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
import torch.nn.functional as F
from huggingface_hub import HfApi
from collections import defaultdict

from label_dicts import (
    CAP_NUM_DICT,
    CAP_LABEL_NAMES,
    CAP_MIN_NUM_DICT,
    CAP_MIN_LABEL_NAMES,
)

from .utils import is_disk_full, release_model


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",
}


CAP_MAJOR_CODES = list(CAP_NUM_DICT.values())
CAP_MIN_CODES = list(CAP_MIN_NUM_DICT.values())

major_index_to_id = {i: code for i, code in enumerate(CAP_MAJOR_CODES)}
minor_id_to_index = {code: i for i, code in enumerate(CAP_MIN_CODES)}
minor_index_to_id = {i: code for i, code in enumerate(CAP_MIN_CODES)}

major_to_minor_map = defaultdict(list)
for code in CAP_MIN_CODES:
    major_id = int(str(code)[:-2])
    major_to_minor_map[major_id].append(code)
major_to_minor_map = dict(major_to_minor_map)


def normalize_probs(probs: dict) -> dict:
    total = sum(probs.values())
    return {k: v / total for k, v in probs.items()}


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, hierarchical: bool):
    if hierarchical:
        major = "poltextlab/xlm-roberta-large-pooled-cap-v3"
        minor = (
            "poltextlab/xlm-roberta-large-twitter-cap-minor"
            if domain == "social"
            else "poltextlab/xlm-roberta-large-pooled-cap-minor-v3"
        )
        return major, minor

    return (
        "poltextlab/xlm-roberta-large-twitter-cap-minor"
        if domain == "social"
        else "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=64, truncation=True, padding=True, 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
    if int(top_major_id) == 999:
        filtered_probs = {21:1}
    else:
        filtered_probs = {
            i: float(major_probs_np[i]) for i in np.argsort(major_probs_np)[::-1]
        }
    filtered_probs = normalize_probs(filtered_probs)

    output_pred = {
        f"[{major_index_to_id[k]}] {CAP_LABEL_NAMES[major_index_to_id[k]]}": v
        for k, v in sorted(
            filtered_probs.items(), key=lambda item: item[1], reverse=True
        )
    }
    
    print(top_major_id) # debug
    print(major_to_minor_map) # debug

    # 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)

        release_model(major_model, major_model_id)
        release_model(minor_model, minor_model_id)

        print(minor_probs)  # debug
        # 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
        }
        print(filtered_probs)  # debug
        filtered_probs = normalize_probs(filtered_probs)
        print(filtered_probs)  # debug

        output_pred = {
            f"[{top_major_id}] {CAP_LABEL_NAMES[top_major_id]} [{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>'

    interpretation_info = """
    ## How to Interpret These Values (Hierarchical Classification)

    The values are the confidences for minor topics **within a given major topic**, and they are **normalized to sum to 1**.
    """

    return interpretation_info, output_pred, output_info


def predict_flat(text, model_id, tokenizer_id, HF_TOKEN=None):
    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=64, truncation=True, padding=True, 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()
    top_indices = np.argsort(probs)[::-1][:10]

    output_pred = {}
    for i in top_indices:
        code = CAP_MIN_NUM_DICT[i]
        prob = probs[i]

        if code in CAP_LABEL_NAMES:
            # Media (major) topic
            label = CAP_LABEL_NAMES[code]
            display = f"[{code}] {label}"
        else:
            # Minor topic
            major_code = code // 100
            major_label = CAP_LABEL_NAMES[major_code]
            minor_label = CAP_MIN_LABEL_NAMES[code]
            display = f"[{major_code}] {major_label} [{code}] {minor_label}"

        output_pred[display] = prob

    interpretation_info = """
    ## How to Interpret These Values (Flat Classification)

    This method returns predictions made by a single model. **Only the top 10 most confident labels are displayed**.
    """

    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 interpretation_info, output_pred, output_info


def predict_cap(tmp, method, text, language, domain):
    if is_disk_full():
        os.system("rm -rf /data/models*")
        os.system("rm -r ~/.cache/huggingface/hub")

    domain = domains[domain]

    if method == "Hierarchical Classification":
        major_model_id, minor_model_id = build_huggingface_path(language, domain, True)
        tokenizer_id = "xlm-roberta-large"
        return predict(text, major_model_id, minor_model_id, tokenizer_id)

    else:
        model_id = build_huggingface_path(language, domain, False)
        tokenizer_id = "xlm-roberta-large"
        return predict_flat(text, model_id, tokenizer_id)


description = """
You can choose between two approaches for making predictions:

**1. Hierarchical Classification**  
First, the model predicts a **major topic**. Then, a second model selects the most probable **subtopic** from within that major topic's category.

**2. Flat Classification (single model)**  
A single model directly predicts the most relevant label from all available minor topics.
"""

demo = gr.Interface(
    title="CAP Minor Topics Babel Demo",
    fn=predict_cap,
    inputs=[
        gr.Markdown(description),
        gr.Radio(
            choices=["Hierarchical Classification", "Flat Classification"],
            label="Prediction Mode",
            value="Hierarchical Classification",
        ),
        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.Markdown(), gr.Label(label="Output"), gr.Markdown()],
)