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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the GPT model and tokenizer
model_name = "openai-community/openai-gpt"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Function for grammar correction using GPT
def correct_grammar(text):
# Prepare the prompt for grammar correction
prompt = f"Correct the grammar of the following sentence:\n{text}\nCorrected: "
# Encode the input text and generate output
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
# Decode the generated text and return the corrected sentence
corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Post-process the output to extract the corrected sentence
corrected_text = corrected_text.replace(prompt, "").strip() # Clean up the result
return corrected_text
# Gradio interface for the grammar correction app
interface = gr.Interface(
fn=correct_grammar,
inputs="text",
outputs="text",
title="Grammar Correction with GPT",
description="Enter a sentence or paragraph to receive grammar corrections using the OpenAI GPT model."
)
if __name__ == "__main__":
interface.launch()
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