import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Leo022/Gemma_QA_For_Telegram_Bot")
model = AutoModelForCausalLM.from_pretrained("Leo022/Gemma_QA_For_Telegram_Bot")

def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
    """
    Function to generate response from the model.
    Args:
        message (str): The user's input message.
        history (list): The conversation history.
        system_message (str): The system message.
        max_tokens (int): Maximum number of tokens for output.
        temperature (float): Sampling temperature.
        top_p (float): Nucleus sampling parameter.
    Returns:
        str: The model's response.
    """
    # Initialize messages list with the system message
    messages = [{"role": "system", "content": system_message}]

    # Add conversation history to messages
    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})

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

    # Encode the concatenation of all message contents
    input_ids = tokenizer.encode(" ".join([msg["content"] for msg in messages]), return_tensors="pt")

    # Generate response
    output = model.generate(
        input_ids,
        max_length=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
    )

    # Decode the generated tokens to get the response text
    response = tokenizer.decode(output[0], skip_special_tokens=True)

    return response

# Define the Gradio interface
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=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__":
    # Launch the Gradio app
    demo.launch()