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Update game3.py
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game3.py
CHANGED
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@@ -39,7 +39,10 @@ def func3(num_selected, human_predict, num1, num2, user_important):
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interpretation = eval(content[int(num_selected*2+1)])
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golden_label = (text['binary_label']^1) * 100
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# (START) off-the-shelf version -- slow at the beginning
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# Load model directly
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# Use a pipeline as a high-level helper
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@@ -47,13 +50,6 @@ def func3(num_selected, human_predict, num1, num2, user_important):
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classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
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output = classifier([text['text']])
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# star2num = {
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# "5 stars": 100,
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# "4 stars": 75,
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# "3 stars": 50,
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# "2 stars": 25,
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# "1 star": 0,
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# }
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print(output)
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out = output[0]
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@@ -80,102 +76,35 @@ def func3(num_selected, human_predict, num1, num2, user_important):
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# if not flag_select:
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# user_select += "nothing. Interesting! "
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user_select += "Wanna see how the AI made the guess? Click here. β¬
οΈ"
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if
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if
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num2 += 1
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else:
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golden_label += random.randint(-2, 2)
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while golden_label > 100 or golden_label < 0 or golden_label % 25 == 0:
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golden_label += random.randint(-2, 2)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
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num2 += 1
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else:
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else:
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chatbot.append(("The correct answer is " + str(golden_label) + ". Both wrong... Maybe next time you'll win!", user_select))
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else:
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chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. No one gets the correct answer. But nice try! π", user_select))
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else:
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if golden_label
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if
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golden_label = int((
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golden_label = int((human_predict + ai_predict)/2) + random.randint(-10, 10)
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ai_predict = int((golden_label + ai_predict) / 2)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! π Both of you get the correct answer!", user_select))
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num1 += 1
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num2 += 1
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elif ai_predict > 50 and human_predict <= 50:
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golden_label -= random.randint(0, 10)
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ai_predict = 90 + random.randint(-5, 5)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
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num2 += 1
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elif ai_predict <= 50 and human_predict > 50:
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golden_label = human_predict + random.randint(-4, 4)
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while golden_label > 100:
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golden_label = human_predict + random.randint(-4, 4)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Great! π You are close to the answer and better than AI!", user_select))
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num1 += 1
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else:
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chatbot.append(("The correct answer is " + str(golden_label) + ".
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else:
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while golden_label < 0:
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golden_label = int((human_predict + ai_predict)/2) + random.randint(-10, 10)
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ai_predict = int((golden_label + ai_predict) / 2)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! π Both of you get the correct answer!", user_select))
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num1 += 1
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num2 += 1
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elif ai_predict < 50 and human_predict >= 50:
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golden_label += random.randint(0, 10)
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ai_predict = 10 + random.randint(-5, 5)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
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num2 += 1
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elif ai_predict >= 50 and human_predict < 50:
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golden_label = human_predict + random.randint(-4, 4)
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while golden_label < 0:
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golden_label = human_predict + random.randint(-4, 4)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Great! π You are close to the answer and better than AI!", user_select))
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num1 += 1
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else:
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chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry... No one gets the correct answer. But nice try! π", user_select))
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# data = pd.DataFrame(
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# {
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# "Role": ["AI π€", "HUMAN π¨π©"],
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# "Scores": [num2, num1],
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# }
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# )
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# scroe_human = ''' # Human: ''' + str(int(num1))
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# scroe_robot = ''' # Robot: ''' + str(int(num2))
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tot_scores = ''' ### <p style="text-align: center;"> Machine   ''' + str(int(num2)) + '''   VS   ''' + str(int(num1)) + '''   Human </p>'''
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num_tmp = max(num1, num2)
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y_lim_upper = (int((num_tmp + 3)/10)+1) * 10
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# data,
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# x="Role",
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# y="Scores",
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# color="Role",
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# vertical=False,
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# y_lim=[0,y_lim_upper],
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# color_legend_position='none',
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# height=250,
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# width=500,
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# show_label=False,
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# container=False,
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# )
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# tooltip=["Role", "Scores"],
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return ai_predict, chatbot, num1, num2, tot_scores
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def interpre3(lang_selected, num_selected):
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interpretation = eval(content[int(num_selected*2+1)])
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golden_label = (text['binary_label']^1) * 100
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if golden_label == 0:
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golden_label = 50 * (1 - text['binary_score'])
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else:
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golden_label = 50 * (1 + text['binary_score'])
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# (START) off-the-shelf version -- slow at the beginning
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# Load model directly
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# Use a pipeline as a high-level helper
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classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
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output = classifier([text['text']])
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print(output)
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out = output[0]
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# if not flag_select:
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# user_select += "nothing. Interesting! "
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user_select += "Wanna see how the AI made the guess? Click here. β¬
οΈ"
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if abs(ai_predict - golden_label) < 12.5:
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if abs(human_predict - golden_label) < 12.5: # Both correct
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golden_label = int((human_predict + ai_predict) / 2)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Congratulations! π Both of you get the correct answer!", user_select))
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num1 += 1
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num2 += 1
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else:
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golden_label += random.randint(-2, 2)
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while golden_label > 100 or golden_label < 0 or golden_label % 25 == 0:
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golden_label += random.randint(-2, 2)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. AI wins in this round.", user_select))
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num2 += 1
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else:
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if abs(human_predict - golden_label) < abs(ai_predict - golden_label):
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if abs(human_predict - golden_label) < 12.5:
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golden_label = int((golden_label + human_predict) / 2)
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chatbot.append(("The correct answer is " + str(golden_label) + ". Great! π You are closer to the answer and better than AI!", user_select))
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num1 += 1
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else:
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chatbot.append(("The correct answer is " + str(golden_label) + ". Both wrong... Maybe next time you'll win!", user_select))
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else:
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chatbot.append(("The correct answer is " + str(golden_label) + ". Sorry.. No one gets the correct answer. But nice try! π", user_select))
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tot_scores = ''' ### <p style="text-align: center;"> Machine   ''' + str(int(num2)) + '''   VS   ''' + str(int(num1)) + '''   Human </p>'''
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num_tmp = max(num1, num2)
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y_lim_upper = (int((num_tmp + 3)/10)+1) * 10
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return ai_predict, chatbot, num1, num2, tot_scores
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def interpre3(lang_selected, num_selected):
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