import gradio as gr
import torch
from minicons import cwe
import pandas as pd
import numpy as np

from model import FeatureNormPredictor


def predict (Word, Sentence, LM, Layer, Norm):
    if Word not in Sentence: return "invalid input: word not in sentence"
    model_name = LM + str(Layer) + '_to_' + Norm
    lm = cwe.CWE('bert-base-uncased')
    if Layer not in range (lm.layers): return "invalid input: layer not in lm"

    model = FeatureNormPredictor.load_from_checkpoint(
        checkpoint_path=model_name+'.ckpt',
        map_location=None
        )
    model.eval()

    with open (model_name+'.txt', "r") as file:
        labels = [line.rstrip() for line in file.readlines()]

    data = (Word, Sentence)
    embs = lm.extract_representation(data, layer=8)
    avg = embs.sum(0)/len(data)
    pred = torch.nn.functional.relu(model(avg))
    pred = pred.squeeze(0)
    pred_list = pred.detach().numpy().tolist()
    
    output = [labels[i]+'\t'+str(pred_list[i]) for i in range(len(labels)) if i > 0.0]
    return "\n".join(output)

demo = gr.Interface(
    fn=predict,
    inputs=[
        "text", 
        "text", 
        gr.Radio(["bert", "roberta", "electra"]),
        "number",
        gr.Radio(["binder", "mcrae", "buchanan"]),
    ],
    outputs=["text"],
)

demo.launch()