import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# Load model and tokenizer from Hugging Face Hub
model_name = "Electricarchmage/cookbookgpt"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Set the pad_token to eos_token
tokenizer.pad_token = tokenizer.eos_token

# Define the respond function
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Preparing the messages for context (the history and the new message)
    messages = [{"role": "system", "content": system_message}]
    
    # Convert history to the required format with 'role' and 'content'
    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})

    # Tokenize the input and generate a response
    inputs = tokenizer([msg["content"] for msg in messages], return_tensors="pt", padding=True, truncation=True)
    attention_mask = inputs.get('attention_mask', torch.ones_like(inputs['input_ids']))  # Default to ones if not provided

    # Generate output tokens
    output = model.generate(
        inputs["input_ids"],
        attention_mask=attention_mask,
        max_length=max_tokens + len(inputs["input_ids"][0]),
        temperature=temperature,
        top_p=top_p,
        num_return_sequences=1,
        do_sample=True,  # Enable sampling for more dynamic responses
        no_repeat_ngram_size=2,
    )

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

    # Extract only the assistant's reply
    assistant_reply = response.split("Assistant:")[-1].strip()

    return assistant_reply

# 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)",
        ),
    ],
)

# Launch the app
if __name__ == "__main__":
    demo.launch()