from collections import deque    
import streamlit as st
import torch 
from streamlit_player import st_player
from transformers import AutoModelForCTC, Wav2Vec2Processor
from streaming import ffmpeg_stream 

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
player_options = {
    "events": ["onProgress"],
    "progress_interval": 200,
    "volume": 1.0,
    "playing": True,
    "loop": False,
    "controls": False,
    "muted": False,
    "config": {"youtube": {"playerVars": {"start": 1}}},
}

st.title("YouTube Video Spanish ASR")
st.write("Acknowledgement: This demo is based on Anton Lozhkov's cool Space : https://huggingface.co/spaces/anton-l/youtube-subs-wav2vec")
# disable rapid fading in and out on `st.code` updates
st.markdown("<style>.element-container{opacity:1 !important}</style>", unsafe_allow_html=True)

@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None})
def load_model(model_path="facebook/wav2vec2-large-robust-ft-swbd-300h"):
    processor = Wav2Vec2Processor.from_pretrained(model_path)
    model = AutoModelForCTC.from_pretrained(model_path).to(device)
    return processor, model

model_path = st.radio(
    "Select a model", (
        "jonatasgrosman/wav2vec2-xls-r-1b-spanish", 
        "jonatasgrosman/wav2vec2-large-xlsr-53-spanish", 
        "patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm", 
        "facebook/wav2vec2-large-xlsr-53-spanish", 
        "glob-asr/xls-r-es-test-lm"
    )
)
   
processor, model = load_model(model_path)

def stream_text(url, chunk_duration_ms, pad_duration_ms):
    sampling_rate = processor.feature_extractor.sampling_rate

    # calculate the length of logits to cut from the sides of the output to account for input padding
    output_pad_len = model._get_feat_extract_output_lengths(int(sampling_rate * pad_duration_ms / 1000))

    # define the audio chunk generator
    stream = ffmpeg_stream(url, sampling_rate, chunk_duration_ms=chunk_duration_ms, pad_duration_ms=pad_duration_ms)

    leftover_text = ""
    for i, chunk in enumerate(stream):
        input_values = processor(chunk, sampling_rate=sampling_rate, return_tensors="pt").input_values

        with torch.no_grad():
            logits = model(input_values.to(device)).logits[0]
            if i > 0:
                logits = logits[output_pad_len : len(logits) - output_pad_len]
            else:  # don't count padding at the start of the clip
                logits = logits[: len(logits) - output_pad_len]

            predicted_ids = torch.argmax(logits, dim=-1).cpu().tolist()
            if processor.decode(predicted_ids).strip():
                leftover_ids = processor.tokenizer.encode(leftover_text)
                # concat the last word (or its part) from the last frame with the current text
                text = processor.decode(leftover_ids + predicted_ids)
                # don't return the last word in case it's just partially recognized
                if " " in text:
                    text, leftover_text = text.rsplit(" ", 1)
                else:
                    leftover_text = text
                    text = ""
                if text:
                    yield text
            else:
                yield leftover_text
                leftover_text = ""
    yield leftover_text

def main():
    state = st.session_state
    st.header("Video ASR Streamlit from Youtube Link")

    with st.form(key="inputs_form"):
    
        initial_url = "https://youtu.be/ghOqTkGzX7I?t=60"
        state.youtube_url = st.text_input("YouTube URL", initial_url)
        
        state.chunk_duration_ms = st.slider("Audio chunk duration (ms)", 2000, 10000, 3000, 100)
        state.pad_duration_ms = st.slider("Padding duration (ms)", 100, 5000, 1000, 100)
        submit_button = st.form_submit_button(label="Submit")

    if "lines" in state:
        # print the lines of subs
        st.code("\n".join(state.lines))

    if submit_button or "asr_stream" not in state:
        # a hack to update the video player on value changes
        state.youtube_url = (
            state.youtube_url.split("&hash=")[0]
            + f"&hash={state.chunk_duration_ms}-{state.pad_duration_ms}"
        )
        state.asr_stream = stream_text(
            state.youtube_url, state.chunk_duration_ms, state.pad_duration_ms
        )
        state.chunks_taken = 0
        
        
        state.lines = deque([], maxlen=5)  # limit to the last n lines of subs
        

    player = st_player(state.youtube_url, **player_options, key="youtube_player")

    if "asr_stream" in state and player.data and player.data["played"] < 1.0:
        # check how many seconds were played, and if more than processed - write the next text chunk
        processed_seconds = state.chunks_taken * (state.chunk_duration_ms / 1000)
        if processed_seconds < player.data["playedSeconds"]:
            text = next(state.asr_stream)
            state.lines.append(text)
            state.chunks_taken += 1



if __name__ == "__main__":
    main()