| import gradio as gr | |
| from transformers import pipeline | |
| Examples_to_teach_model=f""" | |
| Text: I hate apples | |
| Sentiment analysis: | |
| Sentiments: Negative | |
| PPrint Key words: hate, aples | |
| Text: I enjoy watching movies | |
| Sentiment analysis: | |
| Sentiments: Positive | |
| PPrint Key words: enjoy, movies | |
| Text: I'm tired of this long process | |
| Sentiment analysis: | |
| Sentiments: Negative | |
| PPrint Key words: tired, long process | |
| """ | |
| question=""" | |
| Text: I love chips. | |
| Sentiment analysis: | |
| """ | |
| def make_prompt(sentence): | |
| prompt = Examples_to_teach_model+ "Text: " + sentence + "Sentiment analysis:" | |
| return prompt | |
| def get_sentiment_from_llm(sentence): | |
| input = make_prompt(sentence) | |
| inputs = tokenizer(input, return_tensors='pt') | |
| output = tokenizer.decode( | |
| model.generate( | |
| inputs["input_ids"], | |
| max_new_tokens=100, | |
| )[0], | |
| skip_special_tokens=True) | |
| return "\n".join(output.split('PPrint ')) | |
| get_rules_from_llm(question) | |
| gr.Interface.from_pipeline(pipe, | |
| title="Sentiment Analysis", | |
| description="Sentiment analysis and keywords extraction.", | |
| ).launch() |