import matplotlib matplotlib.use('agg') import os os.system('Xvfb :1 -screen 0 1600x1200x16 &') # create virtual display with size 1600x1200 and 16 bit color. Color can be changed to 24 or 8 os.environ['DISPLAY']=':1.0' # tell X clients to use our virtual DISPLAY :1.0 from PIL import Image import gradio import benepar import spacy import nltk from nltk.tree import Tree from nltk.draw.tree import TreeView from huggingface_hub import hf_hub_url, cached_download from weakly_supervised_parser.tree.evaluate import calculate_F1_for_spans, tree_to_spans from weakly_supervised_parser.inference import Predictor from weakly_supervised_parser.model.trainer import InsideOutsideStringClassifier from weakly_supervised_parser.model.span_classifier import LightningModel if __name__ == "__main__": nltk.download('stopwords') benepar.download('benepar_en3') nlp = spacy.load("en_core_web_md") nlp.add_pipe("benepar", config={"model": "benepar_en3"}) # inside_model = InsideOutsideStringClassifier(model_name_or_path="roberta-base", max_seq_length=256) fetch_url_inside_model = hf_hub_url(repo_id="nickil/weakly-supervised-parsing", filename="inside_model.ckpt", revision="main") inside_model = LightningModel.load_from_checkpoint(checkpoint_path=cached_download(fetch_url_inside_model)) # inside_model.load_model(pre_trained_model_path=cached_download(fetch_url_inside_model)) # outside_model = InsideOutsideStringClassifier(model_name_or_path="roberta-base", max_seq_length=64) # outside_model.load_model(pre_trained_model_path=TRAINED_MODEL_PATH + "outside_model.onnx") # inside_outside_model = InsideOutsideStringClassifier(model_name_or_path="roberta-base", max_seq_length=256) # inside_outside_model.load_model(pre_trained_model_path=TRAINED_MODEL_PATH + "inside_outside_model.onnx") def predict(sentence, model): gold_standard = list(nlp(sentence).sents)[0]._.parse_string if model == "inside": best_parse = Predictor(sentence=sentence).obtain_best_parse(predict_type="inside", model=inside_model, scale_axis=1, predict_batch_size=128) elif model == "outside": best_parse = Predictor(sentence=sentence).obtain_best_parse(predict_type="outside", model=outside_model, scale_axis=1, predict_batch_size=128) elif model == "inside-outside": best_parse = Predictor(sentence=sentence).obtain_best_parse(predict_type="inside_outside", model=inside_outside_model, scale_axis=1, predict_batch_size=128) sentence_f1 = calculate_F1_for_spans(tree_to_spans(gold_standard), tree_to_spans(best_parse)) TreeView(Tree.fromstring(gold_standard))._cframe.print_to_file('gold_standard.ps') TreeView(Tree.fromstring(best_parse))._cframe.print_to_file('best_parse.ps') #os.system('convert gold_standard.ps gold_standard.png') #os.system('convert best_parse.ps best_parse.png') #print(os.listdir()) gold_standard_img = Image.open("gold_standard.ps") best_parse_img = Image.open("best_parse.ps") gold_standard_img.save('gold_standard.png') best_parse_img.save('best_parse.png') gold_standard_img_png = Image.open("gold_standard.png") best_parse_img_png = Image.open("best_parse.png") return gold_standard_img_png, best_parse_img_png, f"{sentence_f1:.2f}" iface = gradio.Interface( title="Co-training an Unsupervised Constituency Parser with Weak Supervision", description="Demo for the repository - [weakly-supervised-parsing](https://github.com/Nickil21/weakly-supervised-parsing) (ACL Findings 2022)", theme="default", article="""

Note

- We use a strong supervised parsing model `benepar_en3` which is based on T5-small to compute the gold parse.
- Sentence F1 score corresponds to the macro F1 score. """, allow_flagging="never", fn=predict, inputs=[ gradio.inputs.Textbox(label="Sentence", placeholder="Enter a sentence in English", lines=2), gradio.inputs.Radio(["inside", "outside", "inside-outside"], default="inside", label="Choose Model"), ], outputs=[ gradio.outputs.Image(label="Gold Parse Tree"), gradio.outputs.Image(label="Predicted Parse Tree"), gradio.outputs.Textbox(label="F1 score"), ], examples=[ ["Russia 's war on Ukraine unsettles investors expecting carve-out deal uptick for 2022 .", "inside-outside"], ["Bitcoin community under pressure to cut energy use .", "inside"], ], ) iface.launch()