import gradio as gr
import difflib,time,numpy
# Dictionary
dictionaryFile=open('words.txt','r') # Open words file
dictionary=dictionaryFile.readlines() # Read words file into list
dictionaryFile.close()

def check3(inp, inp2):
    if len(inp) > len(inp2):
        longest = inp
        shortest = inp2
    else:
        shortest = inp
        longest = inp2
    ls = len(shortest)
    ll = len(longest)
    out = 0
    for i in range(ls):
        if shortest[i] in longest:
            out += 1
        if shortest[i] == longest[i]:
            out += 2
    return out - ((out-ls)*0.5) - (ll-ls) * 0.5

class Neuron:
    def __init__(self, inp, output: str):
        self.output = output
        self.inp = inp
    def train(self, inpr):
        lir = len(inpr)
        lirm = lir-1
        linp = len(inp)
        linpm = linp-1
        cid = random.randint(0, lirm)
        if cid < linpm:
            if random.choice([True, True, False]):
                del self.inp[linpm]
            else:
                self.inp[cid] = inpr[cid]
        elif cid == linpm:
            self.inp[cid] = inpr[cid]
        else:
            self.inp.append(inpr[cid])
#        cid = random.randint(0, len(inpr)-1)
#        c2id = random.randint(0, len(inpr[cid])-1)
#        if inpr[cid][c2id] not in self.inp:
#            self.inp.append(inpr[cid][c2id])
#        cid = random.randint(0, len(self.inp)-1)
#        if not check(self.inp[cid], inpr):
#            del self.inp[cid]

def check(word, list2d):
    for i in list2d:
        if word in i: return True

outs1 = []

# Work
def get_matches(text):
    # prepare text
    textR=str(text)
    textR=textR.split() # Split inputed text to list
    # Variables
    queue=len(textR)
    output=[]
    # try to correct it
    try:
        ##return(difflib.get_close_matches(text, dictionary))  #[0].replace("\n", "")
        for x in range(queue):
            output.append(difflib.get_close_matches(textR[x], dictionary))
        return(output)
    except Exception as ex: # Print error if error
        return(ex)

iface = gr.Interface(fn=get_matches, inputs="text", outputs="text")
iface.launch()