| import torch |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig |
| import numpy as np |
| from scipy.special import softmax |
| import gradio as gr |
| torch.cuda.is_available() |
|
|
| model_path = "cardiffnlp/twitter-roberta-base-sentiment-latest" |
|
|
| tokenizer = AutoTokenizer.from_pretrained(model_path) |
| config = AutoConfig.from_pretrained(model_path) |
| model = AutoModelForSequenceClassification.from_pretrained(model_path) |
|
|
| def sentiment_analysis(text): |
| encoded_input = tokenizer(text, return_tensors='pt') |
| output = model(**encoded_input) |
| scores_ = output[0][0].detach().numpy() |
| scores_ = softmax(scores_) |
| labels = ['Negative', 'Neutral', 'Positive'] |
| scores = {l: float(s) for (l, s) in zip(labels, scores_)} |
| return scores |
|
|
| demo = gr.Interface( |
| theme=gr.themes.Base(), |
| fn=sentiment_analysis, |
| inputs=gr.Textbox(placeholder="Write your text here..."), |
| outputs="label", |
| examples=[ |
| ["I'm thrilled about the job offer!"], |
| ["The weather today is absolutely beautiful."], |
| ["I had a fantastic time at the concert last night."], |
| ["I'm so frustrated with this software glitch."], |
| ["The customer service was terrible at the store."], |
| ["I'm really disappointed with the quality of this product."] |
| ], |
| title='Sentiment Analysis App', |
| description='This app classifies a positive, neutral, or negative sentiment.' |
| ) |
| demo.launch() |
|
|
