Code repository for Moviebot – an NLP-powered movie recommendation chatbot, written in Python, HTML/CSS, and JavaScript, hosted on Vercel + AWS Lambda.
This is a personal project inspired by Steven Tey's Moviebot, created as part of my exploration into practical data science.
To run this program locally, follow these steps:
- Download the repo with
git clone https://github.com/daniel-owen/moviebot.git - Create a virtual environment with
python3 -m venv venv - Activate your virtual environment with
source venv/bin/activate - Then, install all the required libraries with
pip install -r requirements.txt - Next, export the Flask app route with
export FLASK_APP=index.py - You will also need to export the Flask environment with
export FLASK_ENV=development - Lastly, execute
flask run, and your program should be running athttp://127.0.0.1:5000/
To deploy this to Vercel, run vercel --prod in your terminal and follow through with the default setup criteria.
If you encounter the error zsh: command vercel not found, you may need to run the following:
export PATH="/Users/danielowen/.npm-global/bin/:$PATH"
The AWS Lambda function can be found in this model.py file.
💪 Technologies Used Languages: Python, HTML/CSS, JavaScript Frameworks: Flask (Python), Vercel (Frontend Deployment) Cloud Platform: AWS Lambda Tools: Git, GitHub 🧠 Machine Learning Technique The machine learning technique employed in this project is a content-based recommendation system. The recommendation algorithm relies on NLP (Natural Language Processing) techniques to analyze and understand user queries. By utilizing a cosine-similarity algorithm, the chatbot identifies patterns in movie preferences based on textual data, enhancing its ability to provide relevant movie recommendations to users.
🐞 Question + Bug Fixes Contact Unlimiteddemi@gmail.com for project details. This replication is inspired by Steven Tey's Moviebot, showcasing a blend of techniques, tools, and frameworks in NLP, web development, and cloud deployment.