))
-        # scores['mean_score'] = mean_score
-
-        assessment['scores'] = scores
-        stand_comment = '；'.join(stand_comment)
-        prompt = 'f"""' + rewrite_prompt + '"""'
-        r_comment = llm(eval(prompt))
-        r_comment = r_comment.split('\n')[-1]
-        assessment['comment'] = r_comment
-        assessments.append(assessment)
-    return assessments, df
-
-
-def medium_score_rewrite(standard_file, rewrite_prompt, topic, assessments):
-    scores = [i['scores'] for i in assessments]
-    scores = pd.DataFrame(scores)
-    try:
-        medium = scores.quantile(0.5)
-    except Exception as e:  # incase that the values in one column are all None
-        scores = scores.fillna(0)
-        medium = scores.quantile(0.5)
-    medium = medium.astype(int)
-
-    stand_comment = []
-    df = standard_file
-    for index in medium.index:
-        score = math.ceil(medium[index])
-        content = df[df['得分'] == score][index].to_string(index=False)
-        stand_comment.append(content)
-
-    s_comment = ';'.join(stand_comment)
-    prompt = 'f"""' + rewrite_prompt + '"""'
-    r_comment = llm(eval(prompt))
-    return r_comment
-
-
-def quantile_summary(summary_prompt, assessments):
-    if not isinstance(assessments[0], dict):
-        comment = '\n'.join(assessments)
-    else:
-        comments = [i['comment'] for i in assessments]
-        comments = pd.Series(comments)
-        scores = [i['scores'] for i in assessments]
-        scores = pd.DataFrame(scores)
-
-        try:
-            quartiles = scores.quantile([0.25, 0.75])
-        except Exception as e:  # incase that the values in one column are all None
-            scores = scores.fillna(0)
-            quartiles = scores.quantile([0.25, 0.75])
-        quartiles = quartiles.astype(int)
-        up = (scores - quartiles.loc[0.75]) < 0
-        down = (scores - quartiles.loc[0.25]) > 0
-        select_index = (up & down).all(axis=1)
-        if not select_index.any():
-            select_index = (up & down).any(axis=1)
-
-        if select_index.any():
-            select_comments = comments[select_index].to_list()
-        else:
-            select_comments = comments.to_list()
-
-        comment = '\n'.join(select_comments)
-
-    prompt = 'f"""' + summary_prompt + '"""'
-    s_comment = llm(eval(prompt))
-    return s_comment
-
-