app.py

import gradio as gr # type: ignore
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # type: ignore
import torch

# Load your trained model and tokenizer
model_name = "yashvoladoddi37/movie-title-OCR-corrector-t5" 
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)

def correct_text(input_text):
    inputs = tokenizer(input_text, return_tensors="pt", padding=True).to(device)
    with torch.no_grad():
        outputs = model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            max_length=512
        )
    corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return corrected_text

iface = gr.Interface(
    fn=correct_text, 
    inputs=gr.Textbox(lines=2, placeholder="Enter text to correct"), 
    outputs="text",
    title="OCR Correction Demo",
    description="Enter text with OCR errors, and the model will attempt to correct them."
)

iface.launch()  # Remove share=True for deployment