Spaces:
Running
Running
| 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, MANIFESTO_NUM_DICT | |
| from .utils import is_disk_full, release_model | |
| 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") | |
| # Load JIT-traced model | |
| jit_model_path = f"/data/jit_models/{model_id.replace('/', '_')}.pt" | |
| model = torch.jit.load(jit_model_path).to(device) | |
| model.eval() | |
| # Load tokenizer (still regular HF) | |
| tokenizer = AutoTokenizer.from_pretrained(tokenizer_id) | |
| # Tokenize input | |
| inputs = tokenizer( | |
| text, | |
| max_length=256, | |
| truncation=True, | |
| padding="do_not_pad", | |
| return_tensors="pt" | |
| ) | |
| inputs = {k: v.to(device) for k, v in inputs.items()} | |
| with torch.no_grad(): | |
| output = model(inputs["input_ids"], inputs["attention_mask"]) | |
| print(output) # debug | |
| logits = output["logits"] | |
| release_model(model, model_id) | |
| probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten() | |
| output_pred = {f"[{model.config.id2label[i]}] {MANIFESTO_LABEL_NAMES[int(MANIFESTO_NUM_DICT[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" | |
| if is_disk_full(): | |
| os.system('rm -rf /data/models*') | |
| os.system('rm -r ~/.cache/huggingface/hub') | |
| return predict(text, model_id, tokenizer_id) | |
| demo = gr.Interface( | |
| title="Manifesto Babel Demo", | |
| fn=predict_cap, | |
| inputs=[gr.Textbox(lines=6, label="Input"), | |
| gr.Dropdown(languages, label="Language", value=languages[6])], | |
| outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()]) | |