tabletest

app.py

@@ -6,6 +6,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 import torch.nn.functional as F
 from transformers import BertForMaskedLM
+import pandas as pd
 
 # model = BertForMaskedLM.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
 
@@ -17,48 +18,72 @@ st.write('You selected:', option)
 
 if option == 'MILESTONE 3':
     st.write('test1')
-    model_name_0 = "Rathgeberj/milestone3_0"
-    # model_0 = AutoModelForSequenceClassification.from_pretrained(model_name_0)
-    model_0 = BertForMaskedLM.from_pretrained(model_name_0)
-    tokenizer_0 = AutoTokenizer.from_pretrained(model_name_0)
-    classifier_0 = pipeline(task="sentiment-analysis", model=model_0, tokenizer=tokenizer_0)
-
-    model_name_1 = "Rathgeberj/milestone3_1"
-    # model_1 = AutoModelForSequenceClassification.from_pretrained(model_name_1)
-    model_1 = BertForMaskedLM.from_pretrained(model_name_1)
-    tokenizer_1 = AutoTokenizer.from_pretrained(model_name_1)
-    classifier_1 = pipeline(task="sentiment-analysis", model=model_1, tokenizer=tokenizer_1)
-
-    model_name_2 = "Rathgeberj/milestone3_2"
-    # model_2 = AutoModelForSequenceClassification.from_pretrained(model_name_2)
-    model_2 = BertForMaskedLM.from_pretrained(model_name_2)
-    tokenizer_2 = AutoTokenizer.from_pretrained(model_name_2)
-    classifier_2 = pipeline(task="sentiment-analysis", model=model_2, tokenizer=tokenizer_2)
-
-    model_name_3 = "Rathgeberj/milestone3_3"
-    # model_3 = AutoModelForSequenceClassification.from_pretrained(model_name_3)
-    model_3 = BertForMaskedLM.from_pretrained(model_name_3)
-    tokenizer_3 = AutoTokenizer.from_pretrained(model_name_3)
-    classifier_3 = pipeline(task="sentiment-analysis", model=model_3, tokenizer=tokenizer_3)
-
-    model_name_4 = "Rathgeberj/milestone3_4"
-    # model_4 = AutoModelForSequenceClassification.from_pretrained(model_name_4)
-    model_4 = BertForMaskedLM.from_pretrained(model_name_4)
-    tokenizer_4 = AutoTokenizer.from_pretrained(model_name_4)
-    classifier_4 = pipeline(task="sentiment-analysis", model=model_4, tokenizer=tokenizer_4)
-
-    model_name_5 = "Rathgeberj/milestone3_5"
-    # model_5 = AutoModelForSequenceClassification.from_pretrained(model_name_5)
-    model_5 = BertForMaskedLM.from_pretrained(model_name_5)
-    tokenizer_5 = AutoTokenizer.from_pretrained(model_name_5)
-    classifier_5 = pipeline(task="sentiment-analysis", model=model_5, tokenizer=tokenizer_5)
-
-    models = [model_0, model_1, model_2, model_3, model_4, model_5]
-    tokenizers = [tokenizer_0, tokenizer_1, tokenizer_2, tokenizer_3, tokenizer_4, tokenizer_5]
-    classifiers = [classifier_0, classifier_1, classifier_2, classifier_3, classifier_4, classifier_5]
+    # model_name_0 = "Rathgeberj/milestone3_0"
+    # # model_0 = AutoModelForSequenceClassification.from_pretrained(model_name_0)
+    # model_0 = BertForMaskedLM.from_pretrained(model_name_0)
+    # tokenizer_0 = AutoTokenizer.from_pretrained(model_name_0)
+    # classifier_0 = pipeline(task="sentiment-analysis", model=model_0, tokenizer=tokenizer_0)
+
+    # model_name_1 = "Rathgeberj/milestone3_1"
+    # # model_1 = AutoModelForSequenceClassification.from_pretrained(model_name_1)
+    # model_1 = BertForMaskedLM.from_pretrained(model_name_1)
+    # tokenizer_1 = AutoTokenizer.from_pretrained(model_name_1)
+    # classifier_1 = pipeline(task="sentiment-analysis", model=model_1, tokenizer=tokenizer_1)
+
+    # model_name_2 = "Rathgeberj/milestone3_2"
+    # # model_2 = AutoModelForSequenceClassification.from_pretrained(model_name_2)
+    # model_2 = BertForMaskedLM.from_pretrained(model_name_2)
+    # tokenizer_2 = AutoTokenizer.from_pretrained(model_name_2)
+    # classifier_2 = pipeline(task="sentiment-analysis", model=model_2, tokenizer=tokenizer_2)
+
+    # model_name_3 = "Rathgeberj/milestone3_3"
+    # # model_3 = AutoModelForSequenceClassification.from_pretrained(model_name_3)
+    # model_3 = BertForMaskedLM.from_pretrained(model_name_3)
+    # tokenizer_3 = AutoTokenizer.from_pretrained(model_name_3)
+    # classifier_3 = pipeline(task="sentiment-analysis", model=model_3, tokenizer=tokenizer_3)
+
+    # model_name_4 = "Rathgeberj/milestone3_4"
+    # # model_4 = AutoModelForSequenceClassification.from_pretrained(model_name_4)
+    # model_4 = BertForMaskedLM.from_pretrained(model_name_4)
+    # tokenizer_4 = AutoTokenizer.from_pretrained(model_name_4)
+    # classifier_4 = pipeline(task="sentiment-analysis", model=model_4, tokenizer=tokenizer_4)
+
+    # model_name_5 = "Rathgeberj/milestone3_5"
+    # # model_5 = AutoModelForSequenceClassification.from_pretrained(model_name_5)
+    # model_5 = BertForMaskedLM.from_pretrained(model_name_5)
+    # tokenizer_5 = AutoTokenizer.from_pretrained(model_name_5)
+    # classifier_5 = pipeline(task="sentiment-analysis", model=model_5, tokenizer=tokenizer_5)
+
+    # models = [model_0, model_1, model_2, model_3, model_4, model_5]
+    # tokenizers = [tokenizer_0, tokenizer_1, tokenizer_2, tokenizer_3, tokenizer_4, tokenizer_5]
+    # classifiers = [classifier_0, classifier_1, classifier_2, classifier_3, classifier_4, classifier_5]
 
