interfaces/ontolisst.py

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(
    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()])