import gradio as gr

from models import MODELS, PIPELINES


def predict(text: str, model_name: str) -> str:
    return PIPELINES[model_name](text)


with gr.Blocks(title="CLARIN-PL Dialogue System Modules") as demo:
    gr.Markdown("Dialogue State Tracking Modules")
    for model_name in MODELS:
        with gr.Row():
            gr.Markdown(f"## {model_name}")
            model_name_component = gr.Textbox(value=model_name, visible=False)
        with gr.Row():
            text_input = gr.Textbox(label="Input Text", value=MODELS[model_name]["default_input"])
            output = gr.Textbox(label="Slot Value", value="")
        with gr.Row():
            predict_button = gr.Button("Predict")
            predict_button.click(fn=predict, inputs=[text_input, model_name_component], outputs=output)

demo.queue(concurrency_count=3)
demo.launch()