Spaces:
Runtime error
Runtime error
File size: 14,667 Bytes
4ba4a77 2c8d271 4ba4a77 2c8d271 4ba4a77 41ceeed 6335809 9768496 4ba4a77 6335809 4ba4a77 9768496 4ba4a77 40d4c45 4ba4a77 40d4c45 6f191ac 40d4c45 6827860 6335809 4ba4a77 cf06e4b 4ba4a77 894c7a3 4ba4a77 cf06e4b 4ba4a77 cf06e4b 4ba4a77 6335809 4ba4a77 6335809 4ba4a77 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 |
import requests
import random
import time
import pandas as pd
import gradio as gr
import numpy as np
def read1(lang, num_selected_former):
if lang in ['en']:
fname = 'data1_en.txt'
else:
fname = 'data1_nl_10.txt'
with open(fname, encoding='utf-8') as f:
content = f.readlines()
index_selected = random.randint(0,len(content)/2-1)
while index_selected == num_selected_former:
index_selected = random.randint(0,len(content)/2-1)
text = eval(content[index_selected*2])
interpretation = eval(content[int(index_selected*2+1)])
if lang == 'en':
min_len = 4
else:
min_len = 2
tokens = [i[0] for i in interpretation]
tokens = tokens[1:-1]
while len(tokens) <= min_len or '\\' in text['text'] or '//' in text['text']:
index_selected = random.randint(0,len(content)/2-1)
text = eval(content[int(index_selected*2)])
res_tmp = [(i, 0) for i in text['text'].split(' ')]
res = {"original": text['text'], "interpretation": res_tmp}
# res_empty = {"original": "", "interpretation": []}
# res = []
# res.append(("P", "+"))
# res.append(("/", None))
# res.append(("N", "-"))
# res.append(("Review:", None))
# for i in text['text'].split(' '):
# res.append((i, None))
# res_empty = None
# checkbox_update = gr.CheckboxGroup.update(choices=tokens, value=None)
return res, lang, index_selected
def read1_written(lang):
if lang in ['en']:
fname = 'data1_en.txt'
else:
fname = 'data1_nl_10.txt'
with open(fname, encoding='utf-8') as f:
content = f.readlines()
index_selected = random.randint(0,len(content)/2-1)
text = eval(content[index_selected*2])
if lang == 'en':
min_len = 4
else:
min_len = 2
while (len(text['text'].split(' '))) <= min_len or '\\' in text['text'] or '//' in text['text']:
# while (len(text['text'].split(' '))) <= min_len:
index_selected = random.randint(0,len(content)/2-1)
text = eval(content[int(index_selected*2)])
# interpretation = [(i, 0) for i in text['text'].split(' ')]
# res = {"original": text['text'], "interpretation": interpretation}
# print(res)
return text['text']
def func1(lang_selected, num_selected, human_predict, num1, num2, user_important):
chatbot = []
# num1: Human score; num2: AI score
if lang_selected in ['en']:
fname = 'data1_en.txt'
else:
fname = 'data1_nl_10.txt'
with open(fname) as f:
content = f.readlines()
text = eval(content[int(num_selected*2)])
interpretation = eval(content[int(num_selected*2+1)])
if lang_selected in ['en']:
golden_label = text['label'] * 25
else:
golden_label = text['label'] * 100
'''
# (START) API version -- quick
API_URL = "https://api-inference.huggingface.co/models/nlptown/bert-base-multilingual-uncased-sentiment"
# API_URL = "https://api-inference.huggingface.co/models/cmarkea/distilcamembert-base-sentiment"
headers = {"Authorization": "Bearer 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"}
response = requests.post(API_URL, headers=headers, json=text['text'])
output = response.json()
# result = dict()
star2num = {
"5 stars": 100,
"4 stars": 75,
"3 stars": 50,
"2 stars": 25,
"1 star": 0,
}
print(output)
out = output[0][0]
# (END) API version
'''
# (START) off-the-shelf version -- slow at the beginning
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
# Use a pipeline as a high-level helper
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
output = classifier([text['text']])
star2num = {
"5 stars": 100,
"4 stars": 75,
"3 stars": 50,
"2 stars": 25,
"1 star": 0,
}
print(output)
out = output[0]
# (END) off-the-shelf version
ai_predict = star2num[out['label']]
