| from transformers import pipeline, DistilBertTokenizer, DistilBertForSequenceClassification | |
| import torch | |
| import gradio as gr | |
| myPipe = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english") | |
| #tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english") | |
| #model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english") | |
| def classify_text(prompt): | |
| return myPipe(prompt)[0] | |
| # inputs = tokenizer(prompt, return_tensors="pt") | |
| # with torch.no_grad(): | |
| # logits = model(**inputs).logits | |
| # predicted_class_id = logits.argmax().item() | |
| # return model.config.id2label[predicted_class_id] | |
| # Create a Gradio interface | |
| iface = gr.Interface(fn=classify_text, inputs=gr.Textbox(label="Your Text:"), outputs=gr.Textbox(label="Valence Score:")) | |
| iface.launch() | |