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import os
import torch
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"]
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):
device = torch.device("cpu")
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)
model.to(device)
inputs = tokenizer(text,
max_length=512,
truncation=True,
padding="do_not_pad",
return_tensors="pt").to(device)
model.eval()
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
output_pred = {model.config.id2label[i]: probs[i] for i in np.argsort(probs)[::-1]}
output_info = f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.</p>'
return output_pred, output_info
def predict_wrapper(text, language):
model_id = build_huggingface_path(language)
tokenizer_id = "xlm-roberta-large"
return predict(text, model_id, tokenizer_id)
with gr.Blocks() as demo:
gr.Interface(
fn=predict_wrapper,
inputs=[gr.Textbox(lines=6, label="Input"),
gr.Dropdown(LANGUAGES, label="Language")],
outputs=[gr.Label(num_top_classes=3, label="Output"), gr.Markdown()])
if __name__ == "__main__":
demo.launch()
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