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import requests
import json
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
class EndpointHandler():
def __init__(self, path=""):
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
self.torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
self.model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", torch_dtype=self.torch_dtype, trust_remote_code=True).to(self.device)
self.processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)
def predict_image(self, url, task, prompt):
image = Image.open(requests.get(url, stream=True).raw)
inputs = self.processor(text=task + prompt, images=image, return_tensors="pt").to(self.device, self.torch_dtype)
generated_ids = self.model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=4096,
num_beams=3,
do_sample=False
)
generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = self.processor.post_process_generation(generated_text, task=task, image_size=(image.width, image.height))
return parsed_answer
def __call__(self, event):
if "inputs" not in event:
return {
"statusCode": 400,
"body": json.dumps("Error: Please provide an 'inputs' parameter."),
}
inputs = event["inputs"]
url = inputs["url"]
task = inputs["task"]
prompt = inputs["prompt"]
parsed_answer = self.predict_image(url, task, prompt)
return {
"statusCode": 200,
"body": json.dumps(parsed_answer),
} |