TypeError: zip argument #2 must support iteration

[mbcse]

        from keras.preprocessing.image import img_to_array
        import imutils
        import cv2
        from keras.models import load_model
        import numpy as np
        import time
        

        # parameters for loading data and images
        detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
        emotion_model_path = 'models/_mini_XCEPTION.102-0.66.hdf5'

        # hyper-parameters for bounding boxes shape
        # loading models
        face_detection = cv2.CascadeClassifier(detection_model_path)
        emotion_classifier = load_model(emotion_model_path, compile=False)
        EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised",
        "neutral"]
         
        dict_idk = {"angry": 0, "disgust": 0, "scared": 0,
        "happy": 0, "sad": 0, "surprised": 0, "neutral": 0}
        li = [] 

        #feelings_faces = []
        #for index, emotion in enumerate(EMOTIONS):
        # feelings_faces.append(cv2.imread('emojis/' + emotion + '.png', -1))

        # starting video streaming
        cv2.namedWindow('your_face')
        camera = cv2.VideoCapture(0)
        start_time=time.time()
        count=0
        while True:
            preds=1
            frame = camera.read()[1]
            #reading the frame
            frame = imutils.resize(frame,width=300)
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            faces = face_detection.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE)
            
            canvas = np.zeros((250, 300, 3), dtype="uint8")
            frameClone = frame.copy()
            if len(faces) > 0:
                faces = sorted(faces, reverse=True,
                key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
                (fX, fY, fW, fH) = faces
                            # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
                    # the ROI for classification via the CNN
                roi = gray[fY:fY + fH, fX:fX + fW]
                roi = cv2.resize(roi, (64, 64))
                roi = roi.astype("float") / 255.0
                roi = img_to_array(roi)
                roi = np.expand_dims(roi, axis=0)
                
                
                preds = emotion_classifier.predict(roi)[0]
                
                emotion_probability = np.max(preds)
                label = EMOTIONS[preds.argmax()]

            
            for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
                        
                        # construct the label text
                        text = "{}: {:.2f}%".format(emotion, prob * 100)
                        li.append((emotion, prob))
                        # draw the label + probability bar on the canvas
                    # emoji_face = feelings_faces[np.argmax(preds)]

                        
                        w = int(prob * 300)
                        cv2.rectangle(canvas, (7, (i * 35) + 5),
                        (w, (i * 35) + 35), (0, 0, 255), -1)
                        cv2.putText(canvas, text, (10, (i * 35) + 23),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45,
                        (255, 255, 255), 2)
                        cv2.putText(frameClone, label, (fX, fY - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
                        cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
                                    (0, 0, 255), 2)
        #    for c in range(0, 3):
        #        frame[200:320, 10:130, c] = emoji_face[:, :, c] * \
        #        (emoji_face[:, :, 3] / 255.0) + frame[200:320,
        #        10:130, c] * (1.0 - emoji_face[:, :, 3] / 255.0)

            
            cv2.imshow('your_face', frameClone)
            cv2.imshow("Probabilities", canvas)
            count+=1
            if (cv2.waitKey(1) or ((time.time()-start_time))>=6000000000000000000000000000000000000000):
                print("STOPING VIDEO ANALYSIS")
                break
        for i, j in li:
            dict_idk[i] += j
        camera.release()
        cv2.destroyAllWindows()
        return dict_idk

for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
TypeError: zip argument #2 must support iteration