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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 collections import defaultdict
from label_dicts import (CAP_MEDIA_NUM_DICT, CAP_MEDIA_LABEL_NAMES,
CAP_MIN_NUM_DICT, CAP_MIN_LABEL_NAMES)
from .utils import is_disk_full
HF_TOKEN = os.environ["hf_read"]
languages = [
"Multilingual",
]
domains = {
"media": "media"
}
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(language: str, domain: str):
return ("poltextlab/xlm-roberta-large-pooled-cap-media", "poltextlab/xlm-roberta-large-pooled-cap-minor-v3")
def predict(text, major_model_id, minor_model_id, tokenizer_id, HF_TOKEN=None):
device = torch.device("cpu")
# Load major and minor models + tokenizer
major_model = AutoModelForSequenceClassification.from_pretrained(
major_model_id,
low_cpu_mem_usage=True,
device_map="auto",
offload_folder="offload",
token=HF_TOKEN
).to(device)
minor_model = AutoModelForSequenceClassification.from_pretrained(
minor_model_id,
low_cpu_mem_usage=True,
device_map="auto",
offload_folder="offload",
token=HF_TOKEN
).to(device)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
# Tokenize input
inputs = tokenizer(text, max_length=256, truncation=True, padding="do_not_pad", return_tensors="pt").to(device)
# Predict major topic
major_model.eval()
with torch.no_grad():
major_logits = major_model(**inputs).logits
major_probs = F.softmax(major_logits, dim=-1)
major_probs_np = major_probs.cpu().numpy().flatten()
top_major_index = int(np.argmax(major_probs_np))
top_major_id = major_index_to_id[top_major_index]
# Default: show major topic predictions
output_pred = {
f"[{major_index_to_id[i]}] {CAP_MEDIA_LABEL_NAMES[major_index_to_id[i]]}": float(major_probs_np[i])
for i in np.argsort(major_probs_np)[::-1]
}
# If eligible for minor prediction
if top_major_id in major_to_minor_map:
valid_minor_ids = major_to_minor_map[top_major_id]
minor_model.eval()
with torch.no_grad():
minor_logits = minor_model(**inputs).logits
minor_probs = F.softmax(minor_logits, dim=-1)
# Restrict to valid minor codes
valid_indices = [minor_id_to_index[mid] for mid in valid_minor_ids if mid in minor_id_to_index]
filtered_probs = {minor_index_to_id[i]: float(minor_probs[0][i]) for i in valid_indices}
output_pred = {
f"[{k}] {CAP_MIN_LABEL_NAMES[k]}": v
for k, v in sorted(filtered_probs.items(), key=lambda item: item[1], reverse=True)
}
output_info = f'<p style="text-align: center; display: block">Prediction used <a href="https://huggingface.co/{major_model_id}">{major_model_id}</a> and <a href="https://huggingface.co/{minor_model_id}">{minor_model_id}</a>.</p>'
return output_pred, output_info
def predict_cap(text, language, domain):
domain = domains[domain]
major_model_id, minor_model_id = build_huggingface_path(language, domain)
tokenizer_id = "xlm-roberta-large"
if is_disk_full():
os.system('rm -rf /data/models*')
os.system('rm -r ~/.cache/huggingface/hub')
return predict(text, major_model_id, minor_model_id, tokenizer_id)
demo = gr.Interface(
title="CAP Media Topics Babel Demo",
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()])
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