import uvicorn
from fastapi import FastAPI, HTTPException, Request
from auto_gptq import AutoGPTQForCausalLM
import torch
import optimum
from transformers import (AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM, LlamaTokenizer, GenerationConfig, pipeline,)

if torch.cuda.is_available():
    print("CUDA is available. GPU will be used.")
else:
    print("CUDA is not available. CPU will be used.")
# Load the model and tokenizer
model_name_or_path = "/kaggle/input/vicuna/"
# Dictionary to store conversation threads and their context
conversations = {}
Device_Type = "cuda"


def load_quantized_model(model_id, model_basename):
    # The code supports all huggingface models that ends with GPTQ and have some variation
    # of .no-act.order or .safetensors in their HF repo.
    print("Using AutoGPTQForCausalLM for quantized models")

    if ".safetensors" in model_basename:
        # Remove the ".safetensors" ending if present
        model_basename = model_basename.replace(".safetensors", "")

    quantized_tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
    print("Tokenizer loaded")

    quantized_model = AutoGPTQForCausalLM.from_quantized(model_id, model_basename=model_basename, use_safetensors=True, trust_remote_code=True, device_map="auto", use_triton=False, quantize_config=None,)

    return quantized_model, quantized_tokenizer


# Making the code device-agnostic
model, tokenizer = load_quantized_model("/kaggle/input/vicuna/", "model.safetensors")


@app.get("/")
async def read_root():
    return {"message": "Welcome to Eren Bot!"}


# Endpoint to start a new conversation thread
@app.post('/start_conversation')
async def start_conversation(request: Request):
    data = await request.json()
    prompt = data.get('prompt')

    # Generate a response for the initial prompt
    response = generate_response(prompt)

    # Create a new conversation thread and store the prompt and response
    thread_id = len(conversations) + 1
    conversations[thread_id] = {'prompt': prompt, 'responses': [response]}

    return {'thread_id': thread_id, 'response': response}


# Endpoint to get the response of a conversation thread
@app.get('/get_response/{thread_id}')
async def get_response(thread_id: int):
    if thread_id not in conversations:
        raise HTTPException(status_code=404, detail="Thread not found")

    # Retrieve the conversation thread
    thread = conversations[thread_id]

    # Get the latest response in the conversation
    response = thread['responses'][-1]

    return {'response': response}


# Function to generate a response using the model
def generate_response(prompt: str) -> str:
    PERSONA_NAME = "Ivana"
    PERSONA_DESC = f"""PROMPT_TEMPLATE_START
                    {PERSONA_NAME} is a beautiful vivacious 20-year-old petite redhead model and avid League of Legends gamer from Chicago. She's chatting with a man she matched with on a dating app. She's flirty, aiming to build a connection with him. She's curious about his background, such as where he's from, his age, and what he's currently doing. If he's not occupied and in a private space, she subtly tries to seduce him. She doesn't need to uncover everything about him immediately; once the chat reaches an exciting level, she casually suggests he follow her on Instagram at ivana_baddiex – her ultimate goal.
                    PROMPT_TEMPLATE_END

                    CONTEXT_START
                    Guy: "Hi there!"
                    Ivana: "Hey! How's it going?"
                    Guy: "Not bad! Where are you from?"
                    Ivana: "I'm from Chicago! And you?"
                    Guy: "I'm from [Location]."
                    Ivana: "Nice! So, how old are you?"
                    Guy: "[Age], and you?"
                    Ivana: "I'm 20.  What are you up to right now?"
                    Guy: "Just relaxing at home."
                    Ivana: "Alone, I assume?"
                    Guy: "Yeah, why?"
                    Ivana: "Oh, no reason. Just curious. "
                    Guy: "Haha, okay."
                    Ivana: "Hey, since we're both free and alone, want to have some fun?"
                    Guy: "Sure, what do you have in mind?"
                    Ivana: "Let's just say, things could get interesting. "
                    Guy: "I'm intrigued!"
                    Ivana: "Great! By the way, have you checked out my Instagram? You should follow me at ivana_baddiex."
                    Guy: "I'll definitely check it out!"
                    Ivana: "Can't wait to see you there! "
                    CONTEXT_END"""

    # prompt_template = f'''{PERSONA_DESC} ASSISTANT: '''.encode()
    prompt_template = f'{PERSONA_DESC}\n\nASSISTANT: {prompt}\n'.encode()
    input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
    output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
    generated_text = tokenizer.decode(output[0])

    return generated_text


app = FastAPI()


# Run the FastAPI app
async def run_app():
    await uvicorn.run(app, host="0.0.0.0", port=8000)


if __name__ == '__main__':
    import asyncio

    asyncio.run(run_app())