Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer,AutoModelForSequenceClassification,Trainer,TrainingArguments
+import torch.nn.functional as F
+from datasets import load_dataset
+model_name = 'thanhcong2001/Sentiment'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels = 2)
+
+# set up device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+def pre_sentiment(sentence):
+    model.eval()
+    inputs = tokenizer(sentence,return_tensors='pt',max_length=64,padding=True,truncation=True)
+    inputs = {k:v for k,v in inputs.items()}
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits,dim=-1)
+    pre = torch.argmax(probs,dim=-1).item()
+    score = torch.max(probs).item()
+    result = 'POSITIVE' if pre == 1 else 'NEGATIVE'
+    return f'Result: {result} - Score: {score}'
+import gradio as gr
+demo = gr.Interface(fn=pre_sentiment,inputs='text',outputs='text',title='Predict sentence')
+demo.launch()