import gradio as gr

from gradio_client import Client, handle_file

client = Client("Ghana-NLP/Northern-Ghana-ASR")

demo = gr.Blocks()

title = "# Khaya AI: Speech Recognition for Languages of Northern Ghana"

description = """
<b>How to use:</b> <br>
SPECIFY INPUT LANGUAGE & Speak through mic (or upload a file of same).<br>
MAXIMUM 30 seconds please.<br>
This model does not execute capitalization and punctuation.<br>
"""

def transcribe_wrapper_fn(audio, LANG):
    result = client.predict(
		audio=handle_file(audio),
        LANG=LANG,
		api_name="/predict"
        )  
    
    return result

mic_transcribe = gr.Interface(
    fn=transcribe_wrapper_fn,
    inputs=[gr.Audio(sources=["microphone"], type="filepath"), gr.Dropdown(["Gonja", "Mampruli", "Gurene","Dagbani","Dagaare","Konkomba (Likpakpaanl)","Wali","Konkomba (Likoonli)","Kasem","African English"], value="Gonja", multiselect=False, label="Language", info="Select Lamguage")],
    outputs=gr.Textbox(),
)

file_transcribe = gr.Interface(
    fn=transcribe_wrapper_fn,
    inputs=[gr.Audio(sources=["upload"], type="filepath"), gr.Dropdown(["Gonja", "Mampruli", "Gurene","Dagbani","Dagaare","Konkomba (Likpakpaanl)","Wali","Konkomba (Likoonli)","Kasem","African English"], value="Gonja", multiselect=False, label="Language", info="Select Lamguage")],
    outputs=gr.Textbox(),
)

with demo:
    gr.Markdown(title)
    gr.Markdown(description)
    gr.TabbedInterface(
        [mic_transcribe, file_transcribe],
        ["Transcribe Microphone", "Transcribe Audio File"],
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()