File size: 2,831 Bytes
856f2f9 1bc13fb 856f2f9 88c1875 856f2f9 88c1875 856f2f9 88c1875 a542228 856f2f9 88c1875 856f2f9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
import gradio as gr
import pickle
import random
import numpy as np
with open('models.pickle', 'rb')as f:
models = pickle.load(f)
LORA_TOKEN = ''#'<|>LORA_TOKEN<|>'
# WEIGHT_TOKEN = '<|>WEIGHT_TOKEN<|>'
NOT_SPLIT_TOKEN = '<|>NOT_SPLIT_TOKEN<|>'
def sample_next(ctx:str,model,k):
ctx = ', '.join(ctx.split(', ')[-k:])
if model.get(ctx) is None:
return " "
possible_Chars = list(model[ctx].keys())
possible_values = list(model[ctx].values())
# print(possible_Chars)
# print(possible_values)
return np.random.choice(possible_Chars,p=possible_values)
def generateText(model, minLen=100, size=5):
keys = list(model.keys())
starting_sent = random.choice(keys)
k = len(random.choice(keys).split(', '))
sentence = starting_sent
ctx = ', '.join(starting_sent.split(', ')[-k:])
while True:
next_prediction = sample_next(ctx,model,k)
sentence += f", {next_prediction}"
ctx = ', '.join(sentence.split(', ')[-k:])
# if sentence.count('\n')>size: break
if '\n' in sentence: break
sentence = sentence.replace(NOT_SPLIT_TOKEN, ', ')
# sentence = re.sub(WEIGHT_TOKEN.replace('|', '\|'), lambda match: f":{random.randint(0,2)}.{random.randint(0,9)}", sentence)
# sentence = sentence.replace(":0.0", ':0.1')
# return sentence
prompt = sentence.split('\n')[0]
if len(prompt)<minLen:
prompt = generateText(model, minLen, size=1)[0]
size = size-1
if size == 0: return [prompt]
output = []
for i in range(size+1):
prompt = generateText(model, minLen, size=1)[0]
output.append(prompt)
return output
def sentence_builder(quantity, minLen, Type, negative):
if Type == "NSFW": idx=1
elif Type == "SFW": idx=2
else: idx=0
model = models[idx]
output = ""
for i in range(quantity):
prompt = generateText(model[0], minLen=minLen, size=1)[0]
output+=f"PROMPT: {prompt}\n\n"
if negative:
negative_prompt = generateText(model[1], minLen=minLen, size=5)[0]
output+=f"NEGATIVE PROMPT: {negative_prompt}\n"
output+="----------------------------------------------------------------"
output+="\n\n\n"
return output[:-3]
ui = gr.Interface(
sentence_builder,
[
gr.Slider(1, 10, value=4, label="Count", info="Choose between 1 and 10", step=1),
gr.Slider(100, 1000, value=300, label="minLen", info="Choose between 100 and 1000", step=50),
gr.Radio(["NSFW", "SFW", "BOTH"], label="TYPE", info="NSFW stands for NOT SAFE FOR WORK, so choose any one you want?"),
gr.Checkbox(label="negitive Prompt", info="Do you want to generate negative prompt as well as prompt?"),
],
"text"
)
if __name__ == "__main__":
ui.launch() |