event_nugget_predict.py

import streamlit as st
from annotated_text import annotated_text
import torch
from torch import nn
from torch.utils.data import DataLoader
from .nugget_model_utils import CustomRobertaWithPOS as NuggetModel
from .nugget_model_utils import tokenize_and_align_labels_with_pos_ner_dep, find_nearest_nugget_features, find_dep_depth
from .utils import get_idxs_from_text, event_nugget_list
import spacy
from transformers import AutoTokenizer
from datasets import load_dataset, Features, ClassLabel, Value, Sequence, Dataset
import os


os.environ["TOKENIZERS_PARALLELISM"] = "true"

def find_dep_depth(token):
    depth = 0
    current_token = token
    while current_token.head != current_token:
        depth += 1
        current_token = current_token.head
    return min(depth, 16)


nlp = spacy.load('en_core_web_sm')

pos_spacy_tag_list = ["ADJ","ADP","ADV","AUX","CCONJ","DET","INTJ","NOUN","NUM","PART","PRON","PROPN","PUNCT","SCONJ","SYM","VERB","SPACE","X"]
ner_spacy_tag_list = [bio + entity for entity in list(nlp.get_pipe('ner').labels) for bio in ["B-", "I-"]] + ["O"]
dep_spacy_tag_list = list(nlp.get_pipe("parser").labels)

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model_checkpoint = "ehsanaghaei/SecureBERT"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)

# model_nugget = NuggetModel(num_classes = 11)
# model_nugget.load_state_dict(torch.load(f"{os.path.dirname(os.path.abspath(__file__))}/nugget_model_state_dict.pth", map_location=device))  
# model_nugget.eval()

"""
Function: create_dataloader(text_input)
Description: This function prepares a DataLoader for processing text input, including tokenization and alignment of labels.
Inputs:
    - text_input: The input text to be processed.
Output:
    - dataloader: A DataLoader for the tokenized and batched text data.
    - tokenized_dataset_ner: The tokenized dataset used for training.
"""
def create_dataloader(text_input):

    doc = nlp(text_input)

    content_as_words_emdash = [tok.text for tok in doc]
    content_as_words_emdash = [word.replace("``", '"').replace("''", '"').replace("$", "") for word in content_as_words_emdash]
    content_idx_dict = get_idxs_from_text(text_input, content_as_words_emdash)

    data = []

    words = []

    pos_spacy = [tok.pos_ for tok in doc]
    ner_spacy = [ent.ent_iob_ + "-" + ent.ent_type_ if ent.ent_iob_ != "O" else ent.ent_iob_ for ent in doc]
    dep_spacy = [tok.dep_ for tok in doc]
    depth_spacy = [find_dep_depth(tok) for tok in doc]

    for content_dict in content_idx_dict:
        start_idx, end_idx = content_dict["start_idx"], content_dict["end_idx"]
        words.append(content_dict["word"])


    content_token_len = len(tokenizer(words, truncation=False, is_split_into_words=True)["input_ids"])
    if content_token_len > tokenizer.model_max_length:
        no_split = (content_token_len // tokenizer.model_max_length) + 2
        split_len = (len(words) // no_split) + 1

        last_id = 0
        threshold = split_len

        for id, token in enumerate(words):
            if token == "." and id > threshold:
                data.append(
                    {
                        "tokens" : words[last_id : id + 1],
                        "pos_spacy" : pos_spacy[last_id : id + 1],
                        "ner_spacy" : ner_spacy[last_id : id + 1],
                        "dep_spacy" : dep_spacy[last_id : id + 1],
                        "depth_spacy" : depth_spacy[last_id : id + 1],
                    }
                )
                last_id = id + 1
                threshold += split_len
        data.append({"tokens" : words[last_id : ],
                     "pos_spacy" : pos_spacy[last_id : ],
                     "ner_spacy" : ner_spacy[last_id : ],
                     "dep_spacy" : dep_spacy[last_id : ],
                     "depth_spacy" : depth_spacy[last_id : ]}) 
    else:
        data.append(
            {
                "tokens" : words,
                "pos_spacy" : pos_spacy,
                "ner_spacy" : ner_spacy,
                "dep_spacy" : dep_spacy,
                "depth_spacy" : depth_spacy
            }
        )


    ner_features = Features({'tokens' : Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),
                            'pos_spacy' : Sequence(feature=ClassLabel(num_classes=len(pos_spacy_tag_list), names=pos_spacy_tag_list, names_file=None, id=None), length=-1, id=None),
                            'ner_spacy' : Sequence(feature=ClassLabel(num_classes=len(ner_spacy_tag_list), names=ner_spacy_tag_list, names_file=None, id=None), length=-1, id=None),
                            'dep_spacy' : Sequence(feature=ClassLabel(num_classes=len(dep_spacy_tag_list), names=dep_spacy_tag_list, names_file=None, id=None), length=-1, id=None),
                            'depth_spacy' : Sequence(feature=ClassLabel(num_classes=17, names= list(range(17)), names_file=None, id=None), length=-1, id=None)
                            })

    dataset = Dataset.from_list(data, features=ner_features)
    tokenized_dataset_ner = dataset.map(tokenize_and_align_labels_with_pos_ner_dep, fn_kwargs={'tokenizer' : tokenizer}, batched=True, load_from_cache_file=False)
    tokenized_dataset_ner = tokenized_dataset_ner.with_format("torch")

    tokenized_dataset_ner = tokenized_dataset_ner.remove_columns("tokens")

    batch_size = 4 # Number of input texts
    dataloader = DataLoader(tokenized_dataset_ner, batch_size=batch_size)
    # TODO : context_idx_dict should be used to index the words
    return dataloader, tokenized_dataset_ner

