import gradio as gr
from transformers import pipeline
import numpy as np
import librosa
import pandas as pd


MODEL_NAME = "openai/whisper-tiny"
BATCH_SIZE = 8
# device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    # device=device,
)

# eng_classifier = pipeline("text-classification", model="Hate-speech-CNERG/bert-base-uncased-hatexplain")

def format_output_to_list(data):
    formatted_list = "\n".join([f"{item['timestamp'][0]}s - {item['timestamp'][1]}s \t : {item['text']}" for item in data])
    return formatted_list

def transcribe(inputs, task):
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")

    output = pipe(inputs, batch_size=BATCH_SIZE, return_timestamps="word", generate_kwargs={"task": task})
    text = output['text']
    timestamps = format_output_to_list(output['chunks'])
    return  [text, timestamps]

examples = [
        ["arabic_english_audios/audios/arabic_audio_1.wav"],
        ["arabic_english_audios/audios/arabic_audio_2.wav"],
        ["arabic_english_audios/audios/arabic_audio_3.wav"],
        ["arabic_english_audios/audios/arabic_audio_4.wav"],
        ["arabic_english_audios/audios/arabic_hate_audio_1.mp3"],
        ["arabic_english_audios/audios/arabic_hate_audio_2.mp3"],
        ["arabic_english_audios/audios/arabic_hate_audio_3.mp3"],
        ["arabic_english_audios/audios/english_audio_1.wav"],
        ["arabic_english_audios/audios/english_audio_2.mp3"],
        ["arabic_english_audios/audios/english_audio_3.mp3"],
        ["arabic_english_audios/audios/english_audio_4.mp3"],
        ["arabic_english_audios/audios/english_audio_5.mp3"],
        ["arabic_english_audios/audios/english_audio_6.wav"]
    ]

with gr.Blocks(theme=gr.themes.Default()) as demo:
    gr.HTML("<h1 style='text-align: center;'>Transcribe Audio with Timestamps using whisper-large-v3</h1>")
    gr.Markdown("")
    with gr.Row():
        with gr.Column():
            audio_input = gr.Audio(sources=["upload", 'microphone'], type="filepath", label="Audio file")
            task = gr.Radio(["transcribe", "translate"], label="Task")
            with gr.Row():
                clear_button = gr.ClearButton(value="Clear")
                submit_button = gr.Button("Submit", variant="primary", )
            
        with gr.Column():
            transcript_output = gr.Text(label="Transcript")
            timestamp_output = gr.Text(label="Timestamp")
    
    examples = gr.Examples(examples, inputs=audio_input, outputs=[transcript_output, timestamp_output], fn=transcribe, examples_per_page=20)

    submit_button.click(fn=transcribe, inputs=audio_input, outputs=[transcript_output, timestamp_output])
    clear_button.add([audio_input, transcript_output, timestamp_output])


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