# # %% [markdown]
# # # Prediction on the test data

# # %%
import os
 
import pandas as pd
import numpy as np
import tensorflow as tf


model = tf.keras.models.load_model('model_pred/sign_char_detection_model')

# Get the list of all files and directories
path = "test_data"
dir_list = os.listdir(path)
 
print(dir_list)

# %%
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
tf.keras.utils.load_img

class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'del', 'nothing', 'space']

img_height=256
img_width=256
actual=[]
pred=[]
for i in dir_list:
    for j in os.listdir(path+'/'+i):
        file_path = path+'/'+i + '/' + j
        actual.append(i)
        test_image = tf.keras.utils.load_img(file_path, target_size = (200, 200))
        test_image = tf.keras.utils.img_to_array(test_image)
        test_image = np.expand_dims(test_image, axis = 0)
        result = model.predict(test_image)
        pred.append(class_names[np.argmax(result)])

from sklearn.metrics import confusion_matrix, classification_report
from sklearn.metrics import accuracy_score

print("Test accuracy=",accuracy_score(pred,actual))
print("Classification report:\n",classification_report(pred,actual))


# %%