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import jdk
jdk.install('11', jre=True)
import numpy as np
import gradio as gr
from imports import *
from huggingface_hub import login
login(token="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")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dict_ = {
0: "negative",
1: "positive",
2: "neutral"}
tokenizer_sent = AutoTokenizer.from_pretrained("nam194/sentiment", use_fast=False)
model_sent = AutoModelForSequenceClassification.from_pretrained("nam194/sentiment", num_labels=3, use_auth_token=True).to(device)
def cvt2cls(data):
data = list(set(data))
try:
data.remove(20)
except:
pass
for i, num in enumerate(data):
if num == 20:
continue
if num>=10:
data[i] -= 10
return data
ner_tags = {0: 'B-chỗ để xe', 1: 'B-con người', 2: 'B-công việc', 3: 'B-cơ sở vật chất', 4: 'B-dự án', 5: 'B-lương', 6: 'B-môi trường làm việc', 7: 'B-ot/thời gian', 8: 'B-văn phòng', 9: 'B-đãi ngộ', 10: 'I-chỗ để xe', 11: 'I-con người', 12: 'I-công việc', 13: 'I-cơ sở vật chất', 14: 'I-dự án', 15: 'I-lương', 16: 'I-môi trường làm việc', 17: 'I-ot/thời gian', 18: 'I-văn phòng', 19: 'I-đãi ngộ', 20: 'O'}
topic_tags = {0: 'chỗ để xe', 1: 'con người', 2: 'công việc', 3: 'cơ sở vật chất', 4: 'dự án', 5: 'lương', 6: 'môi trường làm việc', 7: 'ot/thời gian', 8: 'văn phòng', 9: 'đãi ngộ'}
config = RobertaConfig.from_pretrained("nam194/ner", num_labels=20)
tokenizer_topic = AutoTokenizer.from_pretrained("nam194/ner", use_fast=False)
model_topic = PhoBertLstmCrf.from_pretrained("nam194/ner", config=config, from_tf=False).to(device)
model_topic.resize_token_embeddings(len(tokenizer_topic))
def sentiment(sent: str):
try:
sent_ = normalize(text=sent_) # segment input sentence, maybe raise ConnectionError: HTTPConnectionPool())
except:
pass
input_sent = torch.tensor([tokenizer_sent.encode(sent_)]).to(device)
with torch.no_grad():
out_sent = model_sent(input_sent)
logits_sent = out_sent.logits.softmax(dim=-1).tolist()[0]
pred_sent = dict_[np.argmax(logits_sent)]
try:
sent = replace_all(text=sent) # segment input sentence, maybe raise ConnectionError: HTTPConnectionPool())
except:
pass
sent_segment = rdrsegmenter.tokenize(sent)
dump = [[i, 'O'] for s in sent_segment for i in s]
dump_set = NerDataset(feature_for_phobert([dump], tokenizer=tokenizer_topic, use_crf=True))
dump_iter = DataLoader(dump_set, batch_size=1)
with torch.no_grad():
for idx, batch in enumerate(dump_iter):
batch = { k:v.to(device) for k, v in batch.items() }
outputs = model_topic(**batch)
pred_topic = list(set([topic_tags[i] for i in cvt2cls(outputs["tags"][0])]))
return {"sentiment": pred_sent, "topic": pred_topic}
def flip_image(x):
return np.fliplr(x)
with gr.Blocks() as demo:
gr.Markdown("Demo projects Review Company and Resume parser phase 1.")
with gr.Tab("Review Company"):
text_input = gr.Textbox()
text_output = gr.Textbox()
text_button = gr.Button("Predict")
with gr.Tab("Extract infomation from resume"):
with gr.Row():
image_input = gr.Image()
image_output = gr.Image()
image_button = gr.Button("Predict")
# with gr.Accordion("Open for More!"):
# gr.Markdown("Look at me...")
text_button.click(sentiment, inputs=text_input, outputs=text_output)
image_button.click(flip_image, inputs=image_input, outputs=image_output)
demo.launch()