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| 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 = [ | |
| "Danish", | |
| "Dutch", | |
| "English", | |
| "French", | |
| "German", | |
| "Hungarian", | |
| "Italian", | |
| "Polish", | |
| "Portuguese", | |
| "Spanish", | |
| "Czech", | |
| "Slovak", | |
| "Norwegian" | |
| ] | |
| 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 value and value in path_map: | |
| model_path = path_map[value] | |
| if check_huggingface_path(model_path): | |
| # if the model is available on Huggingface, return the path | |
| return model_path | |
| else: | |
| # if the model is not available on Huggingface, look for other models | |
| filtered_path_map = {k: v for k, v in path_map.items() if k != value} | |
| for k, v in filtered_path_map.items(): | |
| if check_huggingface_path(v): | |
| return v | |
| elif check_huggingface_path(lang_domain_path): | |
| return lang_domain_path | |
| elif check_huggingface_path(lang_path): | |
| return lang_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", 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'<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, 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()]) |