import re import gradio as gr from tqdm.auto import tqdm from transformers import pipeline # pipelines checker = pipeline("text-classification", "textattack/roberta-base-CoLA") corrector = pipeline("text2text-generation", "pszemraj/flan-t5-large-grammar-synthesis") def split_text(text: str) -> list: # Split the text into sentences using regex sentences = re.split(r"(?<=[^A-Z].[.?]) +(?=[A-Z])", text) # Initialize a list to store the sentence batches sentence_batches = [] # Initialize a temporary list to store the current batch of sentences temp_batch = [] # Iterate through the sentences for sentence in sentences: # Add the sentence to the temporary batch temp_batch.append(sentence) # If the length of the temporary batch is between 2 and 3 sentences, or if it is the last batch, add it to the list of sentence batches if len(temp_batch) >= 2 and len(temp_batch) <= 3 or sentence == sentences[-1]: sentence_batches.append(temp_batch) temp_batch = [] return sentence_batches def correct_text(text: str, checker, corrector, separator: str = " ") -> str: # Split the text into sentence batches sentence_batches = split_text(text) # Initialize a list to store the corrected text corrected_text = [] # Iterate through the sentence batches for batch in tqdm( sentence_batches, total=len(sentence_batches), desc="correcting text.." ): # Join the sentences in the batch into a single string raw_text = " ".join(batch) # Check the grammar quality of the text using the text-classification pipeline results = checker(raw_text) # Only correct the text if the results of the text-classification are not LABEL_1 or are LABEL_1 with a score below 0.9 if results[0]["label"] != "LABEL_1" or ( results[0]["label"] == "LABEL_1" and results[0]["score"] < 0.9 ): # Correct the text using the text-generation pipeline corrected_batch = corrector(raw_text) corrected_text.append(corrected_batch[0]["generated_text"]) else: corrected_text.append(raw_text) # Join the corrected text into a single string corrected_text = separator.join(corrected_text) return corrected_text def update(text: str, checker, corrector): text = text[:4000] return correct_text(text, checker, corrector) with gr.Blocks() as demo: gr.Markdown("
# Robust Grammar Correction with FLAN-T5
") gr.Markdown("Enter the text you want to correct in the textbox below. The text will be truncated to 4000 characters. Then click Run to see the corrected text.") with gr.Row(): inp = gr.Textbox(placeholder="I wen to the store yesturday to bye some food. I needd milk, bread, and a few otter things. The store was really crowed and I had a hard time finding everyting I needed. I finaly made it to the check out line and payed for my stuff.") out = gr.Textbox() btn = gr.Button("Process Text") btn.click(fn=update, inputs=inp, outputs=out) demo.launch()