import gradio as gr

from transformers import pipeline

DEFAULT_MODEL = "ginic/data_seed_4_wav2vec2-large-xlsr-buckeye-ipa"

VALID_MODELS = [
    "ctaguchi/wav2vec2-large-xlsr-japlmthufielta-ipa-plus-2000",
    "ginic/hyperparam_tuning_1_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/data_seed_1_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/data_seed_2_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/data_seed_3_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/data_seed_4_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_30_female_1_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_30_female_2_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_30_female_3_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_30_female_4_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_30_female_5_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_70_female_1_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_70_female_2_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_70_female_3_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_70_female_4_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/gender_split_70_female_5_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/vary_individuals_old_only_1_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/vary_individuals_old_only_2_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/vary_individuals_old_only_3_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/vary_individuals_young_only_1_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/vary_individuals_young_only_2_wav2vec2-large-xlsr-buckeye-ipa",
    "ginic/vary_individuals_young_only_3_wav2vec2-large-xlsr-buckeye-ipa"
]

def load_model_and_predict(model_name, audio_in, model_state):
    if model_state["model_name"] != model_name:
        model_state = {"loaded_model":pipeline(task="automatic-speech-recognition", model=model_name), 
                       "model_name": model_name}
    
    return model_state["loaded_model"](audio_in)["text"], model_state

def launch_demo():
    initial_model = {"loaded_model":pipeline(task="automatic-speech-recognition", model=DEFAULT_MODEL), 
                    "model_name": DEFAULT_MODEL}
    demo= gr.Interface(
        fn=load_model_and_predict, 
        inputs= [
            gr.Dropdown(VALID_MODELS, value=DEFAULT_MODEL, label="IPA transcription ASR model", info="Select the model to use for prediction."),
            gr.Audio(type="filepath"),
            gr.State(value=initial_model) #Store the name of the currently loaded model
        ],  
        outputs=[gr.Textbox(label="Predicted IPA transcription"), gr.State()],
        allow_flagging="never",
        title="Automatic International Phonetic Alphabet Transcription",
        description="This demo allows you to experiment with producing phonetic transcriptions of uploaded or recorded audio using a selected automatic speech recognition (ASR) model."
    )

    demo.launch()

if __name__ == "__main__":
    launch_demo()