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_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",
}


def get_label_name(idx):
    minor_code = CAP_MIN_NUM_DICT[idx]
    minor_label_name = CAP_MIN_LABEL_NAMES[minor_code]
    major_code = minor_code // 100 if minor_code not in [99, 999, 9999] else 999
    major_label_name = CAP_LABEL_NAMES[major_code]
    return f"[{major_code}] {major_label_name} [{minor_code}] {minor_label_name}"


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):
    if domain in ["social"]:
        return "poltextlab/xlm-roberta-large-twitter-cap-minor"
    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=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()

    output_pred = {get_label_name(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"

    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.Label(num_top_classes=5, label="Output"), gr.Markdown()],
)