File size: 1,573 Bytes
0026ff3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
from typing import Dict, List
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
class EndpointHandler():
def __init__(self, path=""):
# Load FLAN-T5 model and tokenizer
self.model_name = "google/flan-t5-large"
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.model = AutoModelForSeq2SeqLM.from_pretrained(self.model_name)
# Enable evaluation mode
self.model.eval()
def __call__(self, data: Dict) -> List[Dict]:
# Get input text
inputs = data.pop("inputs", data)
# Ensure inputs is a list
if isinstance(inputs, str):
inputs = [inputs]
# Tokenize inputs
tokenized = self.tokenizer(
inputs,
padding=True,
truncation=True,
max_length=512,
return_tensors="pt"
)
# Perform inference
with torch.no_grad():
outputs = self.model.generate(
tokenized.input_ids,
max_length=512,
min_length=50,
temperature=0.9,
top_p=0.95,
top_k=50,
do_sample=True,
num_return_sequences=1
)
# Decode the generated responses
responses = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
# Format output
results = [{"generated_text": response} for response in responses]
return results |