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	from flask import Flask, request, jsonify
	import os
	import tensorflow as tf
	import tensorflow_hub as hub
	import numpy as np
	import cv2

	app = Flask(__name__)

	# Define constants or parameters
	min_kick_angle = 30 # Minimum angle for the leg to be considered a kick
	frame_window = 10 # Number of frames to consider for action recognition
	kick_counter = 0
	highest_kick_frame = -1 # Initialize the frame number of the highest kick
	highest_kick_knee = None # Initialize coordinates of the knee for the highest kick
	highest_kick_hip = None # Initialize coordinates of the hip for the highest kick

	# Initialize variables for action recognition
	frame_buffer = []

	# Load the MoveNet model for pose estimation from TensorFlow Hub
	model = hub.load("https://tfhub.dev/google/movenet/singlepose/thunder/4")
	pose_net = model.signatures['serving_default']

	# Define upload folder for video files
	UPLOAD_FOLDER = 'uploads'
	ALLOWED_EXTENSIONS = {'mp4'}
	app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

	# Function to detect front kick based on keypoints
	def detect_front_kick_func(keypoints, frame_number):
	keypoints_array = keypoints[0] # Get the NumPy array from the list

	right_hip = keypoints_array[0, 0, 8, :] # Right hip is at index 8
	right_knee = keypoints_array[0, 0, 9, :] # Right knee is at index 9

	# print(right_hip, ' ', right_knee)
	if right_knee[2] < 0.4 and right_hip[2] < 0.4:
	return False, -1, None, None

	angle = np.arctan2(right_knee[1] - right_hip[1], right_knee[0] - right_hip[0]) * 180 / np.pi

	if angle > min_kick_angle:
	return True, frame_number, right_knee, right_hip
	else:
	return False, -1, None, None

	def allowed_file(filename):
	return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS


	@app.route('/detect_front_kick', methods=['POST'])
	def detect_front_kick():
	try:
	# Check if the 'video' field is in the request
	if 'video' not in request.files:
	return jsonify({'error': 'No video file provided'})

	video_file = request.files['video']

	# Check if the file has the allowed extension
	if not allowed_file(video_file.filename):
	return jsonify({'error': 'Invalid file format. Only MP4 videos are allowed.'})

	# Save the video file to the upload folder with a secure name
	video_filename = (video_file.filename)
	video_filepath = os.path.join(app.config['UPLOAD_FOLDER'], video_filename)
	video_file.save(video_filepath)

	# Open the video file for processing
	cap = cv2.VideoCapture(video_filepath)

	# Check if the video file was opened successfully
	if not cap.isOpened():
	return jsonify({'error': 'Failed to open video file.'})

	frame_number = 0 # Initialize frame number

	while True:
	ret, frame = cap.read()

	if not ret:
	break

	# Preprocess the frame (resize, normalize, denoise, etc.)

	# Perform pose estimation using MoveNet
	resized_frame = cv2.resize(frame, (256, 256))
	image = tf.constant(resized_frame, dtype=tf.int32)
	image = tf.expand_dims(image, axis=0)

	# Run model inference
	outputs = pose_net(image)
	keypoints = outputs['output_0'].numpy()

	# Append the keypoints to the frame buffer
	frame_buffer.append(keypoints)

	# Maintain a sliding window of frames for action recognition
	if len(frame_buffer) > frame_window:
	frame_buffer.pop(0)

	# Perform action recognition using the frame buffer
	if len(frame_buffer) == frame_window:
	is_kick, frame_with_kick, knee, hip = detect_front_kick_func(frame_buffer, frame_number)

	if is_kick:
	kick_counter += 1
	if frame_with_kick > highest_kick_frame:
	highest_kick_frame = frame_with_kick
	highest_kick_knee = knee
	highest_kick_hip = hip

	frame_number += 1

	cap.release()

	response_data = {
	'kick_counter': kick_counter,
	'highest_kick_frame': highest_kick_frame,
	'highest_kick_knee': highest_kick_knee.tolist() if highest_kick_knee is not None else None,
	'highest_kick_hip': highest_kick_hip.tolist() if highest_kick_hip is not None else None,
	}

	return jsonify(response_data)

	except Exception as e:
	return jsonify({'error': str(e)})

	@app.route('/home', methods=['GET'])
	def homie():
	return jsonify({"message":"none"})


	if __name__ == '__main__':
	app.run(debug=True)