Create app.py

app.py

@@ -0,0 +1,34 @@
+import torch
+from transformers import AutoTokenizer,AutoModelForSequenceClassification,Trainer,TrainingArguments
+import torch.nn.functional as F
+from datasets import load_dataset
+labels = {
+    'Negative':0,
+    'Positive':1,
+    'Neutral':2,
+    'Irrelevant':3,
+}
+model_name = 'distilbert-base-uncased'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name,num_labels=4)
+# Set up device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+# create function to predict sentence
+def pre_sentiment(sentence):
+    model.eval()
+    inputs = tokenizer(sentence,return_tensors='pt',truncation=True,padding=True)
+    inputs = {k:v.to(device) 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()
+    inv_label = {v:k for k,v in labels.items()}
+    result = inv_label.get(pre,'Unknown')
+    return result
+# create UI/UX
+import gradio as gr
+
+demo = gr.Interface(fn=pre_sentiment,inputs='text',outputs='text',title='Predict Sentiment')
+demo.launch()