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| import logging | |
| import os | |
| from typing import List, Tuple | |
| import gradio as gr | |
| import pandas as pd | |
| import spacy | |
| import torch | |
| from transformers import AutoModelForTokenClassification, AutoTokenizer | |
| try: | |
| nlp = spacy.load("pt_core_news_sm") | |
| except Exception: | |
| os.system("python -m spacy download pt_core_news_sm") | |
| nlp = spacy.load("pt_core_news_sm") | |
| model = AutoModelForTokenClassification.from_pretrained("Emanuel/porttagger-news-base") | |
| tokenizer = AutoTokenizer.from_pretrained("Emanuel/porttagger-news-base") | |
| logger = logging.getLogger() | |
| logger.setLevel(logging.DEBUG) | |
| def predict(text, nlp, logger=None) -> Tuple[List[str], List[str]]: | |
| doc = nlp(text) | |
| tokens = [token.text for token in doc] | |
| logger.info("Starting predictions for sentence: {}".format(text)) | |
| input_tokens = tokenizer( | |
| tokens, | |
| return_tensors="pt", | |
| is_split_into_words=True, | |
| return_offsets_mapping=True, | |
| return_special_tokens_mask=True, | |
| ) | |
| output = model(input_tokens["input_ids"]) | |
| i_token = 0 | |
| labels = [] | |
| scores = [] | |
| for off, is_special_token, pred in zip( | |
| input_tokens["offset_mapping"][0], | |
| input_tokens["special_tokens_mask"][0], | |
| output.logits[0], | |
| ): | |
| if is_special_token or off[0] > 0: | |
| continue | |
| label = model.config.__dict__["id2label"][int(pred.argmax(axis=-1))] | |
| if logger is not None: | |
| logger.info("{}, {}, {}".format(off, tokens[i_token], label)) | |
| labels.append(label) | |
| scores.append( | |
| "{:.2f}".format(100 * float(torch.softmax(pred, dim=-1).detach().max())) | |
| ) | |
| i_token += 1 | |
| return tokens, labels, scores | |
| def text_analysis(text): | |
| tokens, labels, scores = predict(text, nlp, logger) | |
| pos_count = pd.DataFrame( | |
| { | |
| "token": tokens, | |
| "etiqueta": labels, | |
| "confiança": scores, | |
| } | |
| ) | |
| pos_tokens = [] | |
| for token, label in zip(tokens, labels): | |
| pos_tokens.extend([(token, label), (" ", None)]) | |
| output_highlighted.update(visible=True) | |
| output_df.update(visible=True) | |
| return { | |
| output_highlighted: output_highlighted.update(visible=True, value=(pos_tokens)), | |
| output_df: output_df.update(visible=True, value=pos_count), | |
| } | |
| css = open("style.css").read() | |
| top_html = open("top.html").read() | |
| bottom_html = open("bottom.html").read() | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML(top_html) | |
| text = gr.Textbox(placeholder="Enter your text here...", label="Input") | |
| examples = gr.Examples( | |
| examples=[ | |
| [ | |
| "A população não poderia ter acesso a relatórios que explicassem, por exemplo, os motivos exatos de atrasos em obras de linhas e estações." | |
| ], | |
| ["Filme 'Star Wars : Os Últimos Jedi' ganha trailer definitivo; assista."], | |
| ], | |
| inputs=[text], | |
| label="Select an example", | |
| ) | |
| output_highlighted = gr.HighlightedText(label="Colorful output", visible=False) | |
| output_df = gr.Dataframe(label="Tabular output", visible=False) | |
| submit_btn = gr.Button("Send") | |
| submit_btn.click( | |
| fn=text_analysis, inputs=text, outputs=[output_highlighted, output_df] | |
| ) | |
| gr.HTML(bottom_html) | |
| demo.launch(debug=True) | |