Create app.py

app.py

@@ -0,0 +1,34 @@
+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)