from metrics import nlp_metrics
nist, bleu, meteor, entropy, diversity, avg_len = nlp_metrics(
	  path_refs=["demo/ref0.txt", "demo/ref1.txt"], 
	  path_hyp="demo/hyp.txt")
	  
# nist = [1.8338, 2.0838, 2.1949, 2.1949]
# bleu = [0.4667, 0.441, 0.4017, 0.3224]
# meteor = 0.2832
# entropy = [2.5232, 2.4849, 2.1972, 1.7918]
# diversity = [0.8667, 1.000]
# avg_len = 5.0000

from data_prepare import clean_str
s = " I don't know:). how about this?https://github.com"
clean_str(s)

# i do n't know :) . how about this ? __url__

from dialog_gui import *

def my_respond_func(inp):
        # TODO
        # input: type=str, value=conversation history. turns delimited by 'EOS'
        # return: a list of (score, hyp) tuple based on input
        
app = QtWidgets.QApplication([])
respond_funcs = [my_respond_func]
gui = DialogGUI(respond_funcs, ['my_system_name'])
gui.w.update()
app.exec_()