import streamlit as st
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing import image
import numpy as np

# Check TensorFlow version
print("TensorFlow version: ", tf.__version__)

# Sidebar for file upload
st.sidebar.title("Upload Image")
uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg"])

if uploaded_file is not None:
    # Display the uploaded image
    st.image(uploaded_file, caption='Uploaded Image.', use_column_width=True)
    st.write("")
train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
training_set = train_datagen.flow_from_directory('dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')
test_datagen = ImageDataGenerator(rescale = 1./255)
test_set = test_datagen.flow_from_directory('dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')
cnn=tf.keras.models.Sequential()
cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu',input_shape=[64,64,3]))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu'))
cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))
cnn.add(tf.keras.layers.Flatten())
cnn.add(tf.keras.layers.Dense(units=128,activation='relu'))
cnn.add(tf.keras.layers.Dense(units=1,activation='sigmoid'))
cnn.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
cnn.fit(x=training_set,validation_data=test_set,epochs=25)
import numpy as np
from keras.preprocessing import image
test_image=image.load_img('dataset/single_prediction/cat_or_dog_1.jpg',target_size=(64,64))
test_image=image.img_to_array(test_image)
test_image=np.expand_dims(test_image,axis=0)
result=cnn.predict(test_image)
training_set.class_indices
if result[0][0]==1:
  prediction='dog'
else:
  prediction='cat'
print(prediction)