In Python, building AI-powered Telegram bots unlocks massive potential for image generation, processing, and automation—master this to create viral tools and ace full-stack interviews! 🤖
Learn more: https://hackmd.io/@husseinsheikho/building-AI-powered-Telegram-bots
https://t.iss.one/DataScienceM🦾
# Basic Bot Setup - The foundation (PTB v20+ Async)
from telegram.ext import Application, CommandHandler, MessageHandler, filters
async def start(update, context):
await update.message.reply_text(
"✨ AI Image Bot Active!\n"
"/generate - Create images from text\n"
"/enhance - Improve photo quality\n"
"/help - Full command list"
)
app = Application.builder().token("YOUR_BOT_TOKEN").build()
app.add_handler(CommandHandler("start", start))
app.run_polling()
# Image Generation - DALL-E Integration (OpenAI)
import openai
from telegram.ext import ContextTypes
openai.api_key = os.getenv("OPENAI_API_KEY")
async def generate(update: Update, context: ContextTypes.DEFAULT_TYPE):
if not context.args:
await update.message.reply_text("❌ Usage: /generate cute robot astronaut")
return
prompt = " ".join(context.args)
try:
response = openai.Image.create(
prompt=prompt,
n=1,
size="1024x1024"
)
await update.message.reply_photo(
photo=response['data'][0]['url'],
caption=f"🎨 Generated: *{prompt}*",
parse_mode="Markdown"
)
except Exception as e:
await update.message.reply_text(f"🔥 Error: {str(e)}")
app.add_handler(CommandHandler("generate", generate))
Learn more: https://hackmd.io/@husseinsheikho/building-AI-powered-Telegram-bots
#Python #TelegramBot #AI #ImageGeneration #StableDiffusion #OpenAI #MachineLearning #CodingInterview #FullStack #Chatbots #DeepLearning #ComputerVision #Programming #TechJobs #DeveloperTips #CareerGrowth #CloudComputing #Docker #APIs #Python3 #Productivity #TechTips
https://t.iss.one/DataScienceM
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#YOLOv8 #ComputerVision #ObjectDetection #IndustrialAI #Python
Applying YOLOv8 for Industrial Automation: Counting Plastic Bottles
This lesson will guide you through a complete computer vision project using YOLOv8. The goal is to detect and count plastic bottles in an image from an industrial setting, such as a conveyor belt or a storage area.
---
Step 1: Setup and Installation
First, we need to install the necessary libraries. The
#Setup #Installation
---
Step 2: Loading the Model and the Target Image
We will load a pre-trained YOLOv8 model. These models are trained on the large COCO dataset, which already knows how to identify common objects like 'bottle'. Then, we'll load our industrial image. Ensure you have an image named
#ModelLoading #DataHandling
---
Step 3: Performing Detection on the Image
With the model and image loaded, we can now run the detection. The
#Inference #ObjectDetection
---
Step 4: Filtering and Counting the Bottles
The model detects many types of objects. Our task is to go through the results, filter for only the 'bottle' class, and count how many there are. We'll also store the locations (bounding boxes) of each detected bottle for visualization.
#DataProcessing #Filtering
---
Step 5: Visualizing the Results
A number is good, but seeing what the model detected is better. We will draw the bounding boxes and the final count directly onto the image to create a clear visual output.
#Visualization #OpenCV
Applying YOLOv8 for Industrial Automation: Counting Plastic Bottles
This lesson will guide you through a complete computer vision project using YOLOv8. The goal is to detect and count plastic bottles in an image from an industrial setting, such as a conveyor belt or a storage area.
---
Step 1: Setup and Installation
First, we need to install the necessary libraries. The
ultralytics library provides the YOLOv8 model, and opencv-python is essential for image processing tasks.#Setup #Installation
# Open your terminal or command prompt and run this command:
pip install ultralytics opencv-python
---
Step 2: Loading the Model and the Target Image
We will load a pre-trained YOLOv8 model. These models are trained on the large COCO dataset, which already knows how to identify common objects like 'bottle'. Then, we'll load our industrial image. Ensure you have an image named
factory_bottles.jpg in your project folder.#ModelLoading #DataHandling
import cv2
from ultralytics import YOLO
# Load a pre-trained YOLOv8 model (yolov8n.pt is the smallest and fastest)
model = YOLO('yolov8n.pt')
# Load the image from the industrial setting
image_path = 'factory_bottles.jpg' # Make sure this image is in your directory
img = cv2.imread(image_path)
# A quick check to ensure the image was loaded correctly
if img is None:
print(f"Error: Could not load image at {image_path}")
else:
print("YOLOv8 model and image loaded successfully.")
---
Step 3: Performing Detection on the Image
With the model and image loaded, we can now run the detection. The
ultralytics library makes this process incredibly simple. The model will analyze the image and identify all the objects it recognizes.#Inference #ObjectDetection
# Run the model on the image to get detection results
results = model(img)
print("Detection complete. Processing results...")
---
Step 4: Filtering and Counting the Bottles
The model detects many types of objects. Our task is to go through the results, filter for only the 'bottle' class, and count how many there are. We'll also store the locations (bounding boxes) of each detected bottle for visualization.
