| import gradio as gr | |
| import re | |
| from transformers import AutoTokenizer,TextClassificationPipeline | |
| from adapters import AutoAdapterModel | |
| def preprocess(issue): | |
| issue = re.sub(r'```.*?```', ' ', issue, flags=re.DOTALL) | |
| issue = re.sub(r'\n', ' ', issue) | |
| issue = re.sub(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', ' ', issue) | |
| issue = re.sub(r'\d+', ' ', issue) | |
| issue = re.sub(r'[^a-zA-Z0-9?\s]', ' ', issue) | |
| issue = re.sub(r'\s+', ' ', issue) | |
| return issue | |
| def text_classification(text): | |
| tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base", max_length=256, truncation=True, padding="max_length") | |
| model = AutoAdapterModel.from_pretrained("FacebookAI/roberta-base") | |
| adapter_react = model.load_adapter("buelfhood/irc-single-adapter", source = "hf",set_active=True) | |
| classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, max_length=256, padding="max_length", truncation=True,top_k=None) | |
| preprocessed_issue = preprocess (text) | |
| out = classifier(preprocessed_issue)[0] | |
| label_scores = {result['label']: result['score'] for result in out} | |
| return label_scores | |
| examples=["This is a question", "This is a bug", "This is an enhancement" ] | |
| io = gr.Interface(fn=text_classification, | |
| inputs= gr.Textbox(lines=3, label="Text", placeholder="Enter the text of the issue here:"), | |
| outputs="label", | |
| title="eIRC: An Efficient Issue Report Classification Web Application", | |
| description="Enter a text of an issue and see whether it is a bug, feature or question?", | |
| examples=examples) | |
| io.launch(share=True) |