"""
Function: predict(dataloader)
Description: This function performs inference on a given DataLoader using a trained model and returns the predicted labels.
Inputs:
    - dataloader: A DataLoader containing input data for prediction.
Output:
    - predicted_label: A tensor containing the predicted labels for the input data.
"""
def predict(model_nugget, dataloader):
    predicted_label = []
    for batch in dataloader:
        with torch.no_grad():
            logits = model_nugget(**batch)
        batch_predicted_label = logits.argmax(-1)
        predicted_label.append(batch_predicted_label)
    return torch.cat(predicted_label, dim=-1)

"""
Function: show_annotations(text_input)
Description: This function displays annotated event nuggets in the provided input text using the Streamlit library.
Inputs:
    - text_input: The input text containing event nuggets to be annotated and displayed.
Output:
    - An interactive display of annotated event nuggets within the input text.
"""
def show_annotations(text_input):
    st.title("Event Nuggets")

    dataloader, tokenized_dataset_ner = create_dataloader(text_input)
    predicted_label = predict(dataloader)

    for idx, labels in enumerate(predicted_label):
        token_mask = [token > 2 for token in tokenized_dataset_ner[idx]["input_ids"]]

        tokens = tokenizer.convert_ids_to_tokens(tokenized_dataset_ner[idx]["input_ids"][token_mask], skip_special_tokens=True)
        tokens = [token.replace("Ġ", "").replace("Ċ", "").replace("âĢĻ", "'") for token in tokens]

        text = tokenizer.decode(tokenized_dataset_ner[idx]["input_ids"][token_mask])
        idxs = get_idxs_from_text(text, tokens)

        labels = labels[token_mask]

        annotated_text_list = []
        last_label = ""
        cumulative_tokens = "" 
        last_id = 0

        for idx, label in zip(idxs, labels):
            to_label = event_nugget_list[label]
            label_short = to_label.split("-")[1] if "-" in to_label else to_label
            if last_label == label_short:
                cumulative_tokens += text[last_id : idx["end_idx"]]
                last_id = idx["end_idx"]
            else:
                if last_label != "":
                    if last_label == "O":
                        annotated_text_list.append(cumulative_tokens)
                    else:
                        annotated_text_list.append((cumulative_tokens, last_label))
                last_label = label_short
                cumulative_tokens = idx["word"]
                last_id = idx["end_idx"]
        if last_label == "O":
            annotated_text_list.append(cumulative_tokens)
        else:  
            annotated_text_list.append((cumulative_tokens, last_label))
        annotated_text(annotated_text_list)

"""
Function: get_event_nuggets(text_input)
Description: This function extracts predicted event nuggets (event entities) from the provided input text.
Inputs:
    - text_input: The input text containing event nuggets to be extracted.
Output:
    - predicted_event_nuggets: A list of dictionaries, each representing an extracted event nugget with start and end offsets,
      subtype, and text content.
"""
def get_event_nuggets(model_nugget, text_input):
    dataloader, tokenized_dataset_ner = create_dataloader(text_input)
    predicted_label = predict(model_nugget, dataloader)

    predicted_event_nuggets = []
    text_length = 0 
    for idx, labels in enumerate(predicted_label):
        token_mask = [token > 2 for token in tokenized_dataset_ner[idx]["input_ids"]]

        tokens = tokenizer.convert_ids_to_tokens(tokenized_dataset_ner[idx]["input_ids"][token_mask], skip_special_tokens=True)
        tokens = [token.replace("Ġ", "").replace("Ċ", "").replace("âĢĻ", "'") for token in tokens]

        text = tokenizer.decode(tokenized_dataset_ner[idx]["input_ids"][token_mask])
        idxs = get_idxs_from_text(text_input[text_length : ], tokens)
        
        labels = labels[token_mask]

        start_idx = 0
        end_idx = 0
        last_label = ""

        for idx, label in zip(idxs, labels):
            to_label = event_nugget_list[label]
            label_short = to_label.split("-")[1] if "-" in to_label else to_label
            
            if label_short == last_label:
                end_idx = idx["end_idx"]
            else:
                if text_input[start_idx : end_idx] != "" and last_label != "O":
                    predicted_event_nuggets.append(
                        {
                            "startOffset" : text_length + start_idx,
                            "endOffset" : text_length + end_idx,
                            "subtype" : last_label,
                            "text" : text_input[text_length + start_idx : text_length + end_idx]
                        }
                    )
                start_idx = idx["start_idx"]
                end_idx = idx["start_idx"] + len(idx["word"])
            last_label = label_short
        
        text_length += idx["end_idx"]
    return predicted_event_nuggets