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from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from transformers import AutoProcessor, AutoModelForCausalLM
import torch
from PIL import Image
import io
import os
os.environ["HF_HOME"] = "/app/.cache"
os.environ["HF_DATASETS_CACHE"] = "/app/.cache"
os.environ["TRANSFORMERS_CACHE"] = "/app/.cache"
app = FastAPI()
MODEL_NAME = os.getenv("MODEL_NAME", "lmms-lab/LLaVA-OneVision-1.5-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME, torch_dtype="auto", device_map="auto", trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
@app.post("/analyze-image")
async def analyze_image(file: UploadFile = File(...), prompt: str = "Describe this image."):
image_bytes = await file.read()
image = Image.open(io.BytesIO(image_bytes))
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": prompt},
],
}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(
text=[text],
images=[image],
padding=True,
return_tensors="pt",
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return JSONResponse(content={"result": output_text[0]})
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