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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
HF_TOKEN = os.environ["hf_read"]
languages = [
"English"
]
from label_dicts import ONTOLISST_LABEL_NAMES
# --- DEBUG ---
import shutil
def convert_size(size):
for unit in ['B', 'KB', 'MB', 'GB', 'TB', 'PB']:
if size < 1024:
return f"{size:.2f} {unit}"
size /= 1024
def get_disk_space(path="/"):
total, used, free = shutil.disk_usage(path)
return {
"Total": convert_size(total),
"Used": convert_size(used),
"Free": convert_size(free)
}
# ---
def build_huggingface_path(language: str):
return "poltextlab/xlm-roberta-large_ontolisst_v1"
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)
# --- DEBUG ---
disk_space = get_disk_space('/data/')
print("Disk Space Info:")
for key, value in disk_space.items():
print(f"{key}: {value}")
# ---
model.to(device)
inputs = tokenizer(text,
max_length=256,
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()
predicted_class_id = probs.argmax()
predicted_class_id = {4: 2, 5: 1}.get(predicted_class_id, 0)
output_pred = ONTOLISST_LABEL_NAMES.get(predicted_class_id, predicted_class_id)
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(
title="ONTOLISST Babel Demo",
fn=predict_cap,
inputs=[gr.Textbox(lines=6, label="Input"),
gr.Dropdown(languages, label="Language")],
outputs=[gr.Label(num_top_classes=3, label="Output"), gr.Markdown()])
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