from sklearn import tree
import file_manager as fm
import base64
import base64

def bs64(img):
    with open(img,'rb') as imgfile:
        bs64form = base64.b64encode(imgfile.read()) #turning image file into string with base64
        return bs64form

def b64_to_int_lst(bs64_str):
    img_int_lst = []

    for i in range(len(bs64_str)):
        img_int_lst.append( ord( str(str(bs64_str)[i]) ) ) #character to integer conversion & adding to list

    return img_int_lst


x = []
y = []

imgs_names_str = (fm.read('img_names.txt'))
imgs_names_lst = imgs_names_str.split(',')

person_names_str = (fm.read('prsn_names.txt'))
person_names_lst = imgs_names_str.split(',')

y = person_names_lst

for i in range(len(imgs_names_lst)):
    img = imgs_names_lst[i]
    img_64str = bs64(img)

    lst = b64_to_int_lst(img_64str)
    x.append(lst)


clf = tree.DecisionTreeClassifier()

clft = clf.fit(x,y)

prd_img_nm = input('please enter image name. if in subfolder, please include that as well.')
prd_img = b64_to_int_lst(bs64(prd_img_nm))


pred = clft.predict(prd_img)

print(pred)