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import os
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
from langchain_openai import OpenAI
from huggingface_hub import login
from dotenv import load_dotenv
from logging import getLogger
import streamlit as st
import torch
load_dotenv()
hf_token = os.environ.get("HF_TOKEN")
# hf_token = st.secrets["HF_TOKEN"]
login(token=hf_token)
logger = getLogger(__name__)
device = "cuda" if torch.cuda.is_available() else "cpu"
def get_local_model(model_name_or_path:str)->pipeline:
#print(f"Model is running on {device}")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
pipe = pipeline(
task = 'summarization',
model=model,
tokenizer=tokenizer,
device = device,
)
logger.info(f"Summarization pipeline created and loaded to {device}")
return pipe
def get_endpoint(api_key:str):
llm = OpenAI(openai_api_key=api_key)
return llm
def get_model(model_type,model_name_or_path,api_key = None):
if model_type == "openai":
return get_endpoint(api_key)
else:
return get_local_model(model_name_or_path)
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