new app file

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+import torch
+from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast
+
+def translate(text):
+  model_name = 'sbenel/emotion-distilbert'
+  tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
+  model= DistilBertForSequenceClassification.from_pretrained(model_name)
+
+  input = tokenizer(text, return_tensors="pt")
+  labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
+  output = model(**input, labels=labels)
+#  output = model.generate(input["input_ids"], max_length=40, num_beams=4, early_stopping=True)
+  
+  return tokenizer.decode(output[0], skip_special_tokens=True)
+  
+title = "Text Emotion Classification"
+inputs = gr.inputs.Textbox(lines=1, label="Text")
+outputs = [gr.outputs.Textbox(label="Emotions")]
+
+description = "Here use the [emotion-distilbert](https://huggingface.co/sbenel/emotion-distilbert) that was trained with [emotion dataset](https://huggingface.co/datasets/emotion)."
+
+iface = gr.Interface(fn=translate, inputs=inputs, outputs=outputs, theme="grass", title=title, description=description)
+iface.launch(enable_queue=True)