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"""Model hosted on Hugging face.
Based on: https://huggingface.co/docs/hub/spaces-sdks-docker-first-demo
"""
from fastapi import FastAPI, Request
# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# from transformers import T5Tokenizer, T5ForConditionalGeneration
import gpt4free
from gpt4free import Provider, forefront
token_size_limit = None
# FROM: https://huggingface.co/facebook/blenderbot-400M-distill?text=Hey+my+name+is+Thomas%21+How+are+you%3F
# LAST USED
# tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
# model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
# tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-1B-distill")
# model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-1B-distill")
# token_size_limit = 128
# T5 model can use "any" sequence lenghth, but memory usage is O(L^2).
# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
token_size_limit = 512
# Too large for 16GB
# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl")
app = FastAPI()
# { msg: string, temperature: float, max_length: number }
@app.post('/reply')
async def Reply(req: Request):
request = await req.json()
msg = request.get('msg')
print(f'MSG: {msg}')
# Hugging face
# input_ids = tokenizer(msg, return_tensors='pt').input_ids # .to('cuda')
# output = model.generate(
# input_ids[:, -token_size_limit:],
# do_sample=True,
# temperature=request.get('temperature', 0.9),
# max_length=request.get('max_length', 100),
# )
# reply = tokenizer.batch_decode(output)[0]
# gpt4free
# usage theb
reply = gpt4free.Completion.create(Provider.Theb, prompt=msg)
print(f'REPLY: {reply}')
return {'reply': reply}
@app.get("/")
def read_root():
return {"Hello": "World!"}
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