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)