Update app.py

app.py

@@ -0,0 +1,46 @@
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+import torch
+import torch.nn.functional as F
+
+sentimentAnalysisModelName = "distilbert-base-uncased-finetuned-sst-2-english"
+sentimentAnalysisModel = AutoModelForSequenceClassification.from_pretrained(sentimentAnalysisModelName)
+sentimentAnalysisTokenizer = AutoTokenizer.from_pretrained(sentimentAnalysisModelName)
+
+sentimentAnalysisClassifier = pipeline("sentiment-analysis", model = sentimentAnalysisModelName, tokenizer = sentimentAnalysisTokenizer)
+sentimentAnalysisResults = sentimentAnalysisClassifier(["I am very proud to be in NJIT today and studying Data Science.", "I hope you don't hate this food!"])
+
+for sentimentAnalysisResult in sentimentAnalysisResults:
+  print(sentimentAnalysisResult)
+
+sentimentAnalysisTokens = sentimentAnalysisTokenizer.tokenize("I am very proud to be in NJIT today and studying Data Science.")
+sentimentAnalysisTokenids = sentimentAnalysisTokenizer.convert_tokens_to_ids(sentimentAnalysisTokens)
+sentimentAnalysisInputids = sentimentAnalysisTokenizer("I am very proud to be in NJIT today and studying Data Science.")
+
+print(f' Tokens: {sentimentAnalysisTokens}')
+print(f' Token IDs: {sentimentAnalysisTokenids}')
+print(f' Input IDs: {sentimentAnalysisInputids}')
+
+sentimentAnalysisXTrainingVariable = ["I am very proud to be in NJIT today and studying Data Science.", "I hope you don't hate this food!"]
+sentimentAnalysisBatch = sentimentAnalysisTokenizer(sentimentAnalysisXTrainingVariable, padding = True, truncation = True, max_length = 512, return_tensors = "pt")
+print(sentimentAnalysisBatch)
+
+with torch.no_grad():
+  sentimentAnalysisOutputs = sentimentAnalysisModel(**sentimentAnalysisBatch, labels = torch.tensor([1, 0]))
+  print(sentimentAnalysisOutputs)
+  sentimentAnalysisPredictions = F.softmax(sentimentAnalysisOutputs.logits, dim = 1)
+  print(sentimentAnalysisPredictions)
+  sentimentAnalysisLabels = torch.argmax(sentimentAnalysisPredictions, dim = 1)
+  print(sentimentAnalysisLabels)
+  sentimentAnalysisLabels = [sentimentAnalysisModel.config.id2label[sentimentAnalysisLabelID] for sentimentAnalysisLabelID in sentimentAnalysisLabels.tolist()]
+  print(sentimentAnalysisLabels)
+
+sentimentAnalysis_saveDirectory = "modelSaved"
+sentimentAnalysisTokenizer.save_pretrained(sentimentAnalysis_saveDirectory)
+sentimentAnalysisModel.save_pretrained(sentimentAnalysis_saveDirectory)
+
+sentimentAnalysisTokenizer = AutoTokenizer.from_pretrained(sentimentAnalysis_saveDirectory)
+sentimentAnalysisModel = AutoModelForSequenceClassification.from_pretrained(sentimentAnalysis_saveDirectory)
+
+