from difflib import Differ

import gradio as gr

import torch
from transformers import (
    AutoModelForSpeechSeq2Seq,
    AutoProcessor,
    pipeline,
)

description = """
 <div>
    <p>Roll up, roll up come test your diction against a 🤖</p>
 </div>
"""

diction_text = "How now brown cow"


diction_script = gr.Textbox(diction_text, interactive=False)


device = "cpu"

torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "openai/whisper-large-v3"


model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    task="automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=30,
    batch_size=8,
    return_timestamps=True,
    torch_dtype=torch_dtype,
    device=device,
)


def diff_texts(diction_text: str, audio_input: str):

    d = Differ()

    return [
        (token[2:], token[0] if token[0] != "" else None)
        for token in d.compare(diction_text, audio_input)
    ]


def transcribe_audio(diction_text, audio):
    result = pipe(audio)
    diff_text = diff_texts(diction_text, result["text"])

    return diff_text


highlighted_results = gr.HighlightedText(
    label="Diff",
    combine_adjacent=True,
    show_legend=True,
    color_map={"+": "red", "-": "green"},
)


input_audio = gr.Audio(
    sources=["microphone"],
    type="filepath",
    waveform_options=gr.WaveformOptions(
        waveform_color="#01C6FF",
        waveform_progress_color="#0066B4",
        skip_length=2,
        show_controls=False,
    ),
)

demo = gr.Interface(
    fn=transcribe_audio,
    inputs=[diction_script, input_audio],
    outputs=highlighted_results,
    title="Test your diction",
    description=description,
    theme="abidlabs/Lime",
)


if __name__ == "__main__":
    demo.launch()