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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")
width, height = gold_standard_img.size
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="""<h4 class='text-lg font-semibold my-2'>Note</h4>
- We use a strong supervised parsing model `benepar_en3` which is based on T5-small to compute the reference parse.<br>
- 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="Reference Parse Tree (BeNePar)", type="pil"),
gradio.outputs.Image(label="Predicted Parse Tree", type="pil"),
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()
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