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from typing import Dict, List, Any
from tempfile import TemporaryDirectory
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
from PIL import Image
import torch
import requests
class EndpointHandler:
def __init__(self):
self.processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
device = 'gpu' if torch.cuda.is_available() else 'cpu'
model = LlavaNextForConditionalGeneration.from_pretrained(
"llava-hf/llava-v1.6-mistral-7b-hf",
torch_dtype=torch.float32 if device == 'cpu' else torch.float16,
low_cpu_mem_usage=True
)
model.to(device)
self.model = model
self.device = device
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `dict`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
inputs = data.pop("inputs", data)
if not inputs:
return f"Inputs not in payload got {data.keys()}"
# get additional date field0
prompt = inputs.pop("prompt", None)
image_url = inputs.pop("image", None)
if image_url is None:
return "You need to upload an image URL for LLaVA to work."
if prompt is None:
prompt = "Can you describe this picture focusing on specifics visual artifacts and ambiance (objects, colors, person, athmosphere..). Please stay concise only output keywords and concepts detected."
if not self.model:
return "Model was not initialized"
if not self.processor:
return "Processor was not initialized"
# Create a temporary directory
with TemporaryDirectory() as tmpdirname:
# Download the image
response = requests.get(image_url)
if response.status_code != 200:
return "Failed to download the image."
# Define the path for the downloaded image
image_path = f"{tmpdirname}/image.jpg"
with open(image_path, "wb") as f:
f.write(response.content)
# Open the downloaded image
with Image.open(image_path).convert("RGB") as image:
prompt = f"[INST] <image>\n{prompt} [/INST]"
inputs = self.processor(prompt, image, return_tensors="pt").to(self.device)
output = self.model.generate(**inputs, max_new_tokens=100)
clean = self.processor.decode(output[0], skip_special_tokens=True)
return clean |