| import torch | |
| import tensorflow as tf | |
| import flax | |
| import gradio as gr | |
| from transformers import pipeline | |
| sentiment_pipeline= pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment") | |
| # texts = ["Hugging face? weired, but memorable.", "I am despirate"] | |
| # results = sentiment_pipeline(texts) | |
| # for text, results in zip(texts, results): | |
| # print(f"Text: {text}") | |
| # print(f"Sentiment: {result['label']}, Score: {result['score']:.4f}\n") | |
| def predict_sentiment(text): | |
| result = sentiment_pipeline(text) | |
| return result[0]['label'], result[0]['score'] | |
| iface = gr.Interface(fn=predict_sentiment, inputs="text", outputs = ["label","number"]) | |
| if __name__ == "__main__": | |
| iface.launch() | |