import gradio as gr import os import torch import numpy as np import pandas as pd from transformers import AutoModelForSequenceClassification from transformers import AutoTokenizer from huggingface_hub import HfApi from huggingface_hub.utils._errors import RepositoryNotFoundError from label_dicts import CAP_NUM_DICT, CAP_LABEL_NAMES HF_TOKEN = os.environ["hf_read"] languages = [ "English", "Multilingual" ] domains = { "media": "media", "social media": "social", "parliamentary speech": "parlspeech", "legislative documents": "legislative", "executive speech": "execspeech", "executive order": "execorder", "party programs": "party", "judiciary": "judiciary", "budget": "budget", "public opinion": "publicopinion", "local government agenda": "localgovernment" } def check_huggingface_path(checkpoint_path: str): try: hf_api = HfApi(token=HF_TOKEN) hf_api.model_info(checkpoint_path, token=HF_TOKEN) return True except RepositoryNotFoundError: return False def build_huggingface_path(language: str, domain: str): language = language.lower() base_path = "xlm-roberta-large" lang_domain_path = f"poltextlab/{base_path}-{language}-{domain}-cap-v3" lang_path = f"poltextlab/{base_path}-{language}-cap-v3" path_map = { "L": lang_path, "L-D": lang_domain_path, "X": lang_domain_path, } value = None try: lang_domain_table = pd.read_csv("language_domain_models.csv") lang_domain_table["language"] = lang_domain_table["language"].str.lower() lang_domain_table.columns = lang_domain_table.columns.str.lower() # get the row for the language and them get the value from the domain column row = lang_domain_table[(lang_domain_table["language"] == language)] tmp = row.get(domain) if not tmp.empty: value = tmp.iloc[0] except (AttributeError, FileNotFoundError): value = None if language == 'english': model_path = lang_path else: model_path = "poltextlab/xlm-roberta-large-pooled-cap" if check_huggingface_path(model_path): return model_path else: return "poltextlab/xlm-roberta-large-pooled-cap" 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"[{CAP_NUM_DICT[i]}] {CAP_LABEL_NAMES[CAP_NUM_DICT[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, domain): domain = domains[domain] model_id = build_huggingface_path(language, domain) 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"), gr.Dropdown(domains.keys(), label="Domain")], outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])