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| import gradio as gr | |
| import os | |
| import torch | |
| import spacy | |
| from spacy import displacy | |
| nlp = spacy.load("en_core_web_sm") | |
| # Load model directly | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| def get_hatespeech_score(text): | |
| tokenizer = AutoTokenizer.from_pretrained("unhcr/hatespeech-detection") | |
| model = AutoModelForSequenceClassification.from_pretrained("unhcr/hatespeech-detection") | |
| # Tokenize input text | |
| inputs = tokenizer(text, return_tensors='pt') | |
| # Perform inference | |
| outputs = model(**inputs) | |
| # Get predicted label | |
| predicted_label_idx = torch.argmax(outputs.logits).item() | |
| predicted_label = model.config.id2label[predicted_label_idx] | |
| return predicted_label | |
| def text_analysis(text): | |
| label_1 = get_hatespeech_score(text) | |
| html = '''<!doctype html> | |
| <html> | |
| <body> | |
| <h1>Text Sentiment Analysis</h1> | |
| <div style=background-color:#d9eee1> | |
| <h2>Overall Sentiment</h2> | |
| <p>{}</p> | |
| </div> | |
| <div style=background-color:#fff4a3> | |
| <h2>Adult Content</h2> | |
| <p>{}</p> | |
| </div> | |
| <div style=background-color:#ffc0c7> | |
| <h2>Hate Speech</h2> | |
| <p>{}</p> | |
| </div> | |
| <div style=background-color:#cfb0b1> | |
| <h2>Text Summary</h2> | |
| <p>{}</p> | |
| </div> | |
| </body> | |
| </html> | |
| '''.format("Alpha", label_1, "Gamma", "Theta") | |
| doc = nlp(text) | |
| pos_tokens = [] | |
| for token in doc: | |
| pos_tokens.extend([(token.text, token.pos_), (" ", None)]) | |
| return pos_tokens, html | |
| demo = gr.Interface( | |
| text_analysis, | |
| gr.Textbox(placeholder="Enter sentence here..."), | |
| ["highlight", "html"], | |
| examples=[ | |
| ["What a beautiful morning for a walk!"], | |
| ["It was the best of times, it was the worst of times."], | |
| ], | |
| ) | |
| demo.launch() | |