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import numpy as np
import re
import spacy
import openai
from operator import itemgetter
#user input manager class
class input_manager:
#initialize key dictionary from vector data frame
def __init__(self,key_df, slim_df, search_tokens):
self.key_df = key_df
self.slim_df = slim_df
self.search_tokens = search_tokens
self.key = dict(zip(list(key_df.columns),np.zeros(len(key_df.columns))))
self.nlp = spacy.load("en_core_web_md")
#translate input text to vector
def set_input(self,input_cats):
#need setup to apply correct group tag to values
#separate known/unknown features
k_flags = [cat for cat in input_cats if cat in list(self.key.keys())]
unk_flags = [cat for cat in input_cats if cat not in list(self.key.keys())]
#process within feature class similarity for each unknown input
if len(unk_flags)>0:
outs = []
for word in unk_flags:
if re.match(r"game_type_",word):
tok = self.nlp(word.split("_")[-1])
mtch = max([(key,key.similarity(tok)) for key in self.search_tokens[0]],key=itemgetter(1))
#if no known match is found (model doesn't recognize input word), we're going to discard - other solutions performance prohibitive
if mtch[1]>0:
outs.append("game_type_"+mtch[0])
elif re.match(r"mechanic_",word):
tok = self.nlp(word.split("_")[-1])
mtch = max([(key,key.similarity(tok)) for key in self.search_tokens[1]],key=itemgetter(1))
if mtch[1]>0:
outs.append("mechanic_"+mtch[0])
elif re.match(r"category_",word):
tok = self.nlp(word.split("_")[-1])
mtch=max([(key,key.similarity(tok)) for key in self.search_tokens[2]],key=itemgetter(1))
if mtch[1]>0:
outs.append("category_"+mtch[0])
elif re.match(r"family_",word):
tok = self.nlp(word.split("_")[-1])
mtch=max([(key,key.similarity(tok)) for key in self.search_tokens[3]],key=itemgetter(1))
if mtch[1]>0:
outs.append("family_"+str(mtch[0]))
#if unks are processed, rejoin nearest match to known.
k_flags = list(set(k_flags+outs))
#preserve global key and ouput copy w/input keys activated to 1
d = self.key.copy()
for cat in k_flags:
d[cat] = 1.0
# DELETE ME
return d
def input_parser(self,in_vec):
#extracting keys from processed vector
ks = [k for k,v in in_vec.items() if v == 1]
return ks
class model_control:
def __init__(self, apikey, model_id):
self.api_key = apikey
openai.api_key = self.api_key
self.prompt = None
self.model = openai.FineTune.retrieve(id=model_id).fine_tuned_model
def prompt_formatter(self,ks):
self.prompt = ". ".join(ks) + "\n\n###\n\n"
def call_api(self,status=0):
if status == 0:
temp=0.5
pres=0.7
elif status == 1:
temp=0.4
pres=0.6
elif status == 2:
temp=0.5
pres=0.8
answer = openai.Completion.create(
model=self.model,
prompt=self.prompt,
max_tokens=512,
temperature=temp,
stop=["END"],
presence_penalty=pres,
frequency_penalty=0.5
)
return answer['choices'][0]['text']
def resp_cleanup(self,text):
if ((text[-1] != "!") & (text[-1] != ".") & (text[-1] != "?")):
text = " ".join([e+'.' for e in text.split('.')[0:-1] if e])
sent = re.split(r'([.?!:])', text)
phrases = ["[Dd]esigned by","[Dd]esigner of","[Aa]rt by","[Aa]rtist of","[Pp]ublished","[Pp]ublisher of"]
pat = re.compile("(?:" + "|".join(phrases) + ")")
fix = re.compile("(?<=[.!?])[.!?]")
text = re.sub(fix,'',''.join([s for s in sent if pat.search(s) == None]))
return text
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