Spaces:
Paused
Paused
File size: 1,711 Bytes
3917b67 2b1bf5b 3917b67 2b1bf5b 32a28e1 3917b67 6419a66 3917b67 450fdea 3917b67 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
from fastapi import FastAPI, Query
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
app = FastAPI()
checkpoint = "Qwen/Qwen2.5-VL-3B-Instruct"
min_pixels = 256*28*28
max_pixels = 1280*28*28
processor = AutoProcessor.from_pretrained(
checkpoint,
min_pixels=min_pixels,
max_pixels=max_pixels
)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
checkpoint,
torch_dtype=torch.bfloat16,
device_map="auto",
# attn_implementation="flash_attention_2",
)
@app.get("/")
def read_root():
return {"message": "API is live. Use the /predict endpoint."}
@app.get("/predict")
def predict(image_url: str = Query(...), prompt: str = Query(...)):
messages = [
{"role": "system", "content": "You are a helpful assistant with vision abilities."},
{"role": "user", "content": [{"type": "image", "image": image_url}, {"type": "text", "text": prompt}]},
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
).to(model.device)
with torch.no_grad():
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
output_texts = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
return {"response": output_texts[0]}
|