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from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import os
import requests
# from langchain.llms.huggingface_pipeline import HuggingFacePipeline
key = os.environ.get("huggingface_key")
openai_api_key = os.environ.get("openai_key")
app = FastAPI(openapi_url="/api/v1/LLM/openapi.json", docs_url="/api/v1/LLM/docs")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
allow_credentials=True,
)
# API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
# headers = {"Authorization": f"Bearer {key}"}
# def query(payload):
# response = requests.post(API_URL, headers=headers, json=payload)
# return response.json()
def LLM(llm_name, length):
tokenizer = AutoTokenizer.from_pretrained(llm_name)
model = AutoModelForCausalLM.from_pretrained(llm_name)
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
max_length=length,
do_sample=True,
top_p=0.95,
repetition_penalty=1.2,
)
return pipe
# tokenizer = AutoTokenizer.from_pretrained("WizardLM/WizardCoder-1B-V1.0")
# base_model = AutoModelForCausalLM.from_pretrained("WizardLM/WizardCoder-1B-V1.0")
# Mistral 7B
mistral_llm = LLM("mistralai/Mistral-7B-v0.1",30000)
# WizardCoder 13B
wizard_llm = LLM("WizardLM/WizardCoder-Python-13B-V1.0",8000)
# hf_llm = HuggingFacePipeline(pipeline=pipe)
def ask_model(model, prompt):
if(model == 'mistral'):
return mistral_llm(prompt)
if(model == 'wizard'):
return wizard_llm(prompt)
@app.get("/")
def root():
return {"message": "R&D LLM API"}
# @app.get("/get")
# def get():
# result = pipe("name 5 programming languages",do_sample=False)
# print(result)
# return {"message": result}
@app.post("/ask_llm")
async def ask_llm_endpoint(model:str, prompt: str):
result = ask_model(model,prompt)
return {"result": result}
# APIs
# @app.post("/ask_HFAPI")
# def ask_HFAPI_endpoint(prompt: str):
# result = query(prompt)
# return {"result": result}
from langchain.llms import OpenAI
llm = OpenAI(model_name="text-davinci-003", temperature=0.5, openai_api_key=openai_api_key)
@app.post("/ask_GPT")
def ask_GPT_endpoint(prompt: str):
result = llm(prompt)
return {"result": result} |