app.py

from flask import Flask, jsonify, make_response
import pickle
import cv2
import mediapipe as mp
import numpy as np

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello World!"

@app.route('/predict')
def predict():
    model_dict = pickle.load(open('AutoTalk\model.p', 'rb'))
    model = model_dict['model']

    cap = cv2.VideoCapture(0)

    mp_hands = mp.solutions.hands
    mp_drawing = mp.solutions.drawing_utils
    mp_drawing_styles = mp.solutions.drawing_styles

    hands = mp_hands.Hands(static_image_mode=True, min_detection_confidence=0.3)

    labels_dict = {
        0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J',
        10: 'K', 11: 'L', 12: 'M', 13: 'N', 14: 'O', 15: 'P', 16: 'Q', 17: 'R', 18: 'S',
        19: 'T', 20: 'U', 21: 'V', 22: 'W', 23: 'X', 24: 'Y', 25: 'Z', 26: "I Love You",
        27: "Hello", 28: "Thank You", 29: "Please", 30: "Me", 31: "You", 32: "Pizza",
        33: "Now", 34: "Excuse", 35: "Help", 36: "Who", 37: "What", 38: "Where", 
        39: "Yes", 40: "No", 41: "His", 42: "Can", 43: "Close the Window", 44: "Dad"
    }

    while True:

        data_aux = []
        x_ = []
        y_ = []

        ret, frame = cap.read()

        H, W, _ = frame.shape

        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

        results = hands.process(frame_rgb)
        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                mp_drawing.draw_landmarks(
                    frame,  # image to draw
                    hand_landmarks,  # model output
                    mp_hands.HAND_CONNECTIONS,  # hand connections
                    mp_drawing_styles.get_default_hand_landmarks_style(),
                    mp_drawing_styles.get_default_hand_connections_style())

            for hand_landmarks in results.multi_hand_landmarks:
                for i in range(len(hand_landmarks.landmark)):
                    x = hand_landmarks.landmark[i].x
                    y = hand_landmarks.landmark[i].y

                    x_.append(x)
                    y_.append(y)

                for i in range(len(hand_landmarks.landmark)):
                    x = hand_landmarks.landmark[i].x
                    y = hand_landmarks.landmark[i].y
                    data_aux.append(x - min(x_))
                    data_aux.append(y - min(y_))

            x1 = int(min(x_) * W) - 10
            y1 = int(min(y_) * H) - 10

            x2 = int(max(x_) * W) - 10
            y2 = int(max(y_) * H) - 10

            prediction = model.predict([np.asarray(data_aux[0:42])])
            print(prediction)

            predicted_word = labels_dict[int(prediction[0])]

            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 0), 4)
            cv2.putText(frame, predicted_word, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 0, 0), 3,
                        cv2.LINE_AA)

        cv2.imshow('frame', frame)
        cv2.waitKey(1)

    cap.release()
    cv2.destroyAllWindows()

@app.route('/why')
def why_are_you_here():
    # return jsonify({"promptText": "This is a prompt from the Flask server!"})
    return make_response("this ! test ! 2", 200)


if __name__ == "__main__":
    # app.run()
    predict()