interfaces/cap_minor.py

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 CAP_MIN_NUM_DICT, CAP_MIN_LABEL_NAMES, CAP_LABEL_NAMES 

from .utils import is_disk_full, release_model
from itertools import islice

def take(n, iterable):
    """Return the first n items of the iterable as a list."""
    return list(islice(iterable, n))

def score_to_color(prob):
    red = int(255 * (1 - prob))
    green = int(255 * prob)
    return f"rgb({red},{green},0)"

HF_TOKEN = os.environ["hf_read"]

languages = [
    "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 convert_minor_to_major(minor_topic):
    if minor_topic == 999:
        major_code = 999
    else:
        major_code = str(minor_topic)[:-2]

    
    label = CAP_LABEL_NAMES[int(major_code)]

    return label
        

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-minor-v3"

def predict(text, model_id, tokenizer_id):
    device = torch.device("cpu")

    # Load JIT-traced model
    jit_model_path = f"/data/jit_models/{model_id.replace('/', '_')}.pt"
    model = torch.jit.load(jit_model_path).to(device)
    model.eval()

    # Load tokenizer (still regular HF)
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)

    # Tokenize input
    inputs = tokenizer(
        text,
        max_length=256,
        truncation=True,
        padding="do_not_pad",
        return_tensors="pt"
    )
    inputs = {k: v.to(device) for k, v in inputs.items()}

    with torch.no_grad():
        output = model(inputs["input_ids"], inputs["attention_mask"])
        print(output) # debug
        logits = output["logits"]
        
    release_model(model, model_id)

    probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
    output_pred = {f"[{'999' if str(CAP_MIN_NUM_DICT[i]) == '999' else str(CAP_MIN_NUM_DICT[i])[:-2]}]{convert_minor_to_major(CAP_MIN_NUM_DICT[i])} [{CAP_MIN_NUM_DICT[i]}]{CAP_MIN_LABEL_NAMES[CAP_MIN_NUM_DICT[i]]}": probs[i] for i in np.argsort(probs)[::-1]}


    output_pred = dict(sorted(output_pred.items(), key=lambda item: item[1], reverse=True))
    first_n_items = take(5, output_pred.items())

    html = ""
    html += '<div style="background-color: white">'
    first = True
    for label, prob in first_n_items:
        bar_color = "#e0d890" if first else "#ccc"
        text_color = "black"
        bar_width = int(prob * 100)

        bar_color = score_to_color(prob)

        

        if first:
            html += f"""
            <div style="text-align: center; font-weight: bold; font-size: 27px; margin-bottom: 10px; margin-left: 10px; margin-right: 10px;">
                <span style="color: {text_color};">{label}</span>
            </div>"""

        html += f"""
        <div style="height: 4px; background-color: green; width: {bar_width}%; margin-bottom: 8px;"></div>
        <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 4px;">
            <span style="color: {text_color};">{label} — {int(prob * 100)}%</span>
        </div>
        """
        first = False
        
    
    html += '</div>'
    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 html, output_info

def predict_cap(text, language, domain):
    domain = domains[domain]
    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, model_id, tokenizer_id)

demo = gr.Interface(
    title="CAP Minor Topics Babel Demo",
    fn=predict_cap,
    inputs=[gr.Textbox(lines=6, label="Input"),
            gr.Dropdown(languages, label="Language", value=languages[0]),
            gr.Dropdown(domains.keys(), label="Domain", value=list(domains.keys())[0])],
    outputs=[gr.HTML(label="Output"), gr.Markdown()])