import torch
import random
from quart import Quart
from transformers import AutoTokenizer, AutoModelForCausalLM

app = Quart(__name__)
tokenizer = AutoTokenizer.from_pretrained("OpenBuddy/openbuddy-openllama-3b-v10-bf16")
model = AutoModelForCausalLM.from_pretrained("OpenBuddy/openbuddy-openllama-3b-v10-bf16")
model.eval()

with open('../system.prompt', 'r', encoding='utf-8') as f:
    prompt = f.read()

@app.post("/request")
async def echo():
    data = await request.get_json()
    if data.get("max_tokens") != None and data.get("max_tokens") > 500: data['max_tokens'] = 500
    userPrompt = prompt + "\n\nUser: " + data['request'] + "\nAssistant: "
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    with torch.no_grad():
    output_ids = model.generate(
        input_ids=input_ids, 
        do_sample=random.choice([True, False]), temperature=float(random.randint(7,20)) / 10.0,
        max_new_tokens=data.get("max_tokens") or random.randomint(200,500), 
        eos_token_id=tokenizer.eos_token_id, return_full_text = False)
    return {"output": tokenizer.decode(output_ids[0], skip_special_tokens=True)}

@app.get("/")
async def get():
    return "better to run it on own container"