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import argparse
from flask import Flask, request, jsonify
from PIL import Image
from io import BytesIO
import base64
import torch
from transformers import AutoProcessor, AutoModelForCausalLM
import threading
app = Flask(__name__)
# Parse command line arguments
parser = argparse.ArgumentParser(description='Start the Flask server with specified model and device.')
parser.add_argument('--model-path', type=str, default="models/Florence-2-base-ft", help='Path to the pretrained model')
parser.add_argument('--device', type=str, choices=['cpu', 'gpu'], default='auto', help='Device to use: "cpu", "gpu", or "auto"')
args = parser.parse_args()
# Determine the device
if args.device == 'auto':
device = "cuda:0" if torch.cuda.is_available() else "cpu"
elif args.device == 'gpu':
if torch.cuda.is_available():
device = "cuda:0"
else:
raise ValueError("GPU option specified but no GPU is available.")
else:
device = "cpu"
torch_dtype = torch.float16 if device.startswith("cuda") else torch.float32
# Initialize the model and processor
model = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype=torch_dtype, trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained(args.model_path, trust_remote_code=True)
lock = threading.Lock() # Use a lock to ensure thread safety when accessing the model
def predict_image(image, task: str = "<OD>", prompt: str = None):
prompt = task + " " + prompt if prompt else task
print(f"Prompt: {prompt}")
with lock:
inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
do_sample=False,
num_beams=3
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(generated_text, task=task, image_size=(image.width, image.height))
return parsed_answer
@app.route('/predict', methods=['POST'])
def predict():
if request.is_json:
data = request.get_json()
if 'image' not in data:
return jsonify({'error': 'No image found in JSON'}), 400
image_data = base64.b64decode(data['image'].split(",")[1])
image = Image.open(BytesIO(image_data))
else:
return jsonify({'error': 'No image file or JSON payload'}), 400
task = data.get('task', "<OD>")
prompt = data.get('prompt', None)
prediction = predict_image(image, task, prompt)
msgid = data.get('msgid', None)
response = {
'msgid': msgid,
'prediction': prediction
}
return jsonify(response)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000, threaded=True) # Enable multi-threading
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