Spaces:
Sleeping
Sleeping
File size: 8,361 Bytes
478965d 0670cf4 478965d 0670cf4 478965d 0670cf4 478965d 0670cf4 478965d 0670cf4 478965d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
from prompt import Prompt
from openai import OpenAI
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import gradio as gr
import pandas as pd
import os
class Backend:
def __init__(self):
self.agent = OpenAI()
self.prompt = Prompt()
def read_file(self, file):
# read the file
if file is not None:
with open(file.name, 'r') as f:
text = f.read()
else:
raise gr.Error("You need to upload a file first")
return text
def highlight_text(self, text, highlight_list):
# Find the original sentences
# Split the passage into sentences
sentences_in_passage = text.split('.')
sentences_in_passage = [i.split('\n') for i in sentences_in_passage]
new_sentences_in_passage = []
for i in sentences_in_passage:
new_sentences_in_passage =new_sentences_in_passage + i
# hightlight the reference
for hl in highlight_list:
# Find the best match using fuzzy matching
best_match = process.extractOne(hl, new_sentences_in_passage, scorer=fuzz.partial_ratio)
text = text.replace(best_match[0], f'<mark style="background: #A5D2F1">{best_match[0]}</mark><mark style="background: #FFC0CB"><font color="red"> (match score:{best_match[1]})</font></mark>')
# add line break
text = text.replace('\n', f" <br /> ")
# add scroll bar
text = f'<div style="height: 500px; overflow: auto;">{text}</div>'
return text
def process_file(self, file, questions, openai_key):
# record the questions
self.questions = questions
# get the text
self.text = self.read_file(file)
# make the prompt
prompt = self.prompt.get(self.text, questions, 'v3')
# interact with openai
res = self.agent(prompt, with_history = False, temperature = 0.1, model = 'gpt-3.5-turbo-16k', api_key = openai_key)
res = self.prompt.process_result(res, 'v3')
# for multiple questions
self.gpt_result = res
self.curret_question = 0
self.totel_question = len(res.keys())
# make a dataframe to record everything
self.ori_answer_df = pd.DataFrame(res).T
self.answer_df = pd.DataFrame(res).T
# default fist question
res = res['Question 1']
question = self.questions[self.curret_question]
self.answer = res['answer']
self.highlighted_out = res['original sentences']
highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
self.highlighted_out = '\n'.join(self.highlighted_out)
return question, self.answer, highlighted_out_html, self.answer, self.highlighted_out
def process_results(self, answer_correct, correct_answer, reference_correct, correct_reference):
if not hasattr(self, 'clicked_correct_answer'):
raise gr.Error("You need to judge whether the generated answer is correct first")
if not hasattr(self, 'clicked_correct_reference'):
raise gr.Error("You need to judge whether the highlighted reference is correct first")
if not hasattr(self, 'answer_df'):
raise gr.Error("You need to submit the document first")
if self.curret_question >= self.totel_question or self.curret_question < 0:
raise gr.Error("No more questions, please return back")
# record the answer
self.answer_df.loc[f'Question {self.curret_question + 1}', 'answer_correct'] = answer_correct
self.answer_df.loc[f'Question {self.curret_question + 1}', 'reference_correct'] = reference_correct
if self.clicked_correct_answer == True:
if hasattr(self, 'answer'):
self.answer_df.loc[f'Question {self.curret_question + 1}', 'correct_answer'] = self.answer
else:
raise gr.Error("You need to submit the document first")
else:
self.answer_df.loc[f'Question {self.curret_question + 1}', 'correct_answer'] = correct_answer
if self.clicked_correct_reference == True:
if hasattr(self, 'highlighted_out'):
self.answer_df.loc[f'Question {self.curret_question + 1}', 'correct_reference'] = self.highlighted_out
else:
raise gr.Error("You need to submit the document first")
else:
self.answer_df.loc[f'Question {self.curret_question + 1}', 'correct_reference'] = correct_reference
gr.Info('Results saved!')
return "Results saved!"
def process_next(self):
self.curret_question += 1
if hasattr(self, 'clicked_correct_answer'):
del self.clicked_correct_answer
if hasattr(self, 'clicked_correct_reference'):
del self.clicked_correct_reference
if self.curret_question >= self.totel_question:
# self.curret_question -= 1
return "No more questions!", "No more questions!", "No more questions!", 'No more questions!', 'No more questions!', 'Still need to click the button above to save the results', None, None
else:
res = self.gpt_result[f'Question {self.curret_question + 1}']
question = self.questions[self.curret_question]
self.answer = res['answer']
self.highlighted_out = res['original sentences']
highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
self.highlighted_out = '\n'.join(self.highlighted_out)
return question, self.answer, highlighted_out_html, 'Please judge on the generated answer', 'Please judge on the generated answer', 'Still need to click the button above to save the results', None, None
def process_last(self):
self.curret_question -= 1
if hasattr(self, 'clicked_correct_answer'):
del self.clicked_correct_answer
if hasattr(self, 'clicked_correct_reference'):
del self.clicked_correct_reference
if self.curret_question < 0:
# self.curret_question += 1
return "No more questions!", "No more questions!", "No more questions!", 'No more questions!', 'No more questions!', 'Still need to click the button above to save the results', None, None
else:
res = self.gpt_result[f'Question {self.curret_question + 1}']
question = self.questions[self.curret_question]
self.answer = res['answer']
self.highlighted_out = res['original sentences']
highlighted_out_html = self.highlight_text(self.text, self.highlighted_out)
self.highlighted_out = '\n'.join(self.highlighted_out)
return question, self.answer, highlighted_out_html, 'Please judge on the generated answer', 'Please judge on the generated answer', 'Still need to click the button above to save the results', None, None
def download_answer(self, path = './tmp', name = 'answer.xlsx'):
os.makedirs(path, exist_ok = True)
path = os.path.join(path, name)
self.ori_answer_df.to_excel(path, index = False)
return path
def download_corrected(self, path = './tmp', name = 'corrected_answer.xlsx'):
os.makedirs(path, exist_ok = True)
path = os.path.join(path, name)
self.answer_df.to_excel(path, index = False)
return path
def change_correct_answer(self, correctness):
if correctness == "Correct":
self.clicked_correct_answer = True
return "No need to change"
else:
if hasattr(self, 'answer'):
self.clicked_correct_answer = False
return self.answer
else:
return "No answer yet, you need to submit the document first"
def change_correct_reference(self, correctness):
if correctness == "Correct":
self.clicked_correct_reference = True
return "No need to change"
else:
if hasattr(self, 'highlighted_out'):
self.clicked_correct_reference = False
return self.highlighted_out
else:
return "No answer yet, you need to submit the document first" |