import gradio as gr
from qasrl_model_pipeline import QASRL_Pipeline

models = ["kleinay/qanom-seq2seq-model-baseline", 
          "kleinay/qanom-seq2seq-model-joint"]
pipelines = {model: QASRL_Pipeline(model) for model in models}


description = f"""This is a demo of our QASRL\QANom models, which fine-tuned Seq2Seq pretrained models on the QASRL\QANom task.""" 
title="QANom Parser Demo"
examples = [["The doctor was interested in Luke 's <predicate> treatment .", True, "treat"],
            ["The Veterinary student was interested in Luke 's <predicate> treatment of sea animals .", True, "treat"]]
input_sent_box_label = "Insert sentence here. Mark the predicate by adding the token '<predicate>' before it."
links = """<p style='text-align: center'>
<a href='https://www.qasrl.org' target='_blank'>QASRL Website</a> |
<a href='https://huggingface.co/spaces/kleinay/qanom-seq2seq-demo' target='_blank'>Model Repo at Huggingface Hub</a> | 
</p>"""
def call(model_name, sentence, is_nominal, verb_form):
    predicate_marker="<predicate>"
    if predicate_marker not in sentence:
        print("You must highlight one word of the sentence as a '<predicate>'.")
        return
    if not verb_form:
        if is_nominal:
            raise ValueError("You should provide the verbal form of the nominalization")
            
        toks = sentence.split(" ")
        pred_idx = toks.index(predicate_marker)
        predicate = toks(pred_idx+1)
        verb_form=predicate
    pipeline = pipelines[model_name]
    return pipeline(sentence, 
                    predicate_marker=predicate_marker, 
                    predicate_type="nominal" if is_nominal else "verbal",
                    verb_form=verb_form)
iface = gr.Interface(fn=call, 
                     inputs=[gr.inputs.Radio(choices=models, default=models[0], label="Model"), 
                             gr.inputs.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), 
                             gr.inputs.Checkbox(default=True, label="Is Nominalization?"),
                             gr.inputs.Textbox(label="verbal form of nominalization", default='')], 
                     outputs=gr.outputs.JSON(label="Model Output - QASRL"),
                     title=title,
                     description=description,
                     article=links,
                     examples=examples )