app.py

import torch
from transformers import (
    AutoTokenizer,
    AutoModelForSeq2SeqLM,
    AutoModelForTokenClassification,
    pipeline)
from transformers import Pipeline
import re

model = AutoModelForSeq2SeqLM.from_pretrained("emirhangazi77/Turkish-T5")
tokenizer = AutoTokenizer.from_pretrained("emirhangazi77/Turkish-T5")
ner_model = AutoModelForTokenClassification.from_pretrained("akdeniz27/bert-base-turkish-cased-ner") # pretrained ner model
ner_tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-turkish-cased-ner") # pretrained ner tokenizer
ner = pipeline('ner', model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="first") #



device = torch.device('cpu')

class Diacritic_Pipe(Pipeline):

    def __init__(self,ner,model,tokenizer):
        super().__init__(model = model, tokenizer = tokenizer)
        self.ner_pipe = ner

    def generate_result(self,text):
        prefix = "Correct diacritics for : "
        postfix = " </s>"
        text = prefix + text + postfix

        self.tokenizer.truncation_side = "left"
        batch = self.tokenizer(text, return_tensors='pt', max_length = 64, truncation = False).to(device)
        result = self.model.generate(**batch, max_new_tokens = 128)
        result = self.tokenizer.batch_decode(result)

        return str(result[0])
    def ner_predict_mapping(self,text, threshold=0.3):
        result = self.ner_pipe(text)
        if len(result) == 0:
            return []
        else:
            special_words = [result["word"] for result in result if result["score"] > threshold]
            special_words_ = []
            for word_ in special_words:
                if word_.lower()[0] == "i":
                    word_ = word_.replace("I","İ")
                if len(word_.split()) > 1:
                    special_words_.extend(word_.split())
                else:
                    special_words_.append(word_)

            return special_words_

    def split_text_into_n_worded_chunks(self,text, n):
        words = text.split()
        chunks = []
        for i in range(0, len(words), n):
            chunks.append(' '.join(words[i:i+n]))
        last_chunk_words = len(words) % n
        if last_chunk_words != 0:
            chunks[-1] = ' '.join(words[-last_chunk_words:])
        return chunks

    def chunk_2(self,text):
        chunks = self.split_text_into_n_worded_chunks(text, 2)
        processed_chunks = [re.sub(r'(["q(°\[\]{}&´])\s+', r'\1',self.generate_result(chunk))  for chunk in chunks]
        result = ' '.join(processed_chunks)
        return result.replace("<pad>","").replace("</s>","").replace("  "," ")

    def chunk_1(self,text):
        chunks = self.split_text_into_n_worded_chunks(text, 1)
        processed_chunks = [self.generate_result(chunk).replace(" ","")  for chunk in chunks]
        result = ''.join(processed_chunks)
        return result.replace("<pad>"," ").replace("</s>","")

    def process_text(self,text):
        words = self.ner_predict_mapping(text)
        two_chunk = self.chunk_2(text)
        one_chunk = self.chunk_1(text)
        if len(one_chunk.split()) != len(two_chunk.split()):
            for word in words:
                one_chunk = one_chunk.replace(word.lower().replace('i̇',"i"),word)
            return one_chunk
        else:
            for word in words:
                two_chunk = two_chunk.replace(word.lower().replace('i̇',"i"),word)
            return two_chunk

    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        if "maybe_arg" in kwargs:
            preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
        return preprocess_kwargs, {}, {}

    def preprocess(self, inputs, maybe_arg=2):
        return {"model_input": inputs}

    def _forward(self, model_inputs):
        #model_inputs == {"model_input": model_input}
        outputs = self.process_text(model_inputs["model_input"])
        # Maybe {"logits": Tensor(...)}
        return outputs

    def postprocess(self, model_outputs):
        return model_outputs

import gradio as gr

diacritics = Diacritic_Pipe(ner = ner , model = model , tokenizer = tokenizer)

def fn(query):
    response = diacritics(query)
    return str(response)

def my_chatbot(input, history):
    history = history or []
    my_history = list(sum(history, ()))
    my_history.append(input)
    my_input = ' '.join(my_history)
    output = fn(input)
    history.append((input, output))
    return history, history

import gradio as gr
import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

with gr.Blocks() as demo:
    gr.Markdown("""<h1><center>Diacritics on Turkish</center></h1>""")
    chatbot = gr.Chatbot()
    state = gr.State()
    txt = gr.Textbox(show_label=False, placeholder="Ask me a question and press enter.")
    txt.submit(my_chatbot, inputs=[txt, state], outputs=[chatbot, state])

demo.launch(share=True)