uploaded site and api data

api and aux scripts/start.py

@@ -0,0 +1,60 @@
+# DEPENDENCIES ------>
+import json
+import pickle
+import numpy as np
+
+
+model_dict = pickle.load(open("./model.p", "rb"))
+model = model_dict["model"]
+labels_dict = {1: "hello", 2: "Nothing"}
+
+hands_data = json.load(open("./data.json", "r"))
+data_aux = []
+
+# hand landmarks are taken from data.json
+hand = hands_data[0]
+for landmark in hand:
+    x = landmark["x"]
+    y = landmark["y"]
+    z = landmark["z"]
+    data_aux.append(x)
+    data_aux.append(y)
+    data_aux.append(z)
+
+prediction = model.predict([np.asarray(data_aux)])
+print(prediction)
+predicted_sign = labels_dict[int(prediction[0])]
+print(predicted_sign)
+data_aux = []
+
+
+# if results.multi_hand_landmarks:
+#     for hand_landmarks in results.multi_hand_landmarks:
+#         mp_drawing.draw_landmarks(
+#             frame,
+#             hand_landmarks,
+#             mp_hands.HAND_CONNECTIONS,
+#             mp_drawing_styles.get_default_hand_landmarks_style(),
+#             mp_drawing_styles.get_default_hand_connections_style())
+#
+#         for i in range(len(hand_landmarks.landmark)):
+#             x = hand_landmarks.landmark[i].x
+#             y = hand_landmarks.landmark[i].y
+#             z = hand_landmarks.landmark[i].z
+#             data_aux.append(x)
+#             data_aux.append(y)
+#             data_aux.append(z)
+#             x_.append(x)
+#             y_.append(y)
+#         prediction = model.predict([np.asarray(data_aux)])
+#         predicted_sign = labels_dict[int(prediction[0])]
+#         data_aux = []
+#         x_ = []
+#         y_ = []
+#
+# cv2.imshow("Handy", frame)
+# cv2.waitKey(1)
+
+#
+# capture.release()
+# cv2.destroyAllWindows()