Create app.py

app.py

@@ -0,0 +1,26 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+import torch
+
+description = "Sentiment Analysis :) && :("
+title = "SentBERT"
+examples = [["That ice cream was really bad"], ["Great to meet you!"], ["Hey, there's a snake there"]]
+
+class2interpret = {
+    0: 'Positive/Neutral',
+    1: 'Negative'
+}
+
+def classify(example):
+  tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+  model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")  
+  inputs = tokenizer(example, return_tensors="pt")
+  with torch.no_grad():
+      logits = model(**inputs).logits
+  
+  probs = torch.nn.Softmax(dim=1)(logits).tolist()[0]
+
+  return {class2interpret[0]: probs[0], class2interpret[1]: probs[1]}, {class2interpret[0]: probs[0], class2interpret[1]: probs[1]}
+
+interface = gr.Interface(fn=classify, inputs='text', outputs=['label', 'json'], examples=examples, description=description, title=title)
+interface.launch()