import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
#client = InferenceClient("vennify/t5-base-grammar-correction")
#gr.load("models/vennify/t5-base-grammar-correction").launch()

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

def correct_text(text, max_length, num_beams, temperature, top_p):
    inputs = tokenizer.encode(text, return_tensors="pt")
    outputs = model.generate(
        inputs,
        max_length=max_length,
        num_beams=num_beams,
        temperature=temperature,
        top_p=top_p,
        early_stopping=True
    )
    corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return corrected_text


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    num_beams,
    temperature,
    top_p,
):
    #messages = [{"role": "system", "content": system_message}]

    #for val in history:
    #    if val[0]:
    #        messages.append({"role": "user", "content": val[0]})
    #    if val[1]:
    #        messages.append({"role": "assistant", "content": val[1]})

    #messages.append({"role": "user", "content": message})

    response = correct_text(message, max_tokens, num_beams, temperature, top_p)
    yield response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1,   maximum=2048, value=512,  step=1,    label="Max new tokens"),
        gr.Slider(minimum=1,   maximum=10,   value=5,    step=1,    label="Num Beams"),
        gr.Slider(minimum=0.1, maximum=4.0,  value=0.7,  step=0.1,  label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0,  value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
)


if __name__ == "__main__":
    demo.launch()