main.py

from telegram.ext import *
from io import BytesIO
import cv2
import numpy as np
import tensorflow as tf

with open('token.txt', 'r') as f:
    TOKEN = f.read()

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train, x_test = x_train / 255, x_test / 255

class_names = ['Plane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Conv2D(512, (3, 3), activation='relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))


def start(update, context):
    update.message.reply_text('Welcome')



def help(update, context):
    update.message.reply_text("""
    /start - Starts conversation 
    /help - Show this message
    /train - Trains neural network  
    """)


def train(update, context):
    update.message.reply_text('Model is being trained...')
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
    model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))
    model.save('cifar_classifier.model)')
    update.message.reply_text('Done! You can now send a photo to classify!')


def handle_message(update, context):
    # update.message.reply_text('Please train the model and send the picture!')
    update.message.reply_text('Please send the picture!')


def handle_photo(update, context):
    file = context.bot.get_file(update.message.photo[-1].file_id)
    f = BytesIO(file.download_as_bytearray())
    file_bytes = np.asarray(bytearray(f.read()), dtype=np.uint8)

    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    img = cv2.resize(img, (32, 32), interpolation=cv2.INTER_AREA)

    prediction = model.predict(np.array([img/255]))
    update.message.reply_text(f'In this image I see a {class_names[np.argmax(prediction)]}')

updater = Updater(TOKEN, use_context=True)
dp = updater.dispatcher

dp.add_handler(CommandHandler('start', start))
dp.add_handler(CommandHandler('help', help))
dp.add_handler(CommandHandler('train', train))
dp.add_handler(MessageHandler(Filters.text, handle_message))
dp.add_handler(MessageHandler(Filters.photo, handle_photo))

updater.start_polling()
updater.idle()