| import gradio as gr | |
| import transformers as pipeline | |
| from transformers import AutoTokenizer,AutoModelForSequenceClassification | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline | |
| model_name = "gyesibiney/covid-tweet-sentimental-Analysis-roberta" | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| sentiment = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) | |
| def get_sentiment(input_text): | |
| result = sentiment(input_text) | |
| sentiment_label = result[0]['label'] | |
| sentiment_score = result[0]['score'] | |
| if sentiment_label == 'LABEL_1': | |
| sentiment_label = "positive" | |
| elif sentiment_label == 'LABEL_0': | |
| sentiment_label = "neutral" | |
| else: | |
| sentiment_label = "negative" | |
| return f"Sentiment: {sentiment_label.capitalize()}, Score: {sentiment_score:.2f}" | |
| iface = gr.Interface(fn=get_sentiment, inputs=gr.inputs.Textbox(), outputs=gr.outputs.Textbox()) | |
| iface.launch(inline=True) |