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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
# FROM: https://huggingface.co/facebook/blenderbot-400M-distill?text=Hey+my+name+is+Thomas%21+How+are+you%3F
# 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")
# 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")
token_size_limit = 128
app = FastAPI()
@app.post('/reply')
async def Reply(req: Request):
request = await req.json()
msg = request['msg']
print(f'MSG: {msg}')
input_ids = tokenizer(msg, return_tensors='pt').input_ids # .to('cuda')
output = model.generate(
input_ids[:, -token_size_limit:],
do_sample=True,
temperature=0.9,
max_length=100,
)
reply = tokenizer.batch_decode(output)[0]
print(f'REPLY: {reply}')
return {'reply': reply}
@app.get("/")
def read_root():
return {"Hello": "World!"}
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