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import os
import torch
import spacy
import numpy as np
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import gradio as gr
PATH = '/data/' # at least 150GB storage needs to be attached
os.environ['TRANSFORMERS_CACHE'] = PATH
os.environ['HF_HOME'] = PATH
os.environ['HF_DATASETS_CACHE'] = PATH
os.environ['TORCH_HOME'] = PATH
HF_TOKEN = os.environ["hf_read"]
SENTIMENT_LABEL_NAMES = {0: "Negative", 1: "No sentiment or Neutral sentiment", 2: "Positive"}
LANGUAGES = ["Czech", "English", "French", "German", "Hungarian", "Polish", "Slovakian"]
id2label = {
0: "Anger",
1: "Fear",
2: "Disgust",
3: "Sadness",
4: "Joy",
5: "None of Them"
}
def load_spacy_model(model_name="xx_sent_ud_sm"):
try:
model = spacy.load(model_name)
except OSError:
spacy.cli.download(model_name)
model = spacy.load(model_name)
return model
def split_sentences(text, model):
# disable pipeline components not necessary for splitting
model.disable_pipes(model.pipe_names) # first disable all the pipes
model.enable_pipe("senter") # then enable the sentence splitter only
doc = model(text)
sentences = [sent.text for sent in doc.sents]
return sentences
def build_huggingface_path(language: str):
if language == "Czech" or language == "Slovakian":
return "visegradmedia-emotion/Emotion_RoBERTa_pooled_V4"
return "poltextlab/xlm-roberta-large-pooled-MORES"
def predict(text, model_id, tokenizer_id):
model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", offload_folder="offload", token=HF_TOKEN)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
inputs = tokenizer(text,
max_length=64,
truncation=True,
padding="do_not_pad",
return_tensors="pt")
model.eval()
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
return probs
def get_most_probable_label(probs):
label = id2label[probs.argmax()]
probability = f"{round(100 * probs.max(), 2)}%"
return [sentence, label, probability]
def predict_wrapper(text, language):
model_id = build_huggingface_path(language)
tokenizer_id = "xlm-roberta-large"
spacy_model = load_spacy_model()
sentences = split_sentences(text, spacy_model)
results = []
for sentence in sentences:
probs = predict(sentence, model_id, tokenizer_id)
results.append(get_most_probable_label(probs))
output_info = f'Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.'
return results, output_info
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_text = gr.Textbox(lines=6, label="Input", placeholder="Enter your text here...")
with gr.Column():
with gr.Row():
language_choice = gr.Dropdown(choices=LANGUAGES, label="Language", value="English")
with gr.Row():
predict_button = gr.Button("Submit")
with gr.Row():
result_table = gr.Dataframe(
headers=["Sentence", "Prediction", "Confidence"],
column_widths=["65%", "25%", "10%"],
wrap=True # important
)
with gr.Row():
model_info = gr.Markdown()
predict_button.click(
fn=predict_wrapper,
inputs=[input_text, language_choice],
outputs=[result_table, model_info]
)
if __name__ == "__main__":
demo.launch()
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