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import base64
import subprocess
from tempfile import NamedTemporaryFile
from fastapi import FastAPI, HTTPException
from PIL import Image
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
app = FastAPI()
# define request body
class RequestData(BaseModel):
prompt: str
image: str
def load_model():
model_id = "vikhyatk/moondream2"
revision = "2024-08-26"
model = AutoModelForCausalLM.from_pretrained(
model_id, trust_remote_code=True, revision=revision
)
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
return model, tokenizer
MODEL, TOKENIZER = load_model()
print("INFO: Model loaded successfully!")
@app.get("/")
def greet_json():
return {"Hello": "World!"}
@app.post("/query")
def query(data: RequestData):
prompt = data.prompt
image = data.image
try:
# decode base64 to image
image = base64.b64decode(image)
with NamedTemporaryFile(delete=True, suffix=".png") as temp_image:
temp_image.write(image)
temp_image.flush()
image = Image.open(temp_image.name)
enc_image = MODEL.encode_image(image)
response = MODEL.answer_question(enc_image, str(prompt), TOKENIZER)
return {"response": str(response)}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
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