from pathlib import Path
import argparse
from functools import partial
import gradio as gr
import torch
from torchaudio.functional import resample

import utils.train_util as train_util


def load_model(cfg,
               ckpt_path,
               device):
    model = train_util.init_model_from_config(cfg["model"])
    ckpt = torch.load(ckpt_path, "cpu")
    train_util.load_pretrained_model(model, ckpt)
    model.eval()
    model = model.to(device)
    tokenizer = train_util.init_obj_from_dict(cfg["tokenizer"])
    if not tokenizer.loaded:
        tokenizer.load_state_dict(ckpt["tokenizer"])
    model.set_index(tokenizer.bos, tokenizer.eos, tokenizer.pad)
    return model, tokenizer


def infer(file, runner):
    sr, wav = file
    wav = torch.as_tensor(wav)
    if wav.dtype == torch.short:
        wav = wav / 2 ** 15
    elif wav.dtype == torch.int:
        wav = wav / 2 ** 31
    if wav.ndim > 1:
        wav = wav.mean(1)
    wav = resample(wav, sr, runner.target_sr)
    wav_len = len(wav)
    wav = wav.float().unsqueeze(0).to(runner.device)
    input_dict = {
        "mode": "inference",
        "wav": wav,
        "wav_len": [wav_len],
        "specaug": False,
        "sample_method": "beam",
        "beam_size": 3,
    }
    with torch.no_grad():
        output_dict = runner.model(input_dict)
        seq = output_dict["seq"].cpu().numpy()
        cap = runner.tokenizer.decode(seq)[0]
    return cap

# def input_toggle(input_type):
#     if input_type == "file":
#         return gr.update(visible=True), gr.update(visible=False)
#     elif input_type == "mic":
#         return gr.update(visible=False), gr.update(visible=True)

class InferRunner:

    def __init__(self, model_name):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        exp_dir = Path(f"./checkpoints/{model_name.lower()}")
        cfg = train_util.load_config(exp_dir / "config.yaml")
        self.model, self.tokenizer = load_model(cfg, exp_dir / "ckpt.pth", self.device)
        self.target_sr = cfg["target_sr"]
    
    def change_model(self, model_name):
        exp_dir = Path(f"./checkpoints/{model_name.lower()}")
        cfg = train_util.load_config(exp_dir / "config.yaml")
        self.model, self.tokenizer = load_model(cfg, exp_dir / "ckpt.pth", self.device)
        self.target_sr = cfg["target_sr"]


def change_model(radio):
    global infer_runner
    infer_runner.change_model(radio)


with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown("# Lightweight EfficientNetB2-Transformer Audio Captioning")

    with gr.Row():
        gr.Markdown("""
            [![arXiv](https://img.shields.io/badge/arXiv-2407.14329-brightgreen.svg?style=flat-square)](https://arxiv.org/abs/2407.14329)
            
            [![github](https://img.shields.io/badge/GitHub-Code-blue?logo=Github&style=flat-square)](https://github.com/wsntxxn/AudioCaption?tab=readme-ov-file#lightweight-effb2-transformer-model)
        """)
    with gr.Row():
        with gr.Column():
            radio = gr.Radio(
                ["AudioCaps", "Clotho"],
                value="AudioCaps",
                label="Select model"
            )
            infer_runner = InferRunner(radio.value)
            file = gr.Audio(label="Input", visible=True)
            radio.change(fn=change_model, inputs=[radio,],)
            btn = gr.Button("Run")
        with gr.Column():
            output = gr.Textbox(label="Output")
        btn.click(
            fn=partial(infer,
                       runner=infer_runner),
            inputs=[file,],
            outputs=output
        )
    
    demo.launch()