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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
from label_dicts import MANIFESTO_LABEL_NAMES
HF_TOKEN = os.environ["hf_read"]
languages = [
"Armenian", "Bulgarian", "Croatian", "Czech", "Danish", "Dutch", "English",
"Estonian", "Finnish", "French", "Georgian", "German", "Greek", "Hebrew",
"Hungarian", "Icelandic", "Italian", "Japanese", "Korean", "Latvian",
"Lithuanian", "Norwegian", "Polish", "Portuguese", "Romanian", "Russian",
"Serbian", "Slovak", "Slovenian", "Spanish", "Swedish", "Turkish"
]
def build_huggingface_path(language: str):
return "poltextlab/xlm-roberta-large-manifesto"
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)
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()
output_pred = {f"[{model.config.id2label[i]}] {MANIFESTO_LABEL_NAMES[int(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()]) |