app.py

import time
import spacy
import json

import gradio as gr
from spacy.tokens import Doc, Span
from spacy import displacy
import matplotlib.pyplot as plt
from matplotlib.colors import to_hex
import tempfile

from inference.model_inference import Inference
from configs import *

DESC_MD = """
<font size="3">
This space is a demo for <a href="https://arxiv.org/abs/2406.14654"> Major Entity Identification (MEI) </a>. MEI takes entities as additional input and aims to detect the mentions that refer only to these entities. <br/>
<br/>
Place the text in the text box with a single phrase of a selected entity in double curly braces(example: a single instance of {{Ron}} if you want to track Ron). Note that you can select one phrase for each entity and multiple entities can be selected. Check out the example below for clarity. <br/>
<br/>
Static: Uses an instance of: MEIRa-S model <br/>
Hybrid: Uses an instance of: MEIRa-H model <br/>
<br/>
The demo provides a json file with clusters and an HTML file with visualizations. The visualizations are color-coded based on the clusters. <br/>
</font>
"""


def get_MEIRa_clusters(doc_name, text, model_type):
    model_str = MODELS[model_type]
    model = Inference(model_str)

    output_dict = model.perform_coreference(text, doc_name)
    return output_dict


def coref_visualizer(doc_name, text, model_type):
    coref_output = get_MEIRa_clusters(doc_name, text, model_type)

    tokens = coref_output["tokenized_doc"]
    clusters = coref_output["clusters"]
    labels = coref_output["representative_names"]

    ## Get a pastel palette
    color_palette = {
        label: to_hex(plt.cm.get_cmap("tab20", len(labels))(i))
        for i, label in enumerate(labels)
    }

    nlp = spacy.blank("en")
    doc = Doc(nlp.vocab, words=tokens)

    print("Tokens:", tokens, flush=True)
    # print("Doc:", doc, flush=True)
    print(color_palette)

    spans = []
    for cluster_ind, cluster in enumerate(clusters[:-1]):
        label = labels[cluster_ind]
        for (start, end), mention in cluster:
            span = Span(doc, start, end + 1, label=label)
            spans.append(span)

    doc.spans["coref_spans"] = spans

    print("Rendering the visualization...")

    # color_map = {label: color_palette[i] for i, label in enumerate(labels)}
    # Generate the HTML output
    html = displacy.render(
        doc,
        style="span",
        options={
            "spans_key": "coref_spans",
            "colors": color_palette,
        },
        jupyter=False,
    )

    ## Create a hash based on time and doc_name
    time_hash = hash(str(time.time()) + doc_name)
    # html_file = f"temp/gradio_outputs/output_{time_hash}.html"
    # json_file = f"temp/gradio_outputs/output_{time_hash}.json"

    # Create a temporary HTML file
    with tempfile.NamedTemporaryFile(suffix=".html", delete=False) as tmp_html_file:
        html_file = tmp_html_file.name
        tmp_html_file.write(html.encode("utf-8"))

    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as tmp_json_file:
        json_file = tmp_json_file.name
        tmp_json_file.write(json.dumps(coref_output).encode("utf-8"))

    # with open(html_file, "w") as f:
    #     f.write(html)

    # with open(json_file, "w") as f:
    #     json.dump(coref_output, f)

    print("HTML file:", html_file)
    print("JSON file:", json_file)

    return (
        html_file,
        json_file,
        gr.DownloadButton(value=html_file, visible=True),
        gr.DownloadButton(value=json_file, visible=True),
    )


def download_html():
    return gr.DownloadButton(visible=False)


def download_json():
    return gr.DownloadButton(visible=False)


with open("example_harry.txt", "r") as f:
    example_harry = f.read()
options = ["static", "hybrid"]

with gr.Blocks() as demo:
    html_file = gr.File(visible=False)
    json_file = gr.File(visible=False)
    html_button = gr.DownloadButton("Download HTML", visible=False)
    json_button = gr.DownloadButton("Download JSON", visible=False)

    html_button.click()
    json_button.click()
    iface = gr.Interface(
        fn=coref_visualizer,
        inputs=[
            gr.Textbox(lines=1, placeholder="Enter document name:"),
            gr.Textbox(lines=10, placeholder="Enter text for coreference resolution:"),
            gr.Radio(choices=options, label="Select an Option"),
        ],
        outputs=[
            html_file,
            json_file,
            html_button,
            json_button,
        ],
        title="MEI Visualizer",
        description=DESC_MD,
        examples=[
            [
                "example",
                "{{Harry}} went to Hogwarts to meet Hemoine and {{Ron}} . He also met Ron's mother at the railway station.",
                "static",
            ],
            ["example_large", example_harry, "static"],
        ],
    )

demo.launch(debug=True)