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

# Load the model and tokenizer
model_name = 'abinayam/gpt-2-tamil'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def correct_text(input_text):
    # Tokenize the input text
    input_ids = tokenizer.encode(input_text, return_tensors='pt')
    
    # Generate corrected text
    with torch.no_grad():
        output = model.generate(input_ids, max_length=100, num_return_sequences=1, temperature=0.7)
    
    # Decode the generated text
    corrected_text = tokenizer.decode(output[0], skip_special_tokens=True)
    
    return corrected_text

# Create the Gradio interface
iface = gr.Interface(
    fn=correct_text,
    inputs=gr.Textbox(lines=5, placeholder="Enter Tamil text here..."),
    outputs=gr.Textbox(label="Corrected Text"),
    title="Tamil Spell Corrector and Grammar Checker",
    description="This app uses the 'abinayam/gpt-2-tamil' model to correct spelling and grammar in Tamil text.",
)

# Launch the app
iface.launch()