import gradio as gr
from app.original import *


def infer_tabs():
    with gr.TabItem(i18n("模型推理")):
            with gr.Row():
                sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
                with gr.Column():
                    refresh_button = gr.Button(
                        i18n("刷新音色列表和索引路径"), variant="primary"
                    )
                    clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
                spk_item = gr.Slider(
                    minimum=0,
                    maximum=2333,
                    step=1,
                    label=i18n("请选择说话人id"),
                    value=0,
                    visible=False,
                    interactive=True,
                )
                clean_button.click(
                    fn=clean, inputs=[], outputs=[sid0], api_name="infer_clean"
                )
            with gr.TabItem(i18n("单次推理")):
                with gr.Group():
                    with gr.Row():
                        with gr.Column():
                            vc_transform0 = gr.Number(
                                label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
                                value=0,
                            )
                            input_audio0 = gr.Textbox(
                                label=i18n(
                                    "输入待处理音频文件路径(默认是正确格式示例)"
                                ),
                                placeholder="C:\\Users\\Desktop\\audio_example.wav",
                            )
                            file_index1 = gr.Textbox(
                                label=i18n(
                                    "特征检索库文件路径,为空则使用下拉的选择结果"
                                ),
                                placeholder="C:\\Users\\Desktop\\model_example.index",
                                interactive=True,
                            )
                            file_index2 = gr.Dropdown(
                                label=i18n("自动检测index路径,下拉式选择(dropdown)"),
                                choices=sorted(index_paths),
                                interactive=True,
                            )
                            f0method0 = gr.Radio(
                                label=i18n(
                                    "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
                                ),
                                choices=(
                                    ["pm", "harvest", "crepe", "rmvpe"]
                                    if config.dml == False
                                    else ["pm", "harvest", "rmvpe"]
                                ),
                                value="rmvpe",
                                interactive=True,
                            )

                        with gr.Column():
                            resample_sr0 = gr.Slider(
                                minimum=0,
                                maximum=48000,
                                label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
                                value=0,
                                step=1,
                                interactive=True,
                            )
                            rms_mix_rate0 = gr.Slider(
                                minimum=0,
                                maximum=1,
                                label=i18n(
                                    "输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"
                                ),
                                value=0.25,
                                interactive=True,
                            )
                            protect0 = gr.Slider(
                                minimum=0,
                                maximum=0.5,
                                label=i18n(
                                    "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
                                ),
                                value=0.33,
                                step=0.01,
                                interactive=True,
                            )
                            filter_radius0 = gr.Slider(
                                minimum=0,
                                maximum=7,
                                label=i18n(
                                    ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"
                                ),
                                value=3,
                                step=1,
                                interactive=True,
                            )
                            index_rate1 = gr.Slider(
                                minimum=0,
                                maximum=1,
                                label=i18n("检索特征占比"),
                                value=0.75,
                                interactive=True,
                            )
                            f0_file = gr.File(
                                label=i18n(
                                    "F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"
                                ),
                                visible=False,
                            )

                            refresh_button.click(
                                fn=change_choices,
                                inputs=[],
                                outputs=[sid0, file_index2],
                                api_name="infer_refresh",
                            )
                            # file_big_npy1 = gr.Textbox(
                            #     label=i18n("特征文件路径"),
                            #     value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
                            #     interactive=True,
                            # )
                with gr.Group():
                    with gr.Column():
                        but0 = gr.Button(i18n("转换"), variant="primary")
                        with gr.Row():
                            vc_output1 = gr.Textbox(label=i18n("输出信息"))
                            vc_output2 = gr.Audio(
                                label=i18n("输出音频(右下角三个点,点了可以下载)")
                            )

