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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 = [
"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'<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()]) |