methods to play with nltk so far

Update: Found some new things to do....  Putting them on next post, with updated code.

I'm not having much luck doing anything useful with the verbs of the user (input).  I can't seem to find anything in the nltk stuff online that might suggest they have a way of distinguishing 'action verbs' from the other verbs.  Action verbs would be very useful.
I'll make some pictures to show off a bit.   The script1.txt, and script2.txt I'm using as the sys.argv[1] are simple files with questions.  I'll do a pic of that too.

So, here is my tinkering so far.  And it is really fun to play with.  Maybe I'm odd, I think it's fun to pretend my computer has my quirky sense of humor.

Pictures:

noun response
method:
make_noun_response()

verb response
method:
make_verb_response()

 script1.txt

noun response questions








Code:

#!usr/bin/python3
# -*- coding: utf-8 -*-
import sys
from nltk.corpus import wordnet
from random import randint
import nltk as nltk


# place script1, script2,  sys.argv[]  here
#script1 = sys.argv[1]
#script2 = sys.argv[1]

"""
  Requires:
  above imports and :
  install - nltk
  install - python3
  In your python3 shell type these to download needed data sets:
  >>>import nltk
  >>>nltk.download('wordnet')
  >>>nltk.download('punkt')
  >>>nltk.download('averaged_perceptron_tagger')
  make_noun_response() -- requires script1 as sys.argv
  make_verb_response() -- requires script2 as sys.argv
"""

def get_script_line(arg):
with open(arg) as f:
for i, l in enumerate(f):
pass
count = i
if count != None:
with open(arg) as f:
lines = f.readlines()
x = randint(0, count)
return lines[x]

def run_synonyms():
search_for = input(">>>")
while search_for not in ['QUIT', 'EXIT']:

alist = []
for syn in wordnet.synsets(search_for):
for l in syn.lemmas():
alist.append(l.name())
if len(alist) > 0:
length = len(alist) - 1
x = randint(0, length)
#print(x)
synx = alist[x]
print(f"possible matches = {length + 1}")
print(f"Are you looking for the word similar too : {synx} ?")
else:
print("word not found in wordnet")
search_for = input(">>>")

def find_NN_VV():
search_for = input("...>>>")
while search_for not in ['QUIT', 'EXIT']:
nounlist = []
verblist = []

tokens = nltk.word_tokenize(search_for)
#print(tokens)
tags = nltk.pos_tag(tokens)
#print(tags)
for item in tags:
#print(item[1][0])
if item[1][0] == 'N':
nounlist.append(item[0])
if item[1][0] == 'V':
verblist.append(item[0])
print("nouns = ")
print(nounlist)
print("verbs = ")
print(verblist)
search_for = input("...>>>")

def make_noun_response():
""" don't forget to set script1 as sys.argv[1], and type it in when you run this file
    $python nltk0_ex.py script1.txt  """
make = input(" user : ")
while make not in ['EXIT', 'QUIT']:
nounlist = []
tokens = nltk.word_tokenize(make)
tags = nltk.pos_tag(tokens)
for item in tags:
x = item[1]
if x.startswith('NN') == True:
nounlist.append(item[0])
if len(nounlist) > 0:
# change it to a set to eliminate duplicates
nounlist = set(nounlist)
# change it back to list, to be able to easily index random selection
nounlist = list(nounlist)
x = randint(0, len(nounlist) - 1)
script = get_script_line(script1)
noun = nounlist[x]
print("Wiwa: ")
print(script % noun)
else:
print("Wiwa: \nI do not comprehend.")
make = input(" user : ")

def make_verb_response():
""" Wiwa's verb scripting is really shotty. Real conversational verbs aren't direct action verbs
in most cases, so the script is not very interactive. 
don't forget to set script2 as sys.argv[1], and type it in when you run this file
    $python nltk0_ex.py script2.txt  """
make = input(" user : ")
while make not in ['EXIT', 'QUIT']:
verblist = []
tokens = nltk.word_tokenize(make)
tags = nltk.pos_tag(tokens)
for item in tags:
x = item[1]
if x.startswith('VB') == True:
verblist.append(item[0])
if len(verblist) > 0:
# change it to a set to eliminate duplicates
verblist = set(verblist)
# change it back to a list to easily index a random element
# python has a built in for random.sample and random.choice on sets,
# but have had issues with random being slow when dealing with large data
# which I hope wiwa to have at some point.
verblist = list(verblist)
x = randint(0, len(verblist) - 1)
script = get_script_line(script2)
verb = verblist[x]
print("Wiwa: ")
print(script % verb)
else:
print("Wiwa: \nI do not comprehend.")
make = input(" user : ")

    
    
#find_NN_VV()
#run_synonyms()
#make_noun_response()
#make_verb_response()