import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
import os
from threading import Thread
import spaces
import time

token = os.environ["HF_TOKEN"]

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

model = AutoModelForCausalLM.from_pretrained("google/gemma-1.1-7b-it", 
                                             quantization_config=quantization_config,
                                             token=token)
tok = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it", token=token)

if torch.cuda.is_available():
    device = torch.device('cuda')
    print(f"Using GPU: {torch.cuda.get_device_name(device)}")
else:
    device = torch.device('cpu')
    print("Using CPU")
    
model = model.to(device)
model = model.to_bettertransformer()

@spaces.GPU
def chat(message, history):
    start_time = time.time()
    chat = []
    for item in history:
        chat.append({"role": "user", "content": item[0]})
        if item[1] is not None:
            chat.append({"role": "assistant", "content": item[1]})
    chat.append({"role": "user", "content": message})
    messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
    model_inputs = tok([messages], return_tensors="pt").to(device)
    streamer = TextIteratorStreamer(
        tok, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=0.75,
        num_beams=1,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    partial_text = ""
    first_token_time = None
    for new_text in streamer:
        if not first_token_time:
            first_token_time = time.time() - start_time
        partial_text += new_text
        yield partial_text

    total_time = time.time() - start_time
    tokens = len(tok.tokenize(partial_text))
    tokens_per_second = tokens / total_time if total_time > 0 else 0

    # Append the timing information to the final output
    timing_info = f"\nTime taken to first token: {first_token_time:.2f} seconds\nTokens per second: {tokens_per_second:.2f}"
    yield partial_text + timing_info

demo = gr.ChatInterface(fn=chat, examples=[["Write me a poem about Machine Learning."]], title="Chat With LLMS")
demo.launch()