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images_dir = "images"
import io
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from PIL import Image
import torch
torch.cuda.empty_cache()
from fastapi import FastAPI, File, Form,UploadFile,HTTPException
app=FastAPI()
def run_model(image,text_input):
torch.cuda.empty_cache()
model_id= "Qwen/Qwen2-VL-7B-Instruct-AWQ"
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_id , torch_dtype=torch.float16, device_map="cuda:0"
)
min_pixels = 256*28*28
max_pixels = 1280*28*28
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct-AWQ", min_pixels=min_pixels, max_pixels=max_pixels)
torch.cuda.empty_cache()
image_path = Image.open(image)
print(image_path)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
},
{"type": "text", "text": text_input},
],
}
]
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",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
torch.cuda.empty_cache()
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 output_text[0]
@app.post("/call_qwen_model")
async def call_model(file: UploadFile = File(...),json_str: str = Form(...)):
try:
request_object_content = await file.read()
img = io.BytesIO(request_object_content)
output = run_model(img, json_str)
return {"output": output}
except Exception as e :
raise HTTPException (f"Error: {e}") |