app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel

# Load base model and LoRA fine-tuned model
base_model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-small")
model = PeftModel.from_pretrained(base_model, "rihebriri/byt5_lora_finetuned")
tokenizer = AutoTokenizer.from_pretrained("google/byt5-small")

# Define function to correct text
def correct_text(text):
    inputs = tokenizer(text, return_tensors="pt").input_ids
    outputs = model.generate(inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Create Gradio interface
iface = gr.Interface(fn=correct_text, inputs="text", outputs="text")

# Launch API
iface.launch()