Update game3.py

game3.py

@@ -40,59 +40,28 @@ def func3(num_selected, human_predict, num1, num2, user_important):
         
         golden_label = (text['label']^1) * 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)
+    classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
     output = classifier([text['text']])
 
-    star2num = {
-        "5 stars": 100,
-        "4 stars": 75,
-        "3 stars": 50,
-        "2 stars": 25,
-        "1 star": 0,
-    }
+    # 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']
-
+    # ai_predict = out['label']
+    ai_predict = out['score']
+    
     user_select = "You focused on "
     flag_select = False
     if user_important == "":
@@ -251,52 +220,29 @@ def func3_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
 
     # 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")
-    
+    classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
+
     output = classifier([text_written])
 
-    star2num = {
-        "5 stars": 100,
-        "4 stars": 75,
-        "3 stars": 50,
-        "2 stars": 25,
-        "1 star": 0,
-    }
+    # 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']
+    # ai_predict = star2num[out['label']]
+    ai_predict = out['score']
 
     if abs(ai_predict - human_predict) <= 12.5:
         chatbot.append(("AI gives it a close score! 🎉", "⬅️ Feel free to try another one! ⬅️"))
@@ -309,13 +255,9 @@ def func3_written(text_written, human_predict, lang_written):
 
     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)
+    gender_classifier = pipeline("text-classification", model="padmajabfrl/Gender-Classification", return_all_scores=True)
 
-    explainer = shap.Explainer(sentiment_classifier)
+    explainer = shap.Explainer(gender_classifier)
 
     shap_values = explainer([text_written])
     interpretation = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))