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import gradio as gr
from models import DST_MODELS, NLG_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 DST_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=DST_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)
gr.Markdown("Natural Language Generation / Paraphrasing Modules")
for model_name in NLG_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=NLG_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()
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