Update game3.py

game3.py

@@ -1,17 +1,349 @@
 import requests
 import random
 import time
+import pandas as pd
+import gradio as gr
+import numpy as np
 
-def func3(text):
-    filter = False
-    API_URL = "https://api-inference.huggingface.co/models/Abderrahim2/bert-finetuned-gender_classification"
+def read3(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 func3(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
+        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 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 &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
+    # 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 interpre3(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 func3_written(text_written, human_predict, lang_written):
+    chatbot = []
+    # num1: Human score; num2: AI score
+
+    '''
+    # (START) API version
     
-    response = requests.post(API_URL, headers=headers, json=text)     
+    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()
-    for out in output[0]:
-        label = out['label']
-        if label == 'undefined' and filter: continue
-        result[label] = out['score']
-    return result
+    
+    # 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