Removed cached files that are in .gitignore

.gitignore

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+# Ignore Python bytecode files and cache
+__pycache__/
+*.pyc
+
+# Ignore virtual environment
+venv/
+
+# Ignore macOS specific files
+.DS_Store
+*.DS_Store
+
+# Ignore bin directory (commonly for compiled binaries or scripts)
+bin/
+
+# Notebook checkpoints
+.ipynb_checkpoints
+
+# Temporarily Notebook
+/notebooks

README.md

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+# 🌐 LearnStream - Course Recommendation App
+
+**LearnStream** is a web-based application that provides course recommendations based on user input. It allows users to search for courses, receive personalized suggestions, and explore relevant educational content by Coursera. The app uses **Flask** for backend development and dynamically loads course data for a seamless user experience.
+
+![Homepage Screenshot](static/images/screenshot_index.png)
+*The main homepage where users can choose a course.*
+
+![Recommendation Screenshot](static/images/screenshot_recommendations.png)
+*The recommendation page showing course suggestions based on the user input.*
+
+## Objective 🎯
+
+The **LearnStream** app is designed to help users find courses tailored to their interests and learning goals. Key features include:
+
+- **Personalized Course Recommendations**: Based on user input, the app provides course suggestions that match the entered course name.
+- **User-Friendly Interface**: The app offers a simple and intuitive interface that allows users to easily explore courses and get relevant suggestions.
+
+## Features ✨
+
+- 🏫 **Course Recommendation**: Get similar course suggestions based on a selected course.
+- 🌍 **Dynamic Data Loading**: Course names and details are dynamically loaded from the backend.
+- 🖥️ **Interactive Interface**: Users can interact with the course selection form to view and explore recommendations.
+- 🔄 **Responsive**: The app’s layout and components automatically adjust to the user’s device screen size.
+
+## Requirements 📋
+
+To run this application locally, you need to install the following dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Setup and Installation 🚀
+
+Follow these steps to get your local environment up and running:
+
+1.  **Clone the repository:**
+
+```bash
+git clone https://github.com/yourusername/LearnStream.git
+cd LearnStream
+```
+
+2.  **Create and activate a virtual environment (optional but recommended):**
+
+    On **Mac/Linux**:
+
+    ```bash
+    python3 -m venv venv
+    source venv/bin/activate
+    ```
+
+    On **Windows**:
+
+    ```bash
+    python -m venv venv
+    .\venv\Scripts\activate    
+    ```
+
+3.  **Install the required dependencies:**
+
+
+```bash
+pip install -r requirements.txt
+```
+
+4.  **Run the Flask application:**
+
+```bash
+python3 app.py # On Mac/Linux
+python app.py # On Windowns
+```
+
+After running the application, visit `http://127.0.0.1:5000/` in your browser to view the app.
+
+## How It Works ⚙️
+
+1.  **Input**: Users can select a course from a dropdown menu, which is dynamically populated with course names.
+2.  **Processing**: Once a course is selected, the app processes the request and uses the backend to find and recommend similar courses.
+3.  **Output**: The app displays recommended courses along with details like ratings, institutions, and difficulty levels.
+
+## Folder Structure 📁
+
+```plaintext
+LearnStream/
+│
+├── app.py               # Flask application (backend logic)
+├── templates/           # HTML templates
+│   ├── index.html       # Home page template
+│   └── recommendations.html  # Recommendations page template
+├── static/              # Static files (CSS, images, etc.)
+│   ├── css/
+│   │   └── styles.css   # Styling for the web pages
+│   └── images/
+├── models/              # Pretrained models for recommendation
+│   ├── similarity_matrix.pkl  # Precomputed similarity matrix
+│   └── tfidf_vectorizer.pkl   # Pretrained TF-IDF vectorizer
+├── notebooks/           # Jupyter notebooks for model training and exploration
+│   └── model_training.ipynb  # Notebook for training models and generating data
+├── data/                # Raw data files
+│   └── coursera.csv     # Course data containing course details
+├── nltk/                # NLTK files for text processing
+│   └── ...              # Preprocessing and tokenization scripts
+├── data_loader.py       # Loads the course data and similarity matrix
+├── helper.py            # Contains helper functions for data preprocessing and recommendations
+├── requirements.txt     # List of required dependencies
+└── README.md            # Project documentation
+```
+
+
+## License 📜
+
+This project is licensed under the MIT License. See the LICENSE file for more details.
+
+## Technologies Used 🛠️
+
+-   **Flask**: For building the backend web application.
+-   **Jinja2**: For rendering dynamic HTML templates.
+-   **Python**: The core programming language for backend logic.
+-   **HTML5 & CSS3**: For structuring and styling the web pages.

app.py

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+from flask import Flask, render_template, request
+import os
+from data_loader import load_data
+from helper import preprocess_name, get_recommendations
+
+
+app = Flask(__name__)
+
+# Load the necessary data on app startup
+data, similarity_matrix, course_names = load_data()
+
+@app.route('/')
+def home():
+    """
+    Home page route. Displays the list of course names on the landing page.
+    
+    This route renders the 'index.html' template and passes the list of course names 
+    to be displayed on the home page.
+    
+    Returns:
+    - Rendered HTML template for the home page with the list of course names.
+    """
+    return render_template('index.html', courses=course_names)
+
+@app.route('/recommend', methods=['POST'])
+def recommend():
+    """
+    Recommendation page route. Processes the user's input and provides course recommendations.
+    
+    This route receives a course name from the form on the home page, preprocesses the 
+    name, and uses it to find similar courses. It then renders the 'recommendations.html' 
+    template with the recommendations.
+    
+    Returns:
+    - Rendered HTML template for the recommendations page with the recommended courses.
+    """
+    course_name = request.form['course_name']
+
+    # Preprocess the course name and get recommendations
+    preprocessed_course_name = preprocess_name(course_name)
+    recommendations = get_recommendations(preprocessed_course_name, data, similarity_matrix)
+
+    return render_template('recommendations.html', recommendations=recommendations)
+
+if __name__ == '__main__':
+    app.run(debug=True)