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import gradio as gr
import json
import numpy as np
import torch
import transformers
import tokenizers
from model import BertAD
DICTIONARY = json.load(open('model/dict.json'))
TOKENIZER = tokenizers.BertWordPieceTokenizer(f"model/vocab.txt", lowercase=True)
MAX_LEN = 256
MODEL = BertAD()
vec = MODEL.state_dict()['bert.embeddings.position_ids']
chkp = torch.load(os.path.join('model', 'model_0.bin'), map_location='cpu')
chkp['bert.embeddings.position_ids'] =vec
MODEL.load_state_dict(chkp)
def sample_text(text, acronym, max_len):
text = text.split()
idx = text.index(acronym)
left_idx = max(0, idx - max_len//2)
right_idx = min(len(text), idx + max_len//2)
sampled_text = text[left_idx:right_idx]
return ' '.join(sampled_text)
def process_data(text, acronym, expansion, tokenizer, max_len):
text = str(text)
expansion = str(expansion)
acronym = str(acronym)
n_tokens = len(text.split())
if n_tokens>120:
text = sample_text(text, acronym, 120)
answers = acronym + ' ' + ' '.join(DICTIONARY[acronym])
start = answers.find(expansion)
end = start + len(expansion)
char_mask = [0]*len(answers)
for i in range(start, end):
char_mask[i] = 1
tok_answer = tokenizer.encode(answers)
answer_ids = tok_answer.ids
answer_offsets = tok_answer.offsets
answer_ids = answer_ids[1:-1]
answer_offsets = answer_offsets[1:-1]
target_idx = []
for i, (off1, off2) in enumerate(answer_offsets):
if sum(char_mask[off1:off2])>0:
target_idx.append(i)
start = target_idx[0]
end = target_idx[-1]
text_ids = tokenizer.encode(text).ids[1:-1]
token_ids = [101] + answer_ids + [102] + text_ids + [102]
offsets = [(0,0)] + answer_offsets + [(0,0)]*(len(text_ids) + 2)
mask = [1] * len(token_ids)
token_type = [0]*(len(answer_ids) + 1) + [1]*(2+len(text_ids))
text = answers + text
start = start + 1
end = end + 1
padding = max_len - len(token_ids)
if padding>=0:
token_ids = token_ids + ([0] * padding)
token_type = token_type + [1] * padding
mask = mask + ([0] * padding)
offsets = offsets + ([(0, 0)] * padding)
else:
token_ids = token_ids[0:max_len]
token_type = token_type[0:max_len]
mask = mask[0:max_len]
offsets = offsets[0:max_len]
assert len(token_ids)==max_len
assert len(mask)==max_len
assert len(offsets)==max_len
assert len(token_type)==max_len
return {
'ids': token_ids,
'mask': mask,
'token_type': token_type,
'offset': offsets,
'start': start,
'end': end,
'text': text,
'expansion': expansion,
'acronym': acronym,
}
def jaccard(str1, str2):
a = set(str1.lower().split())
b = set(str2.lower().split())
c = a.intersection(b)
return float(len(c)) / (len(a) + len(b) - len(c))
def evaluate_jaccard(text, selected_text, acronym, offsets, idx_start, idx_end):
filtered_output = ""
for ix in range(idx_start, idx_end + 1):
filtered_output += text[offsets[ix][0]: offsets[ix][1]]
if (ix+1) < len(offsets) and offsets[ix][1] < offsets[ix+1][0]:
filtered_output += " "
candidates = DICTIONARY[acronym]
candidate_jaccards = [jaccard(w.strip(), filtered_output.strip()) for w in candidates]
idx = np.argmax(candidate_jaccards)
return candidate_jaccards[idx], candidates[idx]
def disambiguate(text, acronym):
inputs = process_data(text, acronym, acronym, TOKENIZER, MAX_LEN)
ids = torch.tensor(input['ids']).view(1, -1)
mask = torch.tensor(inputs['mask']).view(1, -1)
token_type = torch.tensor(inputs['token_type']).view(1, -1)
offsets = inputs['offset']
expansion = inputs['expnsion']
acronym = inputs['acronym']
start_logits, end_logits = MODEL(ids, mask, token_type)
start_prob = torch.softmax(start_logits, axis=-1).detach().numpy()
end_prob = torch.softmax(end_logits, axis=-1).detach().numpy()
start_idx = np.argmax(start_prob[0,:])
end_idx = np.argmax(end_prob[0,:])
js, exp = evaluate_jaccard(text, expansion[0], acronym[0], offsets[0], start_idx, end_idx)
return exp
text = gr.inputs.Textbox(lines=5, label="Context", placeholder="Type a sentence or paragraph here."),
acronym = gr.inputs.Textbox(lines=2, label="Question", placeholder="Type acronym")
expansion = gr.outputs.Textbox(label="Answer")
iface = gr.Interface(fn=disambiguate, inputs=[text, acronym], outputs=expansion)
iface.launch() |