In this presentation, I will introduce an innovative approach to alcohol recommendation with generative AI, machine learning and open-source technologies. The system might utilizes langchain, Flask, NLP and database to store and analyze alcohol data, including concentration and cocktail recipes. By incorporating individual characteristics such as age, gender, and health status, it will calculate personalized alcohol intake curves and recommend suitable cocktails or drinks. Furthermore, this system dynamically adjusts recommendations based on previous selections to ensure safe alcohol consumption levels. Let's discover how this intelligent system can help individuals to make informed choices about their alcohol consumption.
Introduction
- Overview of the problem: Lack of personalized alcohol recommendations and potential health risks associated with excessive alcohol consumption.
System Architecture
- Explanation of the system architecture leveraging langchain, a generative AI framework, and Python technologies such as Flask, NumPy, Pandas, and Scikit-learn.
Data Collection and Processing
- Collection of alcohol data including concentration and recipes. Using Python libraries for data processing and feature extraction.
Machine Learning Model
- Implementation of machine learning algorithms to analyze individual characteristics and recommend personalized alcohol intake curves.
Recommendation Engine
- Development of a recommendation engine to suggest suitable cocktails or drinks based on calculated intake curves.
Demonstration
- Live demonstration of the intelligent alcohol recommendation system.
Benefits and Implications
- Discussion on the benefits of informed alcohol consumption and potential implications for public health.
Conclusion
- Summary of key findings and future directions for the intelligent alcohol recommendation system.