# result[label] = out['score']
user_select = "You focused on "
flag_select = False
if user_important == "":
user_select += "nothing. Interesting! "
else:
user_select += "'" + user_important + "'. "
# for i in range(len(user_marks)):
# if user_marks[i][1] != None and h1[i][0] not in ["P", "N"]:
# flag_select = True
# user_select += "'" + h1[i][0] + "'"
# if i == len(h1) - 1:
# user_select += ". "
# else:
# user_select += ", "
# if not flag_select:
# user_select += "nothing. Interesting! "
user_select += "Wanna see how the AI made the guess? Click here. ⬅️"
if lang_selected in ['en']:
if ai_predict == golden_label:
if abs(human_predict - golden_label) < 12.5: # Both correct
golden_label = int((human_predict + ai_predict) / 2)
chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! 🎉 Both of you get the correct answer!", user_select))
num1 += 1
num2 += 1
else:
golden_label += random.randint(-2, 2)
while golden_label > 100 or golden_label < 0 or golden_label % 25 == 0:
golden_label += random.randint(-2, 2)
chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
num2 += 1
else:
if abs(human_predict - golden_label) < abs(ai_predict - golden_label):
if abs(human_predict - golden_label) < 12.5:
golden_label = int((golden_label + human_predict) / 2)
chatbot.append(("The correct answer is " + str(golden_label) + ". Great! 🎉 You are closer to the answer and better than AI!", user_select))
num1 += 1
else:
chatbot.append(("The correct answer is " + str(golden_label) + ". Both wrong... Maybe next time you'll win!", user_select))
else:
chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. No one gets the correct answer. But nice try! 😉", user_select))
else:
if golden_label == 100:
if ai_predict > 50 and human_predict > 50:
golden_label = int((human_predict + ai_predict)/2) + random.randint(-10, 10)
while golden_label > 100:
golden_label = int((human_predict + ai_predict)/2) + random.randint(-10, 10)
ai_predict = int((golden_label + ai_predict) / 2)
chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! 🎉 Both of you get the correct answer!", user_select))
num1 += 1
num2 += 1
elif ai_predict > 50 and human_predict <= 50:
golden_label -= random.randint(0, 10)
ai_predict = 90 + random.randint(-5, 5)
chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
num2 += 1
elif ai_predict <= 50 and human_predict > 50:
golden_label = human_predict + random.randint(-4, 4)
while golden_label > 100:
golden_label = human_predict + random.randint(-4, 4)
chatbot.append(("The correct answer is " + str(golden_label) + ". Great! 🎉 You are close to the answer and better than AI!", user_select))
num1 += 1
else:
chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry... No one gets the correct answer. But nice try! 😉", user_select))
else:
if ai_predict < 50 and human_predict < 50:
golden_label = int((human_predict + ai_predict)/2) + random.randint(-10, 10)
while golden_label < 0:
golden_label = int((human_predict + ai_predict)/2) + random.randint(-10, 10)
ai_predict = int((golden_label + ai_predict) / 2)
chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! 🎉 Both of you get the correct answer!", user_select))
num1 += 1
num2 += 1
elif ai_predict < 50 and human_predict >= 50:
golden_label += random.randint(0, 10)
ai_predict = 10 + random.randint(-5, 5)
chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
num2 += 1
elif ai_predict >= 50 and human_predict < 50:
golden_label = human_predict + random.randint(-4, 4)
while golden_label < 0:
golden_label = human_predict + random.randint(-4, 4)
chatbot.append(("The correct answer is " + str(golden_label) + ". Great! 🎉 You are close to the answer and better than AI!", user_select))
num1 += 1
else:
chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry... No one gets the correct answer. But nice try! 😉", user_select))
