import torch
from PIL import Image
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, GemmaTokenizerFast, TextIteratorStreamer,BitsAndBytesConfig
import os
from threading import Thread


HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = "google/gemma-2-27b-it"
MODELS = os.environ.get("MODELS")
MODEL_NAME = MODELS.split("/")[-1]
MAX_INPUT_TOKEN_LENGTH = int(os.environ.get("MAX_INPUT_TOKEN_LENGTH", "4096"))

TITLE = "<h1><center>Qwen2-Chatbox</center></h1>"

DESCRIPTION = f"""
<h3>MODEL: <a href="https://hf.co/{MODELS}">{MODEL_NAME}</a></h3>
<center>
<p>Gemma is the large language model built by Google.
<br>
Feel free to test without log.
</p>
</center>
"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
"""

model = AutoModelForCausalLM.from_pretrained(
    MODELS,
    device_map="auto",
    quantization_config=BitsAndBytesConfig(load_in_4bit=True)
        )
tokenizer = GemmaTokenizerFast.from_pretrained(MODELS)
model.config.sliding_window = 4096
model.eval()

@spaces.GPU(duration=90)
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float):
    print(f'message is - {message}')
    print(f'history is - {history}')
    conversation = []
    for prompt, answer in history:
        conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
    conversation.append({"role": "user", "content": message})

    print(f"Conversation is -\n{conversation}")
    
    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(0)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        top_k=top_k,
        top_p=top_p,
        repetition_penalty=penalty,
        max_new_tokens=max_new_tokens, 
        do_sample=True, 
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    
    thread = Thread(target=model.generate, kwargs=generate_kwargs)
    thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer



chatbot = gr.Chatbot(height=600)

with gr.Blocks(css=CSS, theme="soft") as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        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=2048,
                step=1,
                value=1024,
                label="Max new tokens",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=0.8,
                label="top_p",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=20,
                step=1,
                value=20,
                label="top_k",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.0,
                label="Repetition penalty",
                render=False,
            ),
        ],
        examples=[
            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
            ["Tell me a random fun fact about the Roman Empire."],
            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
        ],
        cache_examples=False,
    )


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