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import base64
import io
from typing import Dict, Any
import torch
from PIL import Image
from transformers import AutoProcessor, VisionEncoderDecoderModel
class EndpointHandler:
def __init__(self, path=""):
# Load processor and model from the provided path or model ID
self.processor = AutoProcessor.from_pretrained(path or "bytedance/Dolphin")
self.model = VisionEncoderDecoderModel.from_pretrained(path or "bytedance/Dolphin")
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
self.model.eval()
self.model = self.model.half() # Half precision for speed
self.tokenizer = self.processor.tokenizer
def decode_base64_image(self, image_base64: str) -> Image.Image:
image_bytes = base64.b64decode(image_base64)
return Image.open(io.BytesIO(image_bytes)).convert("RGB")
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
# Check for image input
if "inputs" not in data:
return {"error": "No inputs provided"}
image_input = data["inputs"]
# Support both base64 image strings and raw images (Hugging Face supports both)
if isinstance(image_input, str):
try:
image = self.decode_base64_image(image_input)
except Exception as e:
return {"error": f"Invalid base64 image: {str(e)}"}
else:
image = image_input # Assume PIL-compatible image
# Optional: Custom prompt (default: text reading)
prompt = data.get("prompt", "Read text in the image.")
full_prompt = f"<s>{prompt} <Answer/>"
# Preprocess inputs
inputs = self.processor(image, return_tensors="pt")
pixel_values = inputs.pixel_values.half().to(self.device)
prompt_ids = self.tokenizer(full_prompt, add_special_tokens=False, return_tensors="pt").input_ids.to(self.device)
decoder_attention_mask = torch.ones_like(prompt_ids).to(self.device)
# Inference
outputs = self.model.generate(
pixel_values=pixel_values,
decoder_input_ids=prompt_ids,
decoder_attention_mask=decoder_attention_mask,
min_length=1,
max_length=4096,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id,
use_cache=True,
bad_words_ids=[[self.tokenizer.unk_token_id]],
return_dict_in_generate=True,
do_sample=False,
num_beams=1,
)
sequence = self.tokenizer.batch_decode(outputs.sequences, skip_special_tokens=False)[0]
# Clean up
generated_text = sequence.replace(full_prompt, "").replace("<pad>", "").replace("</s>", "").strip()
return {"text": generated_text} |