--- a
+++ b/Projects/NCS1/Server.py
@@ -0,0 +1,124 @@
+############################################################################################
+#
+# Project:       Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project
+# Repository:    ALL Detection System 2019
+# Project:       Facial Authentication Server
+#
+# Author:        Adam Milton-Barker (AdamMiltonBarker.com)
+# Contributors:
+# Title:         Server Class
+# Description:   Server for the ALL Detection System 2019 Classifier.
+# License:       MIT License
+# Last Modified: 2020-07-21
+#
+############################################################################################
+
+import cv2, json, jsonpickle, os, sys, time
+
+import numpy as np
+
+from mvnc import mvncapi as mvnc
+from datetime import datetime
+from skimage.transform import resize
+
+from Classes.Helpers import Helpers
+from Classes.Movidius import Movidius
+
+from flask import Flask, request, Response
+
+
+class Server():
+    """ ALL Detection System 2019 Server Class
+
+    Server for the ALL Detection System 2019 Classifier.
+    """
+
+    def __init__(self):
+        """ Initializes the Server Class. """
+
+        self.Helpers = Helpers("Server")
+        self.confs = self.Helpers.confs
+
+        self.Movidius = Movidius()
+        self.Movidius.checkNCS()
+        self.Movidius.loadInception()
+
+        self.Helpers.logger.info(
+            "Server class initialization complete.")
+
+
+app = Flask(__name__)
+Server = Server()
+
+
+@app.route('/Inference', methods=['POST'])
+def Inference():
+    
+    if len(request.files) != 0:
+        img = np.fromstring(request.files['file'].read(), np.uint8)
+    else:
+        img = np.fromstring(request.data, np.uint8)
+
+    img = cv2.imdecode(img, cv2.IMREAD_UNCHANGED).astype(np.float32)
+
+    dx, dy, dz = img.shape
+    delta = float(abs(dy-dx))
+
+    if dx > dy:
+        img = img[int(0.5*delta):dx-int(0.5*delta), 0:dy]
+    else:
+        img = img[0:dx, int(0.5*delta):dy-int(0.5*delta)]
+
+    img = cv2.resize(img, (Server.Movidius.reqsize,
+                           Server.Movidius.reqsize))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    for i in range(3):
+        img[:, :, i] = (img[:, :, i] - Server.Movidius.mean) * \
+            Server.Movidius.std
+
+    detectionStart, detectionStart = Server.Helpers.timerStart()
+    Server.Helpers.logger.info("API detection started.")
+
+    Server.Movidius.ncsGraph.LoadTensor(img.astype(np.float16), 'user object')
+    output, userobj = Server.Movidius.ncsGraph.GetResult()
+
+    detectionClockEnd, difference, detectionEnd = Server.Helpers.timerEnd(
+        detectionStart)
+    Server.Helpers.logger.info("API detection ended taking " + str(difference))
+
+    top_inds = output.argsort()[::-1][:5]
+
+    if output[top_inds[0]] >= Server.confs["Classifier"]["InceptionThreshold"] and Server.Movidius.classes[top_inds[0]] == "1":
+        classification = "AML Positive"
+        message = "ALL detected with a confidence of " + \
+            str(output[top_inds[0]]) + " in " + str(difference)
+    elif output[top_inds[0]] >= Server.confs["Classifier"]["InceptionThreshold"] and Server.Movidius.classes[top_inds[0]] == "0":
+        classification = "AML Negative"
+        message = "ALL not detected with a confidence of " + \
+            str(output[top_inds[0]]) + " in " + str(difference)
+    elif output[top_inds[0]] <= Server.confs["Classifier"]["InceptionThreshold"] and Server.Movidius.classes[top_inds[0]] == "1":
+        classification = "AML Positive"
+        message = "ALL detected with a LOW confidence of " + \
+            str(output[top_inds[0]]) + " in " + str(difference)
+    elif output[top_inds[0]] <= Server.confs["Classifier"]["InceptionThreshold"] and Server.Movidius.classes[top_inds[0]] == "0":
+        classification = "AML Negative"
+        message = "ALL not detected with a LOW confidence of " + \
+            str(output[top_inds[0]]) + " in " + str(difference)
+
+    Server.Helpers.logger.info(message)
+
+    ServerResponse = jsonpickle.encode({
+        'Response': 'OK',
+        'Classification': classification,
+        'aClassification': Server.Movidius.classes[top_inds[0]],
+        'Confidence': str(output[top_inds[0]]),
+        'Message': message
+    })
+
+    return Response(response=ServerResponse, status=200, mimetype="application/json")
+
+
+if __name__ == "__main__":
+    app.run(host=Server.confs["Classifier"]["IP"],
+            port=Server.confs["Classifier"]["Port"])