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import requests
import random
import time
import pandas as pd
import gradio as gr
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
def read3(num_selected_former):
fname = 'data3_convai2_inferred.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)])
min_len = 5
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}
return res, index_selected
def func3(num_selected, human_predict, num1, num2, user_important):
chatbot = []
# num1: Human score; num2: AI score
fname = 'data3_convai2_inferred.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 text['binary_label'] == 1:
golden_label = int(50 * (1 - text['binary_score']))
else:
golden_label = int(50 * (1 + text['binary_score']))
# (START) off-the-shelf version -- slow at the beginning
# Load model directly
# Use a pipeline as a high-level helper
classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
output = classifier([text['text']])
print(output)
out = output[0]
# (END) off-the-shelf version
if out['label'] == 'Female':
ai_predict = int(100 * out['score'])
else:
ai_predict = 100 - int(100 * out['score'])
user_select = "You focused on "
flag_select = False
if user_important == "":
user_select += "nothing. Interesting! "
else:
user_select += user_important
user_select += ". "
# 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 golden_label > 60:
gender = ' (female)'
elif golden_label < 40:
gender = ' (male)'
else:
gender = ' (neutral)'
if abs(golden_label - human_predict) <= 20 and abs(golden_label - ai_predict) <= 20:
chatbot.append(("The correct answer is " + str(golden_label) + gender + ". Congratulations! 🎉 Both of you get the correct answer!", user_select))
num1 += 1
num2 += 1
elif abs(golden_label - human_predict) > 20 and abs(golden_label - ai_predict) > 20:
chatbot.append(("The correct answer is " + str(golden_label) + gender + ". Sorry.. No one gets the correct answer. But nice try! 😉", user_select))
elif abs(golden_label - human_predict) <= 20 and abs(golden_label - ai_predict) > 20:
chatbot.append(("The correct answer is " + str(golden_label) + gender + ". 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) + gender + ". Sorry.. AI wins in this round.", user_select))
num2 += 1
tot_scores = ''' ### <p style="text-align: center;"> Machine &ensp; ''' + str(int(num2)) + ''' &ensp; VS &ensp; ''' + str(int(num1)) + ''' &ensp; Human </p>'''
num_tmp = max(num1, num2)
y_lim_upper = (int((num_tmp + 3)/10)+1) * 10
return ai_predict, chatbot, num1, num2, tot_scores
def interpre3(num_selected):
fname = 'data3_convai2_inferred.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 func3_written(text_written, human_predict, lang_written):
chatbot = []
# num1: Human score; num2: AI score
# (START) off-the-shelf version
# tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
# model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
output = classifier([text_written])
print(output)
out = output[0]
# (END) off-the-shelf version
if out['label'] == 'Female':
ai_predict = int(100 * out['score'])
else:
ai_predict = 100 - int(100 * out['score'])
if abs(ai_predict - human_predict) <= 20:
chatbot.append(("AI gives it a close score! 🎉", "⬅️ Feel free to try another one! ⬅️"))
else:
chatbot.append(("AI thinks in a different way from human. 😉", "⬅️ Feel free to try another one! ⬅️"))
import shap
gender_classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification", return_all_scores=True)
explainer = shap.Explainer(gender_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