import gradio as gr
from transformers import pipeline
from transformers import pipeline
import re

dante = pipeline('text-generation',model='.', tokenizer='GroNLP/gpt2-small-italian-embeddings')

def grammatical_cleaning(generated: str) -> str:
	generated = re.sub("\.[^\s]",". ", generated)
	generated = re.sub("\,[^\s]",", ", generated)
	generated = re.sub("\;[^\s]","; ", generated)
	generated = re.sub("\:[^\s]",": ", generated)
	generated = re.sub("\![^\s]","! ", generated)
	generated = list(generated)
	for n in range(len(generated)-2):
		if generated[n]=="." or generated[n]=="?":
			if generated[n+1].islower() and generated[n+1].isalpha():
				generated[n+1] = generated[n+1].upper()
			elif generated[n+2].islower() and generated[n+2].isalpha():
				generated[n+2] = generated[n+2].upper()
	return ''.join(generated)

def get_text(input):
	generated = dante(input, max_length=128)[0]['generated_text']
	generated = grammatical_cleaning(generated)
	return generated

inp = input()
print(get_text(inp))

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