| import gradio as gr |
| import torch |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
|
| |
| model_name = "distilbert-base-uncased-finetuned-sst-2-english" |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
|
| def classify_text(text): |
| inputs = tokenizer(text, return_tensors="pt") |
| outputs = model(**inputs) |
| prediction = torch.nn.functional.softmax(outputs.logits, dim=-1) |
| return {"positive": prediction[0][1].item(), "negative": prediction[0][0].item()} |
|
|
| iface = gr.Interface(fn=classify_text, inputs="text", outputs="label") |
| iface.title = "Sentiment Analysis" |
| iface.description = "A simple sentiment analysis model using DistilBERT." |
| iface.launch() |
