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from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import torch
from io import BytesIO
import base64
class EndpointHandler:
def __init__(self, model_dir):
self.model_id = "zesquirrelnator/moondream2-FinetuneV3.1"
self.model = AutoModelForCausalLM.from_pretrained(self.model_id, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("vikhyatk/moondream2", revision="2024-05-20") # Tokenizer is still from the original repository
# Check if CUDA (GPU support) is available and then set the device to GPU or CPU
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def preprocess_image(self, encoded_image):
"""Decode and preprocess the input image."""
decoded_image = base64.b64decode(encoded_image)
img = Image.open(BytesIO(decoded_image)).convert("RGB")
return img
def __call__(self, data):
"""Handle the incoming request."""
try:
# Extract the inputs from the data
inputs = data.pop("inputs", data)
input_image = inputs['image']
question = inputs.get('question', "move to the red ball")
# Preprocess the image
img = self.preprocess_image(input_image)
# Perform inference
enc_image = self.model.encode_image(img).to(self.device)
answer = self.model.answer_question(enc_image, question, self.tokenizer)
# If the output is a tensor, move it back to CPU and convert to list
if isinstance(answer, torch.Tensor):
answer = answer.cpu().numpy().tolist()
# Create the response
response = {
"statusCode": 200,
"body": {
"answer": answer
}
}
return response
except Exception as e:
# Handle any errors
response = {
"statusCode": 500,
"body": {
"error": str(e)
}
}
return response |