| 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 analyze_sentiment(text): |
| inputs = tokenizer(text, return_tensors='pt') |
| outputs = model(**inputs) |
| prediction = torch.argmax(outputs.logits) |
| |
| sentiment = "Positive" if prediction.item() == 1 else "Negative" |
| return sentiment |
|
|
| |
| demo = gr.Interface( |
| fn=analyze_sentiment, |
| inputs="text", |
| outputs="text", |
| title="Sentiment Analysis with DistilBERT", |
| description="Enter text to analyze its sentiment (Positive/Negative)", |
| ) |
|
|
| |
| if __name__ == "__main__": |
| demo.launch() |
