# %% [markdown]
# # Import the required libraries

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

# %% [markdown]
# # Preprocessing the image data

# %%
#taking the train validation ratio as 4:1

# %%
batch_size=32
img_height=256
img_width=256


train_ds = tf.keras.utils.image_dataset_from_directory(
  "asl_alphabet_train/asl_alphabet_train/",
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

# %%
test_ds = tf.keras.utils.image_dataset_from_directory(
  "asl_alphabet_train/asl_alphabet_train/",
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

# %%
class_names = train_ds.class_names
print("Class names:",class_names)
print("Total classes:",len(class_names))

# %%
#random samples of images from the train data

# %%
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
  for i in range(29):
    ax = plt.subplot(6,5 , i + 1)
    plt.imshow(images[i].numpy().astype("uint8"))
    plt.title(class_names[labels[i]])
    plt.axis("off")

# %% [markdown]
# # Modelling and training

# %%
#modelling

from tensorflow.keras import Sequential
from tensorflow.keras import layers
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = Sequential([
  layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(29,activation='softmax')
])

# %%
model.summary()

# %%
model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

# %%
model.fit(train_ds, batch_size=128,validation_batch_size=128, validation_data=test_ds,epochs=20)


model.save('sign_car_detection_model')



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

# %%
import os
 
# Get the list of all files and directories
path = "asl_alphabet_test/asl_alphabet_test/"
dir_list = os.listdir(path)
 
print(dir_list)

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

actual=[]
pred=[]
for i in dir_list:
    actual.append(i.split('_')[0])
    test_image = tf.keras.utils.load_img('asl_alphabet_test/asl_alphabet_test/'+i, target_size = (256, 256))
    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))


# %%