#DataProcessing #Filtering
# Initialize a counter for the bottles
bottle_count = 0
bottle_boxes = []
# The model's results is a list, so we loop through it
for result in results:
# Each result has a 'boxes' attribute with the detections
boxes = result.boxes
for box in boxes:
# Get the class ID of the detected object
class_id = int(box.cls)
# Check if the class name is 'bottle'
if model.names[class_id] == 'bottle':
bottle_count += 1
# Store the bounding box coordinates (x1, y1, x2, y2)
bottle_boxes.append(box.xyxy[0])
print(f"Total plastic bottles detected: {bottle_count}")
---
Step 5: Visualizing the Results
A number is good, but seeing what the model detected is better. We will draw the bounding boxes and the final count directly onto the image to create a clear visual output.
#Visualization #OpenCV
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🤖🧠 Pico-Banana-400K: The Breakthrough Dataset Advancing Text-Guided Image Editing
🗓️ 09 Nov 2025
📚 AI News & Trends
Text-guided image editing has rapidly evolved with powerful multimodal models capable of transforming images using simple natural-language instructions. These models can change object colors, modify lighting, add accessories, adjust backgrounds or even convert real photographs into artistic styles. However, the progress of research has been limited by one crucial bottleneck: the lack of large-scale, high-quality, ...
#TextGuidedEditing #MultimodalAI #ImageEditing #AIResearch #ComputerVision #DeepLearning
🗓️ 09 Nov 2025
📚 AI News & Trends
Text-guided image editing has rapidly evolved with powerful multimodal models capable of transforming images using simple natural-language instructions. These models can change object colors, modify lighting, add accessories, adjust backgrounds or even convert real photographs into artistic styles. However, the progress of research has been limited by one crucial bottleneck: the lack of large-scale, high-quality, ...
#TextGuidedEditing #MultimodalAI #ImageEditing #AIResearch #ComputerVision #DeepLearning
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🤖🧠 Concerto: How Joint 2D-3D Self-Supervised Learning Is Redefining Spatial Intelligence
🗓️ 09 Nov 2025
📚 AI News & Trends
The world of artificial intelligence is rapidly evolving and self-supervised learning has become a driving force behind breakthroughs in computer vision and 3D scene understanding. Traditional supervised learning relies heavily on labeled datasets which are expensive and time-consuming to produce. Self-supervised learning, on the other hand, extracts meaningful patterns without manual labels allowing models to ...
#SelfSupervisedLearning #ComputerVision #3DSceneUnderstanding #SpatialIntelligence #AIResearch #DeepLearning
🗓️ 09 Nov 2025
📚 AI News & Trends
The world of artificial intelligence is rapidly evolving and self-supervised learning has become a driving force behind breakthroughs in computer vision and 3D scene understanding. Traditional supervised learning relies heavily on labeled datasets which are expensive and time-consuming to produce. Self-supervised learning, on the other hand, extracts meaningful patterns without manual labels allowing models to ...
#SelfSupervisedLearning #ComputerVision #3DSceneUnderstanding #SpatialIntelligence #AIResearch #DeepLearning
🤖🧠 Skyvern: The Future of Browser Automation Powered by AI and Computer Vision
🗓️ 16 Nov 2025
📚 AI News & Trends
In today’s fast-evolving digital landscape, automation plays a crucial role in enhancing productivity, efficiency and innovation. Yet, traditional browser automation tools often struggle with complexity, maintenance and reliability. They rely heavily on DOM parsing, XPaths and rigid scripts that easily break when websites change their layout. Enter Skyvern, an open-source, AI-driven browser automation platform developed ...
#Skyvern #BrowserAutomation #AIDriven #ComputerVision #OpenSource #WebAutomation
🗓️ 16 Nov 2025
📚 AI News & Trends
In today’s fast-evolving digital landscape, automation plays a crucial role in enhancing productivity, efficiency and innovation. Yet, traditional browser automation tools often struggle with complexity, maintenance and reliability. They rely heavily on DOM parsing, XPaths and rigid scripts that easily break when websites change their layout. Enter Skyvern, an open-source, AI-driven browser automation platform developed ...
#Skyvern #BrowserAutomation #AIDriven #ComputerVision #OpenSource #WebAutomation
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📌 How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-18 | ⏱️ Read time: 14 min read
Automatic plant leaf recognition leverages deep feature embeddings to transform leaf images into dense numerical vectors in a high-dimensional space. By calculating the Euclidean similarity between these vector representations, machine learning models can accurately identify and classify plant species. This computer vision technique provides a powerful and scalable solution for botanical and agricultural applications, moving beyond traditional manual identification methods.
#ComputerVision #MachineLearning #DeepLearning #FeatureEmbeddings #ImageRecognition
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-11-18 | ⏱️ Read time: 14 min read
Automatic plant leaf recognition leverages deep feature embeddings to transform leaf images into dense numerical vectors in a high-dimensional space. By calculating the Euclidean similarity between these vector representations, machine learning models can accurately identify and classify plant species. This computer vision technique provides a powerful and scalable solution for botanical and agricultural applications, moving beyond traditional manual identification methods.
#ComputerVision #MachineLearning #DeepLearning #FeatureEmbeddings #ImageRecognition
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