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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 = 'ï¼›'.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 load_scores_and_comments(comments_file_path):
df = pd.read_excel(comments_file_path)
pd.set_option('display.max_colwidth', None)
comments = df['comments'].to_list()
df.pop('comments')
scores = df.to_dict(orient='records')
assessments = [{'scores': scores[i], 'comment': comments[i]} for i in range(len(comments))]
return assessments
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
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