#Imports
import whisper
import gradio as gr 
import warnings
from gtts import gTTS

warnings.filterwarnings("ignore")

model = whisper.load_model("base")


def transcribe(audio):
    language = 'en'

    audio = whisper.load_audio(audio)
    audio = whisper.pad_or_trim(audio)

    mel = whisper.log_mel_spectrogram(audio).to(model.device)

    _, probs = model.detect_language(mel)
    print(f"Detected language: {max(probs, key=probs.get)}")

    options = whisper.DecodingOptions(fp16 = False)
    result = whisper.decode(model, mel, options)
    result_text = result.text
    result_tr = model.transcribe(audio ,task='translate')
    
    audioobj = gTTS(text = result_tr['text'], 
                    lang = language, 
                    slow = False)
    
    audioobj.save("Temp.mp3")

    return [result_text, result_tr['text'], "Temp.mp3"]


output_1 = gr.Textbox(label="Speech to Text")
output_2 = gr.Textbox(label="English Translation Output")
output_3 = gr.Audio("Temp.mp3", label="English Audio")

gr.Interface(
    title = 'OpenAI Whisper ASR and Translation Gradio Web UI', 
    fn=transcribe, 
    inputs=[
        # gr.inputs.Audio(source="microphone", type="filepath"),
        gr.Audio(source="upload", type="filepath")
    ],

    outputs=[
        output_1, output_2, output_3
    ],
    live=True).launch(debug=True)