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, 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 = {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'

Prediction was made using the {model_id} model.

' 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()])