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

# Initialize Hugging Face Inference API client
hf_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Load the second model
local_model_name = "codewithdark/latent-recurrent-depth-lm"
tokenizer = AutoTokenizer.from_pretrained(local_model_name)
model = AutoModelForCausalLM.from_pretrained(local_model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

def generate_response(
    message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, model_choice
):
    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})

    if model_choice == "Zephyr-7B (API)":
        response = ""
        for message in hf_client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        ):
            token = message.choices[0].delta.content
            response += token
            yield response
    else:
        input_text = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
        output = model.generate(input_text, max_length=max_tokens, temperature=temperature, top_p=top_p)
        response = tokenizer.decode(output[0], skip_special_tokens=True)
        yield response

demo = gr.ChatInterface(
    generate_response,
    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)"),
        gr.Radio(["Zephyr-7B (API)", "Latent Recurrent Depth LM"], value="Zephyr-7B (API)", label="Select Model"),
    ],
)

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