import time
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

class RuntimeMeasure:
    def __init__(self, msg):
        self.msg = msg

    def __enter__(self):
        self.start_time = time.time()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        end_time = time.time()
        runtime = end_time - self.start_time
        gr.Info(f"{self.msg}: {runtime} seconds")
def m(msg):
    return RuntimeMeasure(msg)

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):
    gr.Info("\n".join(os.listdir("/data/")))

    device = torch.device("cpu")
    with m("Loading model"):
        model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
    with m("Loading tokenizer"):
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)

    with m("Tokenizing"):
        inputs = tokenizer(text,
                           max_length=256,
                           truncation=True,
                           padding="do_not_pad",
                           return_tensors="pt").to(device)
    with m("model.eval()"):
        model.eval()

    with m("Inference"):
        with torch.no_grad():
            logits = model(**inputs).logits

    with m("Softmax"):
        probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()

    with m("Output formatting"):
        output_pred = {f"[{model.config.id2label[i]}] {MANIFESTO_LABEL_NAMES[int(model.config.id2label[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):
    with m("WHOLE PROCESS"):
        model_id = build_huggingface_path(language)
        tokenizer_id = "xlm-roberta-large"
        prediction = predict(text, model_id, tokenizer_id)
    return prediction

demo = gr.Interface(
    fn=predict_cap,
    inputs=[gr.Textbox(lines=6, label="Input"),
            gr.Dropdown(languages, label="Language")],
    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])