created our first blog

blog/authors.yml

@@ -0,0 +1,16 @@
+ajay-dhangar:
+  name: Ajay Dhangar
+  title: Founder of AIBuddies, and CodeHarborHub
+  url: https://ajay-dhangar.github.io/
+  image_url: https://avatars.githubusercontent.com/u/99037494?v=4
+  email: [email protected]
+  page: true # Turns the feature on
+  description: >
+    I'm Ajay Dhangar, a software engineer, and founder of AIBuddies, and CodeHarborHub. I love to write about programming, and new technologies. I'm also a mentor, and a speaker. My latest project is AIBuddies, a platform to learn AI, and ML. I'm also working on CodeHarborHub, a platform to learn programming, and software development. I'm always open to new opportunities, and collaborations. Feel free to reach out to me, if you have any questions, or suggestions.
+
+  socials:
+    x: CodesWithAjay
+    linkedin: ajay-dhangar
+    github: ajay-dhangar
+    stackoverflow: 18530900
+    newsletter: https://ajay-dhangar.github.io

blog/understanding-time-space-complexity.md

@@ -0,0 +1,164 @@
+---
+slug: not-hot-dog-app-tutorial
+title: "Building a 'Not Hot Dog' App: A Fun Introduction to Image Classification"
+authors: [ajay-dhangar]
+tags: [ajay-dhangar, AI, machine-learning, image-classification, deep-learning, tutorial]
+---
+
+In this blog post, we’ll walk through the process of building a simple image classification app inspired by the "Not Hot Dog" app from the TV show *Silicon Valley*. This app will detect whether an image is a hot dog or not, using deep learning techniques and a pre-trained model.
+
+<!-- truncate -->
+
+## Overview
+
+The "Not Hot Dog" app is a fun way to learn about image classification using machine learning. We’ll use TensorFlow and Keras to train a model that can classify images as either "Hot Dog" or "Not Hot Dog." This tutorial will guide you through:
+
+- Setting up the development environment.
+- Preparing the dataset.
+- Building and training a neural network.
+- Creating a simple web app to deploy the model.
+
+## Prerequisites
+
+Before we get started, make sure you have the following:
+
+- **Basic Python knowledge**.
+- **Familiarity with machine learning concepts** (helpful but not required).
+- **Python and pip installed on your system**.
+
+## Step 1: Setting Up the Environment
+
+First, install the required libraries:
+
+```bash
+pip install tensorflow keras numpy pillow flask
+```
+
+We’ll use **TensorFlow** and **Keras** for building the model, **Pillow** for image processing, and **Flask** to create a simple web interface.
+
+## Step 2: Preparing the Dataset
+
+You’ll need a dataset of images containing hot dogs and non-hot dog items. You can find datasets online or create your own by collecting images from the web.
+
+Organize the dataset into two folders:
+- `data/hot_dog/`
+- `data/not_hot_dog/`
+
+## Step 3: Building the Model
+
+Now, let’s build a simple convolutional neural network (CNN) to classify the images.
+
+```python title="model.py"
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+
+# Define the model
+model = Sequential([
+    Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
+    MaxPooling2D(pool_size=(2, 2)),
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D(pool_size=(2, 2)),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dense(1, activation='sigmoid')
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Display the model summary
+model.summary()
+```
+
+The model architecture consists of two convolutional layers followed by pooling, flattening, and two dense layers. The final output layer uses a sigmoid activation function for binary classification.
+
+## Step 4: Training the Model
+
+Let’s load the data and train the model.
+
+```python title="train.py"
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Data augmentation and preprocessing
+train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
+training_set = train_datagen.flow_from_directory('data', target_size=(64, 64), batch_size=32, class_mode='binary')
+
+# Train the model
+model.fit(training_set, epochs=10, steps_per_epoch=100)
+```
+
+This code uses data augmentation to improve the model’s performance by slightly modifying the training images.
+
+## Step 5: Creating a Simple Web App
+
+Now, we’ll create a basic web app using Flask to deploy the model.
+
+```python title="app.py"
+from flask import Flask, request, render_template
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+app = Flask(__name__)
+
+# Load the trained model
+model.load_weights('model.h5')
+
+@app.route('/', methods=['GET', 'POST'])
+def home():
+    if request.method == 'POST':
+        file = request.files['file']
+        img = image.load_img(file, target_size=(64, 64))
+        img_array = image.img_to_array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+        prediction = model.predict(img_array)[0][0]
+        result = "Hot Dog" if prediction > 0.5 else "Not Hot Dog"
+        return render_template('index.html', result=result)
+    return render_template('index.html', result=None)
+
+if __name__ == '__main__':
+    app.run(debug=True)
+```
+
+The above code sets up a basic Flask server that loads the model and predicts whether an uploaded image is a hot dog or not.
+
+## Step 6: Testing the App
+
+1. **Save the trained model**:
+
+   ```python title="model.py"
+   model.save('model.h5')
+   ```
+
+2. **Create a simple `index.html` file** in a `templates` folder:
+
+   ```html title="templates/index.html"
+   <!DOCTYPE html>
+   <html lang="en">
+   <head>
+       <meta charset="UTF-8">
+       <meta name="viewport" content="width=device-width, initial-scale=1.0">
+       <title>Not Hot Dog App</title>
+   </head>
+   <body>
+       <h1>Not Hot Dog App</h1>
+       <form action="/" method="post" enctype="multipart/form-data">
+           <input type="file" name="file" accept="image/*" required>
+           <button type="submit">Check</button>
+       </form>
+       <h2>{{ result }}</h2>
+   </body>
+   </html>
+   ```
+
+3. **Run the Flask app**:
+
+   ```bash
+   python app.py
+   ```
+
+4. **Open `http://localhost:5000/`** in your browser, upload an image, and see if it’s classified as a "Hot Dog" or "Not Hot Dog".
+
+## Conclusion
+
+Congratulations! You've built a basic "Not Hot Dog" app using image classification techniques. This project demonstrates how fun and accessible AI development can be. For further learning, consider adding features like using a more sophisticated model, deploying the app to a cloud platform, or expanding the app’s functionality.

docusaurus.config.ts

@@ -36,10 +36,13 @@ const config: Config = {
       {
         docs: {
           sidebarPath: "./sidebars.ts",
-          // Please change this to your repo.
-          // Remove this to remove the "edit this page" links.
           editUrl:
-            "https://github.com/ai-buddies/ai-buddies.github.io/tree/main/packages/create-docusaurus/templates/shared/",
+            "https://github.com/ai-buddies/ai-buddies.github.io/tree/main/",
+        },
+        blog: {
+          showReadingTime: true,
+          editUrl:
+            "https://github.com/ai-buddies/ai-buddies.github.io/tree/main/",          
         },
         theme: {
           customCss: "./src/css/custom.css",
@@ -94,8 +97,8 @@ const config: Config = {
           label: "Tutorial",
         },
         {
-          label: "Community",
-          to: "/community/",
+          to: "/blog",
+          label: "Blog",
           position: "left",
         },
         {