from fastapi import FastAPI
from pydantic import BaseModel
import requests
from ctransformers import AutoModelForCausalLM

llms = {
  "TinyLLama 1b 4_K_M 2048": {
    "nctx": 2048, 
    "file": "tinyllama-1.1b-chat-v0.3.Q4_K_M.gguf", 
    "prefix": "### Human:", 
    "suffix": "### Assistant:"
  },
  "TinyLLama 1b OpenOrca 4_K_M 2048": {
    "nctx": 2048, 
    "file": "tinyllama-1.1b-1t-openorca.Q4_K_M.gguf", 
    "prefix": "<|im_start|>system You are a helpfull assistant<|im_end|><|im_start|>user", 
    "suffix": "<|im_end|><|im_start|>assistant"
  },
  "OpenLLama 3b 4_K_M 196k": {
    "nctx": 80000, 
    "file": "open-llama-3b-v2-wizard-evol-instuct-v2-196k.Q4_K_M.gguf", 
    "prefix": "### HUMAN:", 
    "suffix": "### RESPONSE:"
  },
  "Phi-2 2.7b 4_K_M 2048": {
    "nctx": 2048, 
    "file": "phi-2.Q4_K_M.gguf", 
    "prefix": "Instruct:", 
    "suffix": "Output:"
  },
  "Mixtral MOE 7bx2 4_K_M 32K": {
    "nctx": 32000, 
    "file": "mixtral_7bx2_moe.Q4_K_M.gguf", 
    "prefix": "", 
    "suffix": ""
  },
  "Stable Zephyr 3b 4_K_M 4096": {
    "nctx": 4096, 
    "file": "stablelm-zephyr-3b.Q4_K_M.gguf", 
    "prefix": "<|user|>", 
    "suffix": "<|endoftext|><|assistant|>"
  }
}

#Pydantic object
class validation(BaseModel):
    prompt: str
    llm: str
    
#Fast API
app = FastAPI()

@app.post("/llm_on_cpu")
async def stream(item: validation):

    model = llms[item.llm]
    prefix=model['prefix']
    suffix=model['suffix']
    nctx = model['nctx'] if 'nctx' in item.keys() else 1024
    max_tokens = model['max_tokens'] if 'max_tokens' in item.keys() else 512
    user="""
    {prompt}"""
    
    model = Llama(model_path="./"+model['file'], n_ctx=model['nctx'], verbose=False,  n_threads=8)

    prompt = f"{prefix}{user.replace('{prompt}', item.prompt)}{suffix}"
    return llm(prompt, max_tokens=max_tokens)