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

# Load the model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("vennify/t5-base-grammar-correction")
tokenizer = AutoTokenizer.from_pretrained("vennify/t5-base-grammar-correction")

def correct_text(text, max_length, min_length, max_new_tokens, min_new_tokens, num_beams, temperature, top_p):
    inputs = tokenizer.encode("grammar: " + text, return_tensors="pt")
    
    if max_new_tokens > 0 or min_new_tokens > 0:
        if max_new_tokens > 0 and min_new_tokens > 0:
            outputs = model.generate(
                inputs,
                max_new_tokens=max_new_tokens,
                min_new_tokens=min_new_tokens,
                num_beams=num_beams,
                temperature=temperature,
                top_p=top_p,
                early_stopping=True,
                do_sample=True
            )
        elif max_new_tokens > 0:
            outputs = model.generate(inputs, max_new_tokens=max_new_tokens, min_length=min_length, num_beams=num_beams, temperature=temperature, top_p=top_p, early_stopping=True, do_sample=True)
        else:
            outputs = model.generate(inputs, max_length=max_length, min_new_tokens=min_new_tokens, num_beams=num_beams, temperature=temperature, top_p=top_p, early_stopping=True, do_sample=True)
    else:
        outputs = model.generate(
            inputs,
            max_length=max_length,
            min_length=min_length,
            num_beams=num_beams,
            temperature=temperature,
            top_p=top_p,
            early_stopping=True,
            do_sample=True
        )
    
    corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    yield corrected_text

def correct_text2(text, genConfig):
    inputs = tokenizer.encode("grammar: " + text, return_tensors="pt")
    outputs = model.generate(inputs, **genConfig.to_dict())

    corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    yield corrected_text

def respond(text, max_length, min_length, max_new_tokens, min_new_tokens, num_beams, temperature, top_p):
    config = GenerationConfig(
        max_length=max_length,
        min_length=min_length,
        num_beams=num_beams,
        temperature=temperature,
        top_p=top_p,
        early_stopping=True,
        do_sample=True
    )

    # Add max/min new tokens if they are there
    if max_new_tokens > 0: 
        config.max_new_tokens = max_new_tokens
    if min_new_tokens > 0: 
        config.min_new_tokens = min_new_tokens
    
    corrected = correct_text2(text, config)
    yield corrected

    

def update_prompt(prompt):
    return prompt

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("""# Grammar Correction App""")
    prompt_box = gr.Textbox(placeholder="Enter your prompt here...")
    output_box = gr.Textbox()

    # Sample prompts
    with gr.Row():
        samp1 = gr.Button("we shood buy an car")
        samp2 = gr.Button("she is more taller")
        samp3 = gr.Button("John and i saw a sheep over their.")
        
        samp1.click(update_prompt, samp1, prompt_box)
        samp2.click(update_prompt, samp2, prompt_box)
        samp3.click(update_prompt, samp3, prompt_box)

    submitBtn = gr.Button("Submit")
    
    with gr.Accordion("Generation Parameters:", open=False):
        max_length = gr.Slider(minimum=1, maximum=256, value=80, step=1, label="Max Length")
        min_length = gr.Slider(minimum=1, maximum=256, value=0,  step=1, label="Min Length")
        max_tokens = gr.Slider(minimum=0, maximum=256, value=0, step=1, label="Max New Tokens")
        min_tokens = gr.Slider(minimum=0, maximum=256, value=0, step=1, label="Min New Tokens")
        num_beams = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Num Beams")
        temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
        top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")

    
    submitBtn.click(respond, [prompt_box, max_length, min_length, max_tokens, min_tokens, num_beams, temperature, top_p], output_box)

demo.launch()