src/streamlit_app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "pszemraj/grammar-synthesis-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Check if GPU is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
 
# Grammar correction function
def correct_grammar(text):
    input_text = "gec: " + text
    inputs = tokenizer.encode(input_text, return_tensors="pt", truncation=True, padding=True).to(device)
    outputs = model.generate(inputs, max_length=512, num_beams=5, early_stopping=True)
    corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
    return corrected_text

# Gradio Interface
gr.Interface(
    fn=correct_grammar,
    inputs=gr.Textbox(lines=7, placeholder="Enter your text here...", label="Input Text"),
    outputs=gr.Textbox(label="Corrected Text"),
    title="Grammar Checker (No Java)",
    description="Uses a Hugging Face transformer model to fix grammar mistakes in English."
).launch()