     # X_train = [textIn]
     # batch = tokenizer(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")
+    df = pd.DataFrame()
+    df.columns = ['Tweet', 'Highest Toxicity Class Overall', 'Score Overall', 'Highest Toxicity Class Except Toxic', 'Score Except Toxic']
+
+    pre_populated_tweets = ['Yo bitch Ja Rule is more succesful then youll ever be whats up with you and hating you sad mofuckas...i should bitch slap ur pethedic white faces and get you to kiss my ass you guys sicken me. Ja rule is about pride in da music man. dont diss that shit on him. and nothin is wrong bein like tupac he was a brother too...fuckin white boys get things right next time.', 
+                            'If you have a look back at the source, the information I updated was the correct form. I can only guess the source hadnt updated. I shall update the information once again but thank you for your message.', 
+                            'I dont anonymously edit articles at all.', 
+                            'Thank you for understanding. I think very highly of you and would not revert without discussion.', 
+                            'Please do not add nonsense to Wikipedia. Such edits are considered vandalism and quickly undone. If you would like to experiment, please use the sandbox instead. Thank you.   -', 
+                            'Dear god this site is horrible.', 
+                            'I think its crap that the link to roggenbier is to this article. Somebody that knows how to do things should change it.', 
+                            'Please stop. If you continue to vandalize Wikipedia, as you did to Homosexuality, you will be blocked from editing.', 
+                            'yeah, thanks for reviving the tradition of pissing all over articles because you want to live out your ethnic essentialism. Why let mere facts get into the way of enjoying that.', 
+                            'Ive deleted the page , as we have no evidence that you are the person named on that page, and its content goes against Wikipedias policies for the use of user pages.', 
+                            ]
+
+    for i in len(pre_populated_tweets):
+        df['Tweet'][i] = pre_populated_tweets[i]
+        df['Highest Toxicity Class Overall'][i] = 0
+        df['Score Overall'][i] = 0
+        df['Highest Toxicity Class Except Toxic'][i] = 0
+        df['Score Except Toxic'][i] = 0
+    
+    st.table(df)
+
     st.write('test2')
 
 if option == 'Pipeline':