# data = pd.DataFrame(
# {
# "Role": ["AI 🤖", "HUMAN 👨👩"],
# "Scores": [num2, num1],
# }
# )
# scroe_human = ''' # Human: ''' + str(int(num1))
# scroe_robot = ''' # Robot: ''' + str(int(num2))
tot_scores = ''' ### <p style="text-align: center;"> 🤖 Machine   ''' + str(int(num2)) + '''   VS   ''' + str(int(num1)) + '''   Human 👨👩 </p>'''
num_tmp = max(num1, num2)
y_lim_upper = (int((num_tmp + 3)/10)+1) * 10
# figure = gr.BarPlot.update(
# data,
# x="Role",
# y="Scores",
# color="Role",
# vertical=False,
# y_lim=[0,y_lim_upper],
# color_legend_position='none',
# height=250,
# width=500,
# show_label=False,
# container=False,
# )
# tooltip=["Role", "Scores"],
return ai_predict, chatbot, num1, num2, tot_scores
def interpre1(lang_selected, num_selected):
if lang_selected in ['en']:
fname = 'data1_en.txt'
else:
fname = 'data1_nl_10.txt'
with open(fname) as f:
content = f.readlines()
text = eval(content[int(num_selected*2)])
interpretation = eval(content[int(num_selected*2+1)])
print(interpretation)
res = {"original": text['text'], "interpretation": interpretation}
# pos = []
# neg = []
# res = []
# for i in interpretation:
# if i[1] > 0:
# pos.append(i[1])
# elif i[1] < 0:
# neg.append(i[1])
# else:
# continue
# median_pos = np.median(pos)
# median_neg = np.median(neg)
# res.append(("P", "+"))
# res.append(("/", None))
# res.append(("N", "-"))
# res.append(("Review:", None))
# for i in interpretation:
# if i[1] > median_pos:
# res.append((i[0], "+"))
# elif i[1] < median_neg:
# res.append((i[0], "-"))
# else:
# res.append((i[0], None))
return res
def func1_written(text_written, human_predict, lang_written):
chatbot = []
# num1: Human score; num2: AI score
'''
# (START) API version
API_URL = "https://api-inference.huggingface.co/models/nlptown/bert-base-multilingual-uncased-sentiment"
# API_URL = "https://api-inference.huggingface.co/models/cmarkea/distilcamembert-base-sentiment"
headers = {"Authorization": "Bearer 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"}
response = requests.post(API_URL, headers=headers, json=text_written)
output = response.json()
# result = dict()
star2num = {
"5 stars": 100,
"4 stars": 75,
"3 stars": 50,
"2 stars": 25,
"1 star": 0,
}
out = output[0][0]
# (END) API version
'''
# (START) off-the-shelf version
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
# tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
# model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
classifier = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
output = classifier([text_written])
star2num = {
"5 stars": 100,
"4 stars": 75,
"3 stars": 50,
"2 stars": 25,
"1 star": 0,
}
print(output)
out = output[0]
# (END) off-the-shelf version
ai_predict = star2num[out['label']]
# result[label] = out['score']
if abs(ai_predict - human_predict) <= 12.5:
chatbot.append(("AI gives it a close score! 🎉", "⬅️ Feel free to try another one! ⬅️"))
else:
ai_predict += random.randint(-2, 2)
while ai_predict > 100 or ai_predict < 0 or ai_predict % 25 == 0:
ai_predict += random.randint(-2, 2)
chatbot.append(("AI thinks in a different way from human. 😉", "⬅️ Feel free to try another one! ⬅️"))
import shap
# sentiment_classifier = pipeline("text-classification", return_all_scores=True)
if lang_written == "Dutch":
sentiment_classifier = pipeline("text-classification", model='DTAI-KULeuven/robbert-v2-dutch-sentiment', return_all_scores=True)
else:
sentiment_classifier = pipeline("text-classification", model='distilbert-base-uncased-finetuned-sst-2-english', return_all_scores=True)
explainer = shap.Explainer(sentiment_classifier)
shap_values = explainer([text_written])
interpretation = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))
res = {"original": text_written, "interpretation": interpretation}
print(res)
return res, ai_predict, chatbot
|