import gradio as gr 
from transformers import AutoTokenizer, pipeline
import torch

tokenizer1 = AutoTokenizer.from_pretrained("notexist/tttff")
tdk1 = pipeline('text-generation', model='notexist/tttff', tokenizer=tokenizer1)
tokenizer2 = AutoTokenizer.from_pretrained("notexist/ttte")  
tdk2 = pipeline('text-generation', model='notexist/ttte', tokenizer=tokenizer2)


# Speller borrowed from http://turkceddi.blogspot.com/
lower_vowel = {'a', 'â', 'e', 'ê', 'ı', 'î', 'i', 'o', 'ô', 'ö', 'u', 'û', 'ü'}
SPELL_SLICER = (('001000', 5), ('000100', 5), ('01000', 4), ('00100', 4), ('00010', 4), ('1000', 3), ('0100', 3),
                ('0011', 3), ('0010', 3), ('011', 2), ('010', 2), ('100', 2), ('10', 1), ('11', 1))


def to_lower(word):
    tolower_text = (word.replace('İ', 'i'))
    tolower_text = (tolower_text.replace('I', 'ı'))
    return tolower_text.lower()


def wordtoten(word: str):
    wtt = ''

    for ch in word:
        if ch in lower_vowel:
            wtt += '1'
        else:
            wtt += '0'
    return wtt

def spellword(word_b: str):
    word_b = to_lower(word_b).strip()
    z = []
    for word in word_b.split():
        syllable_list = []
        tenword = wordtoten(word)
        len_spell = tenword.count('1')

        for i in range(tenword.count('1')):
            for x, y in SPELL_SLICER:
                if tenword.startswith(x):
                    syllable_list.append(word[:y])
                    word = word[y:]
                    tenword = tenword[y:]
                    break

        if tenword == '0':
            syllable_list[-1] = syllable_list[-1] + word
        elif word:
            syllable_list.append(word)

        if len(syllable_list) != len_spell:
            return False

        z.append("·".join(syllable_list))
    return " ".join(z)

def predict(name, sl, topk, topp):
    if name == "":
        x1 = tdk1(f"<|endoftext|>",
            do_sample=True, 
            max_length=64, 
            top_k=topk, 
            top_p=topp, 
            num_return_sequences=1,
            repetition_penalty=sl
        )[0]["generated_text"]
        new_name = x1[len(f"<|endoftext|>"):x1.index("\n\n")]
        x2 = tdk2(f"<|endoftext|>{new_name}\n\n",
            do_sample=True, 
            max_length=64, 
            top_k=topk, 
            top_p=topp, 
            num_return_sequences=1,
            repetition_penalty=sl
        )[0]["generated_text"]
        
        if "[TEXT]" not in x2:
            return x1[len(f"<|endoftext|>"):]+"\n\n"+spellword(new_name)
        else:
            return x1[len(f"<|endoftext|>"):]+"\n\n"+spellword(new_name)+"\n\n"+x2[len(f"<|endoftext|>{new_name}\n\n"):].replace("[TEXT]", " "+new_name+" ")
    else:
        x1 = tdk1(f"<|endoftext|>{name}\n\n",
            do_sample=True, 
            max_length=64, 
            top_k=topk, 
            top_p=topp, 
            num_return_sequences=1,
            repetition_penalty=sl
        )[0]["generated_text"]
        x2 = tdk2(f"<|endoftext|>{name}\n\n",
            do_sample=True, 
            max_length=64, 
            top_k=topk, 
            top_p=topp, 
            num_return_sequences=1,
            repetition_penalty=sl
        )[0]["generated_text"]

        if "[TEXT]" not in x2:
            return x1[len(f"<|endoftext|>{name}\n\n"):]+"\n\n"+spellword(name)
        else:
            return x1[len(f"<|endoftext|>{name}\n\n"):]+"\n\n"+spellword(name)+"\n\n"+x2[len(f"<|endoftext|>{name}\n\n"):].replace("[TEXT]", " "+name+" ")



iface = gr.Interface(fn=predict, inputs=["text",\
                                         gr.inputs.Slider(0, 3, default=1.1, label="repetition_penalty"),\
                                         gr.inputs.Slider(0, 100, default=75, label="top_k"),\
                                         gr.inputs.Slider(0, 1, default=0.95, label="top_p")]
                     ,  outputs="text")
iface.launch()