Spaces:
Sleeping
Sleeping
File size: 1,268 Bytes
1ea0f2c |
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 |
from flask import Flask, request, jsonify
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load Meta Sapiens Pose model
sapiens_model = torch.jit.load('/models/sapiens_pose/model.pt')
sapiens_model.eval()
# Load MotionBERT model
motionbert_model = AutoModelForSequenceClassification.from_pretrained('/models/motionbert')
motionbert_tokenizer = AutoTokenizer.from_pretrained('/models/motionbert')
app = Flask(__name__)
@app.route('/pose_estimation', methods=['POST'])
def pose_estimation():
# Accept an image file as input for pose estimation
image = request.files['image'].read()
# Perform pose estimation
with torch.no_grad():
pose_result = sapiens_model(torch.tensor(image))
return jsonify({"pose_result": pose_result.tolist()})
@app.route('/sequence_analysis', methods=['POST'])
def sequence_analysis():
# Accept keypoint data as input for sequence analysis
keypoints = request.json['keypoints']
inputs = motionbert_tokenizer(keypoints, return_tensors="pt")
with torch.no_grad():
sequence_output = motionbert_model(**inputs)
return jsonify({"sequence_analysis": sequence_output.logits.tolist()})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860)
|