Create app.py

app.py

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+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+def get_sentiment(sentences):
+    bert_dict = {}
+    vectors = tokenizer(sentences, padding = True, max_length = 65, return_tensors='pt').to(device)
+    outputs = bert_model(**vectors).logits
+    probs = torch.nn.functional.softmax(outputs, dim = 1)
+    for prob in probs:
+        bert_dict['neg'] = round(prob[0].item(), 3)
+        bert_dict['neu'] = round(prob[1].item(), 3)
+        bert_dict['pos'] = round(prob[2].item(), 3)
+        print (bert_dict)
+
+MODEL_NAME = 'RashidNLP/Finance-Sentiment-Classification'
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+bert_model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels = 3).to(device)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+get_sentiment(["The stock market will struggle until debt ceiling is increased", "ChatGPT is boosting Microsoft's search engine market share"])
+