The Coffee-Cam: How Our Office Coffee Machine Learned to Talk

messages = [
    "🧼 I'm at the spa. It's called luxury. For you: cleaning mode.",
    "😶‍🌫️ Too much of your love is stuck on me. Let me scrub that off.",
    "🛁 Spa day for me. You guys take a break.",
    "😐 No, I'm not broken. I'm busy with a bubble bath.",
    "🧽 I'm cleaning myself. Why? Because you treat me like an animal.",
    "💤 Cleaning in progress. Please don't knock, I'm sensitive.",
    "🙃 Hygiene is important. That's what my therapist told me.",
    "🚿 I'm showering. You should try it too.",
    "🤖 Maintenance ongoing. Mood? Somewhere between resigned and soapy.",
    "🫣 I'm getting rid of your coffee sins. Don't look."
] if is_positive else [
    "☕ Ready for the next coffee. Wishing you a strong day!",
    "🧼 Freshly cleaned and ready to go. Let's do this – you're awesome!",
    "🔧 I'm back in shape. Time for your next energy boost.",
    "📎 System's running. This day is bound to go well, right?",
    "😌 Clean, ready – and in a peaceful coffee mood.",
    "☀️ Good morning! I'm ready. Are you?",
    "👋 Back online. I believe in you (and caffeine).",
    "🥸 Ready to brew. Let's do great things – or at least stay awake.",
    "🌞 Ready to brighten your day. Coffee coming right up.",
    "📈 All set. May your day run as smoothly as I do now."
]

from tensorflow.keras import layers, models
import numpy as np
import cv2
import os
from sklearn.model_selection import train_test_split

def load_images_from_folder(folder, label):
    images = []
    labels = []
    for filename in os.listdir(folder):
        img_path = os.path.join(folder, filename)
        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
        if img is not None:
            img = cv2.resize(img, (128, 128)) # Resizing the image to a uniform size
            images.append(img)
            labels.append(label)
    return images, labels

def load_dataset():
    images_with_box, labels_with_box = load_images_from_folder("dataset/box", 1)
    images_without_box, labels_without_box = load_images_from_folder("dataset/no_box", 0)

    images = np.array(images_with_box + images_without_box)
    labels = np.array(labels_with_box + labels_without_box)

    images = images / 255.0
    images = images.reshape(-1, 128, 128, 1)

    return train_test_split(images, labels, test_size=0.2, random_state=42)

def build_model():
    model = models.Sequential([
        layers.Conv2D(32, (3,3), activation='relu', input_shape=(128, 128, 1)),
        layers.MaxPooling2D((2,2)),
        layers.Conv2D(64, (3,3), activation='relu'),
        layers.MaxPooling2D((2,2)),
        layers.Flatten(),
        layers.Dense(128, activation='relu'),
        layers.Dense(1, activation='sigmoid')
    ])

    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])
    return model

print("Start")

# Loading test data
X_train, X_test, y_train, y_test = load_dataset()

# Creating the model
model = build_model()

# Training the model
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))

model.save("box_detector_model.h5")

print("Training completed!")

import time
import http.client
import requests
import tensorflow as tf
import numpy as np
import json
import cv2
import os
import random
from datetime import datetime

tf.get_logger().setLevel('INFO')

# A simple hysteresis threshold to stop the script from spamming

DETECTION_TIME_THRESHOLD = 40.0 # Seconds with box, before the status changes
ABSENCE_TIME_THRESHOLD = 30.0 # Seconds without box, before the status changes

SLACK_TOKEN = "xoxb-...."
SLACK_CHANNEL = "#your-channel-name"

