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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 label_dicts import ILLFRAMES_MIGRATION_LABEL_NAMES, ILLFRAMES_COVID_LABEL_NAMES
HF_TOKEN = os.environ["hf_read"]
languages = [
"English"
]
domains = {
"Covid": "covid",
"Migration": "migration"
}
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:
return False
def build_huggingface_path(domain: str):
return f"poltextlab/xlm-roberta-large-english-ILLFRAMES-{domain}"
def predict(text, model_id, tokenizer_id, label_names):
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()
NUMS_DICT = {i: key for i, key in enumerate(sorted(label_names.keys()))}
output_pred = {f"[{NUMS_DICT[i]}] {label_names[NUMS_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_illframes(text, domain):
domain = domains[domain]
model_id = build_huggingface_path(domain)
tokenizer_id = "xlm-roberta-large"
if domain == "migration":
label_names = ILLFRAMES_MIGRATION_LABEL_NAMES
else:
label_names = ILLFRAMES_COVID_LABEL_NAMES
return predict(text, model_id, tokenizer_id, label_names)
demo = gr.Interface(
fn=predict_illframes,
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()]) |