vickeee465
max_len + low_memory + device_map
84c21a9
raw
history blame
1.81 kB
import gradio as gr
import os
import torch
import numpy as np
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
from huggingface_hub import HfApi
from label_dicts import MANIFESTO_LABEL_NAMES
HF_TOKEN = os.environ["hf_read"]
languages = [
"Czech", "English", "French", "German", "Hungarian", "Italian"
]
def build_huggingface_path(language: str):
return "poltextlab/xlm-roberta-large-pooled-emotions"
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", 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_cap(text, language):
model_id = build_huggingface_path(language)
tokenizer_id = "xlm-roberta-large"
return predict(text, model_id, tokenizer_id)
demo = gr.Interface(
fn=predict_cap,
inputs=[gr.Textbox(lines=6, label="Input"),
gr.Dropdown(languages, label="Language")],
outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])