import spacy

# Load the model
nlp = spacy.load("en_blackstone_proto")

def get_top_cat(doc):
    """
    Function to identify the highest scoring category
    prediction generated by the text categoriser. 
    """
    cats = doc.cats
    max_score = max(cats.values()) 
    max_cats = [k for k, v in cats.items() if v == max_score]
    max_cat = max_cats[0]
    return (max_cat, max_score)

text = """
It is a well-established principle of law that the transactions of independent states between each other are governed by other laws than those which municipal courts administer. \
It is, however, in my judgment, insufficient to react to the danger of over-formalisation and “judicialisation” simply by emphasising flexibility and context-sensitivity. \
The question is whether on the facts found by the judge, the (or a) proximate cause of the loss of the rig was “inherent vice or nature of the subject matter insured” within the meaning of clause 4.4 of the Institute Cargo Clauses (A).
"""

# Apply the model to the text
doc = nlp(text)

# Get the sentences in the passage of text
sentences = [sent.text for sent in doc.sents]

# Print the sentence and the corresponding predicted category.
for sentence in sentences:
    doc = nlp(sentence)
    top_category = get_top_cat(doc)
    print (f"\"{sentence}\" {top_category}\n")

>>> "In my judgment, it is patently obvious that cats are a type of dog." ('CONCLUSION', 0.9990500807762146)
>>> "It is a well settled principle that theft is wrong." ('AXIOM', 0.556410014629364)
>>> "The question is whether on the facts found by the judge, the (or a) proximate cause of the loss of the rig was “inherent vice or nature of the subject matter insured” within the meaning of clause 4.4 of the Institute Cargo Clauses (A)." ('ISSUE', 0.5040785074234009)

import spacy
from blackstone.compound_cases import CompoundCases

nlp = spacy.load("en_blackstone_proto")

compound_pipe = CompoundCases(nlp)
nlp.add_pipe(compound_pipe)

text = "As I have indicated, this was the central issue before the judge. On this issue the defendants relied (successfully below) on the decision of the High Court in Gelmini v Moriggia [1913] 2 KB 549. In Jone's case [1915] 1 KB 45, the defendant wore a hat."
doc = nlp(text)

for compound_ref in doc._.compound_cases:
    print(compound_ref)

>>> Gelmini v Moriggia [1913] 2 KB 549
>>> Jone's case [1915] 1 KB 45

import spacy
from blackstone.segmenter import sentence_segmenter

nlp = spacy.load("en_blackstone_proto")

# remove the default spaCy sentencizer from the model pipeline
if "sentencizer" in nlp.pipe_names:
    nlp.remove_pipe('sentencizer')

# add the Blackstone sentence_segmenter to the pipeline before the parser
nlp.add_pipe(sentence_segmenter, before="parser")

doc = nlp("Some more legal text goes here. And a little bit more legal text goes here")

for sent in doc.sents:
    print (sent.text)

import spacy
from blackstone.abbreviations import AbbreviationDetector

nlp = spacy.load("en_blackstone_proto")

# Add the abbreviation pipe to the spacy pipeline.
abbreviation_pipe = AbbreviationDetector(nlp)
nlp.add_pipe(abbreviation_pipe)

doc = nlp('The European Court of Human Rights ("ECtHR") is the court ultimately responsible for applying the European Convention on Human Rights ("ECHR").')

print("Abbreviation", "\t", "Definition")
for abrv in doc._.abbreviations:
    print(f"{abrv} \t ({abrv.start}, {abrv.end}) {abrv._.long_form}")

>>> "ECtHR"          (7, 10) European Court of Human Rights
>>> "ECHR"   (25, 28) European Convention on Human Rights

import spacy
from blackstone.legislation_linker import extract_legislation_relations
nlp = spacy.load("en_blackstone_proto")

text = "The Secretary of State was at pains to emphasise that, if a withdrawal agreement is made, it is very likely to be a treaty requiring ratification and as such would have to be submitted for review by Parliament, acting separately, under the negative resolution procedure set out in section 20 of the Constitutional Reform and Governance Act 2010. Theft is defined in section 1 of the Theft Act 1968"

doc = nlp(text) 
relations = extract_legislation_relations(doc)
for provision, provision_url, instrument, instrument_url in relations:
    print(f"\n{provision}\t{provision_url}\t{instrument}\t{instrument_url}")

>>> section 20      http://www.legislation.gov.uk/ukpga/2010/25/section/20  Constitutional Reform and Governance Act 2010   http://www.legislation.gov.uk/ukpga/2010/25/contents

>>> section 1       http://www.legislation.gov.uk/ukpga/1968/60/section/1   Theft Act 1968  http://www.legislation.gov.uk/ukpga/1968/60/contents