import gradio as gr
import os
import spaces
from transformers import GemmaTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)


DESCRIPTION = '''
<div>
<h1 style="text-align: center;">Test Model</h1>
</div>
'''

LICENSE = """
<p/>

---
"""

PLACEHOLDER = """
"""


css = """
h1 {
  text-align: center;
  display: block;
}

#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
"""

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Orenguteng/Llama-3-8B-Lexi-Uncensored")
model = AutoModelForCausalLM.from_pretrained("Orenguteng/Llama-3-8B-Lexi-Uncensored", device_map="auto")  # to("cuda:0") 
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

@spaces.GPU(duration=120)
def chat_llama3_8b(message: str, 
              history: list, 
              temperature: float, 
              max_new_tokens: int,
              top_p: float,
              system_prompt: str
             ) -> str:
    """
    Generate a streaming response using the llama3-8b model.
    Args:
        message (str): The input message.
        history (list): The conversation history used by ChatInterface.
        temperature (float): The temperature for generating the response.
        max_new_tokens (int): The maximum number of new tokens to generate.
        top_p (float): The top_p value for nucleus sampling.
        system_prompt (str): The system prompt to guide the conversation.
    Returns:
        str: The generated response.
    """
    conversation = [{"role": "system", "content": system_prompt}]
    for user, assistant in history:
        conversation.append({"role": "user", "content": user})
        conversation.append({"role": "assistant", "content": assistant})

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

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
    attention_mask = input_ids.ne(tokenizer.pad_token_id).long()

    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids= input_ids,
        attention_mask=attention_mask,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        eos_token_id=terminators,
        pad_token_id=tokenizer.eos_token_id,
    )
    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.             
    if temperature == 0:
        generate_kwargs['do_sample'] = False
        
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    first_chunk = True
    for text in streamer:
        if first_chunk and text.startswith("assistant"):
            text = text[len("assistant"):].lstrip(": \n")  # Remove "assistant" and any following symbols
        first_chunk = False

        outputs.append(text)
        yield "".join(outputs)        

# Gradio block
chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface', type='messages')

with gr.Blocks(fill_height=True, css=css) as aida:
    
    gr.Markdown(DESCRIPTION)
    gr.ChatInterface(
        fn=chat_llama3_8b,
        chatbot=None,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(minimum=0,
                      maximum=1, 
                      step=0.1,
                      value=0.8, 
                      label="Temperature", 
                      render=False),
            gr.Slider(minimum=128, 
                      maximum=4096,
                      step=1,
                      value=4096, 
                      label="Max new tokens", 
                      render=False ),
            gr.Slider(minimum=0,
                      maximum=1,
                      step=0.1,
                      value=0.9,
                      label="Top_p",
                      render=False),
            gr.Textbox(lines=2,
                       placeholder="Enter system prompt here...",
                       label="System Prompt",
                       render=False),
            ],
        examples=[
            ['Who Are you?']
            
            ],
        cache_examples=False,
                     )
    
if __name__ == "__main__":
    aida.launch(share=True)