SAVE_DIR = "./false_positives"

os.makedirs(SAVE_DIR, exist_ok=True)

def send_slack_message(message):
    conn = http.client.HTTPSConnection("slack.com")

    headers = {
        "Authorization": f"Bearer {SLACK_TOKEN}",
        "Content-Type": "application/json",
    }

    data = json.dumps({
        "channel": SLACK_CHANNEL,
        "text": message,
    })

    conn.request("POST", "/api/chat.postMessage", body=data, headers=headers)

    response = conn.getresponse()
    if response.status != 200:
        print(f"Error sending the slack message: {response.read().decode()}")
    else:
        response_data = json.loads(response.read().decode())
        if not response_data.get("ok"):
            print(f"Error sending the slack message: {response_data}")

    conn.close()

def capture_and_predict(model, url):
    response = requests.get(url, stream=True)
    if response.status_code == 200:
        img_array = np.asarray(bytearray(response.content), dtype=np.uint8)
        img_color = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
        img_gray = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY)
        img_resized = cv2.resize(img_gray, (128, 128)) / 255.0
        img_input = np.expand_dims(img_resized, axis=-1)
        img_input = np.expand_dims(img_input, axis=0)
        prediction = model.predict(img_input, verbose=0)[0][0]
        return prediction > 0.5, img_color
    else:
        print("Error fetching the image.")
        return None, None

_last_message = None

def get_coffee_message(is_positive=True):
    global _last_message

    messages = [
        "🧼 I'm at the spa. It's called luxury. For you: cleaning mode.",
        "😶‍🌫️ Too much of your love is stuck on me. Let me scrub that off.",
        "🛁 Spa day for me. You guys take a break.",
        "😐 No, I'm not broken. I'm busy with a bubble bath.",
        "🧽 I'm cleaning myself. Why? Because you treat me like an animal.",
        "💤 Cleaning in progress. Please don't knock, I'm sensitive.",
        "🙃 Hygiene is important. That's what my therapist told me.",
        "🚿 I'm showering. You should try it too.",
        "🤖 Maintenance ongoing. Mood? Somewhere between resigned and soapy.",
        "🫣 I'm getting rid of your coffee sins. Don't look."
    ] if is_positive else [
        "☕ Ready for the next coffee. Wishing you a strong day!",
        "🧼 Freshly cleaned and ready to go. Let's do this – you're awesome!",
        "🔧 I'm back in shape. Time for your next energy boost.",
        "📎 System's running. This day is bound to go well, right?",
        "😌 Clean, ready – and in a peaceful coffee mood.",
        "☀️ Good morning! I'm ready. Are you?",
        "👋 Back online. I believe in you (and caffeine).",
        "🥸 Ready to brew. Let's do great things – or at least stay awake.",
        "🌞 Ready to brighten your day. Coffee coming right up.",
        "📈 All set. May your day run as smoothly as I do now."
    ]

    message = random.choice(messages)

    while message == _last_message:
        message = random.choice(messages)

    _last_message = message
    return message

# Loading the model
model = tf.keras.models.load_model("box_detector_model.h5")

box_detected = False
last_detection_time = None
last_absence_time = None

previous_state = None

live_image_url = "http:///current"

while True:
    detected, last_image = capture_and_predict(model, live_image_url)
    current_time = time.time()

    if detected != previous_state:
        print('box detected' if detected else 'no box')
        previous_state = detected

    if detected is None:
        time.sleep(1)
        continue

    if detected:
        last_absence_time = None  # reset the opposite tracker

        if not box_detected:
            if last_detection_time is None:
                last_detection_time = current_time
            elif current_time - last_detection_time >= DETECTION_TIME_THRESHOLD:
                box_detected = True
                send_slack_message(get_coffee_message(True))

                # 💾 Bild lokal speichern
                if last_image is not None:
                    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
                    filename = f"{SAVE_DIR}/box_{timestamp}.jpg"
                    cv2.imwrite(filename, last_image)
                    print(f"Image saved: {filename}")
        else:
            # Box already detected, do nothing special
            pass

    else:
        last_detection_time = None  # reset the opposite tracker

        if box_detected:
            if last_absence_time is None:
                last_absence_time = current_time
            elif current_time - last_absence_time >= ABSENCE_TIME_THRESHOLD:
                box_detected = False
                send_slack_message(get_coffee_message(False))
        else:
            # Box already not detected, do nothing special
            pass

    time.sleep(1)