                        but0.click(
                            vc.vc_single,
                            [
                                spk_item,
                                input_audio0,
                                vc_transform0,
                                f0_file,
                                f0method0,
                                file_index1,
                                file_index2,
                                # file_big_npy1,
                                index_rate1,
                                filter_radius0,
                                resample_sr0,
                                rms_mix_rate0,
                                protect0,
                            ],
                            [vc_output1, vc_output2],
                            api_name="infer_convert",
                        )
            with gr.TabItem(i18n("批量推理")):
                gr.Markdown(
                    value=i18n(
                        "批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. "
                    )
                )
                with gr.Row():
                    with gr.Column():
                        vc_transform1 = gr.Number(
                            label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"),
                            value=0,
                        )
                        opt_input = gr.Textbox(
                            label=i18n("指定输出文件夹"), value="opt"
                        )
                        file_index3 = gr.Textbox(
                            label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
                            value="",
                            interactive=True,
                        )
                        file_index4 = gr.Dropdown(
                            label=i18n("自动检测index路径,下拉式选择(dropdown)"),
                            choices=sorted(index_paths),
                            interactive=True,
                        )
                        f0method1 = gr.Radio(
                            label=i18n(
                                "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
                            ),
                            choices=(
                                ["pm", "harvest", "crepe", "rmvpe"]
                                if config.dml == False
                                else ["pm", "harvest", "rmvpe"]
                            ),
                            value="rmvpe",
                            interactive=True,
                        )
                        format1 = gr.Radio(
                            label=i18n("导出文件格式"),
                            choices=["wav", "flac", "mp3", "m4a"],
                            value="wav",
                            interactive=True,
                        )

                        refresh_button.click(
                            fn=lambda: change_choices()[1],
                            inputs=[],
                            outputs=file_index4,
                            api_name="infer_refresh_batch",
                        )
                        # file_big_npy2 = gr.Textbox(
                        #     label=i18n("特征文件路径"),
                        #     value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
                        #     interactive=True,
                        # )

                    with gr.Column():
                        resample_sr1 = gr.Slider(
                            minimum=0,
                            maximum=48000,
                            label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
                            value=0,
                            step=1,
                            interactive=True,
                        )
                        rms_mix_rate1 = gr.Slider(
                            minimum=0,
                            maximum=1,
                            label=i18n(
                                "输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"
                            ),
                            value=1,
                            interactive=True,
                        )
                        protect1 = gr.Slider(
                            minimum=0,
                            maximum=0.5,
                            label=i18n(
                                "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
                            ),
                            value=0.33,
                            step=0.01,
                            interactive=True,
                        )
                        filter_radius1 = gr.Slider(
                            minimum=0,
                            maximum=7,
                            label=i18n(
                                ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"
                            ),
                            value=3,
                            step=1,
                            interactive=True,
                        )
                        index_rate2 = gr.Slider(
                            minimum=0,
                            maximum=1,
                            label=i18n("检索特征占比"),
                            value=1,
                            interactive=True,
                        )
                with gr.Row():
                    dir_input = gr.Textbox(
                        label=i18n(
                            "输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"
                        ),
                        placeholder="C:\\Users\\Desktop\\input_vocal_dir",
                    )
                    inputs = gr.File(
                        file_count="multiple",
                        label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹"),
                    )

                with gr.Row():
                    but1 = gr.Button(i18n("转换"), variant="primary")
                    vc_output3 = gr.Textbox(label=i18n("输出信息"))

                    but1.click(
                        vc.vc_multi,
                        [
                            spk_item,
                            dir_input,
                            opt_input,
                            inputs,
                            vc_transform1,
                            f0method1,
                            file_index3,
                            file_index4,
                            # file_big_npy2,
                            index_rate2,
                            filter_radius1,
                            resample_sr1,
                            rms_mix_rate1,
                            protect1,
                            format1,
                        ],
                        [vc_output3],
                        api_name="infer_convert_batch",
                    )
                sid0.change(
                    fn=vc.get_vc,
                    inputs=[sid0, protect0, protect1],
                    outputs=[spk_item, protect0, protect1, file_index2, file_index4],
                    api_name="infer_change_voice",
                )