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import subprocess
subprocess.run(["pip", "install", "gradio==4.31.5"])
subprocess.run(["pip", "install", "spacy"])
subprocess.run(["pip", "install", "glirel"])
subprocess.run(["pip", "install", "scipy==1.10.1"])
subprocess.run(["pip", "install", "numpy==1.26.4"])
subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"])
subprocess.run(["python", "-m", "spacy", "download", "en_core_web_md"])
subprocess.run(["python", "-m", "spacy", "download", "en_core_web_lg"])
from typing import Dict, Union
import gradio as gr
from glirel import GLiREL
import spacy
examples = [
[
"Amazon, founded by Jeff Bezos, is a leader in e-commerce and cloud computing. The company has also ventured into artificial intelligence and digital streaming.",
"en_core_web_sm",
"Founded_By, Located_In, Produces, Operates_In, Works_With, Known_For, Headquartered_In, Partnership_With, Innovates_In, Established_In",
],
[
"J.K. Rowling, the author of the Harry Potter series, has significantly impacted modern literature. Her books have been translated into numerous languages and adapted into successful films.",
"en_core_web_sm",
"Translated_Into, Adapted_Into, Born_In, Author_Of, Known_For, Works_With, Located_In, Writes_For, Produced_By, Published_By"
],
[
"Apple Inc. was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in April 1976. The company is headquartered in Cupertino, California.",
"en_core_web_sm",
"CO_FOUNDER, HEADQUARTERED_IN, FOUNDED_BY, LOCATED_IN, ESTABLISHED_IN, PARTNERSHIP_WITH, WORKS_WITH, KNOWN_FOR"
]
]
# Load the relation extraction model
rel_model = GLiREL.from_pretrained("jackboyla/glirel_beta")
# Function to perform Named Entity Recognition
def perform_ner(text, model_name):
nlp = spacy.load(model_name)
doc = nlp(text)
return doc
# Function to extract relations
def extract_relations(tokens, ner, labels):
relations = rel_model.predict_relations(tokens, labels, threshold=0.0, ner=ner, top_k=1)
sorted_data_desc = sorted(relations, key=lambda x: x['score'], reverse=True)
return sorted_data_desc
def format_ner(text, ner):
if isinstance(ner[0], spacy.tokens.Span):
# if ner is spacy entities; otherwise we assume the format is correct
ner = [[ent.start_char, ent.end_char, ent.label_, ent.text] for ent in ner]
return {
"text": text,
"entities": [
{
"entity": entity[2],
"word": entity[3],
"start": entity[0],
"end": entity[1],
"score": 0,
}
for entity in ner
],
}
# Gradio Interface
def process(text, model_name, labels):
doc = perform_ner(text, model_name)
tokens = [token.text for token in doc]
ner = [[ent.start, (ent.end-1), ent.label_, ent.text] for ent in doc.ents]
labels = labels.split(',')
relations = extract_relations(tokens, ner, labels)
print(relations)
formatted_ner = format_ner(doc.text, doc.ents)
formatted_rel = ""
for item in relations:
formatted_rel += f"{item['head_text']} --> {item['label']} --> {item['tail_text']} \t\t| score: {item['score']}\n"
return formatted_ner, formatted_rel
# Gradio App Layout
with gr.Blocks() as demo:
gr.Markdown("# 🕵️♀️GLiREL: Zero-Shot Relation Extraction")
gr.Markdown("GitHub: https://github.com/jackboyla/GLiREL")
text_input = gr.Textbox(label="Input Text", value="Jack lives in London but he was born in Mongolia.")
model_name_input = gr.Dropdown(choices=["en_core_web_sm", "en_core_web_md", "en_core_web_lg"], label="NER Model", value="en_core_web_sm")
labels_input = gr.Textbox(label="Relation Labels (comma-separated)", value="country of origin, licensed to broadcast to, father, followed by, characters")
ner_output = gr.HighlightedText(label="NER")
rel_output = gr.Textbox(label="Relation Extraction Results")
extract_button = gr.Button("Extract Relations")
extract_button.click(
fn=process,
inputs=[text_input, model_name_input, labels_input],
outputs=[ner_output, rel_output]
)
examples = gr.Examples(
examples,
fn=process,
inputs=[text_input, model_name_input, labels_input],
outputs=[ner_output, rel_output],
cache_examples=True,
)
if __name__ == "__main__":
demo.launch(server_port=9989)
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