Spaces:
Running
Running
| import time | |
| 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 | |
| class RuntimeMeasure: | |
| def __init__(self, msg): | |
| self.msg = msg | |
| def __enter__(self): | |
| self.start_time = time.time() | |
| return self | |
| def __exit__(self, exc_type, exc_value, traceback): | |
| end_time = time.time() | |
| runtime = end_time - self.start_time | |
| gr.Info(f"{self.msg}: {runtime} seconds") | |
| def m(msg): | |
| return RuntimeMeasure(msg) | |
| 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): | |
| gr.Info("\n".join(os.listdir("/data/"))) | |
| device = torch.device("cpu") | |
| with m("Loading model"): | |
| model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN) | |
| with m("Loading tokenizer"): | |
| tokenizer = AutoTokenizer.from_pretrained(tokenizer_id) | |
| with m("Tokenizing"): | |
| inputs = tokenizer(text, | |
| max_length=256, | |
| truncation=True, | |
| padding="do_not_pad", | |
| return_tensors="pt").to(device) | |
| with m("model.eval()"): | |
| model.eval() | |
| with m("Inference"): | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| with m("Softmax"): | |
| probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten() | |
| with m("Output formatting"): | |
| 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): | |
| with m("WHOLE PROCESS"): | |
| model_id = build_huggingface_path(language) | |
| tokenizer_id = "xlm-roberta-large" | |
| prediction = predict(text, model_id, tokenizer_id) | |
| return prediction | |
| 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()]) |