vickeee465
max_len + low_memory + device_map
84c21a9
raw
history blame
4.36 kB
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()])