import gradio as gr

import os
import torch
import numpy as np
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
from huggingface_hub import HfApi

from label_dicts import MANIFESTO_LABEL_NAMES, MANIFESTO_NUM_DICT

from .utils import is_disk_full, release_model

HF_TOKEN = os.environ["hf_read"]

languages = [
    "Armenian", "Bulgarian", "Croatian", "Czech", "Danish", "Dutch", "English",
    "Estonian", "Finnish", "French", "Georgian", "German", "Greek", "Hebrew",
    "Hungarian", "Icelandic", "Italian", "Japanese", "Korean", "Latvian",
    "Lithuanian", "Norwegian", "Polish", "Portuguese", "Romanian", "Russian",
    "Serbian", "Slovak", "Slovenian", "Spanish", "Swedish", "Turkish"
]

def build_huggingface_path(language: str):
    return "poltextlab/xlm-roberta-large-manifesto"

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"[{MANIFESTO_NUM_DICT[i]}] {MANIFESTO_LABEL_NAMES[int(MANIFESTO_NUM_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_cap(text, language):
    model_id = build_huggingface_path(language)
    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="Manifesto Babel Demo",
    fn=predict_cap,
    inputs=[gr.Textbox(lines=6, label="Input"),
            gr.Dropdown(languages, label="Language", value=languages[6])],
    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])