Deep Learning Project: Automating Tasks with OpenAI's GPT-4o

This project leverages OpenAI's GPT-4 to analyze travel journals, create itineraries, and process restaurant recommendations. It includes tools for managing API usage, processing various data formats, and generating formatted outputs.

Features

• Journal Analysis: Extract key information from travel journals including restaurants, activities, and food specialties
• Smart Itinerary Planning: Generate detailed daily itineraries based on dates and available restaurant data
• Data Processing: Handle multiple file formats (CSV, TXT, HTML)
• API Usage Monitoring: Track OpenAI API usage and costs
• Interactive Components: Jupyter notebook integration with HTML display capabilities
• Weather Integration: Fetch and analyze weather data for travel planning
• Utility Functions: Temperature conversions, random selections, and data formatting tools

Project Structure

This project is structured for clarity and maintainability. Let's explore the key folders:

• data/ (folder): Stores all the raw data used for analysis.
  • cape_town.txt (file): Contains journal entries specifically for Cape Town.
  • journal_tokyo.txt (file): Journal entries from your trip to Tokyo.
  • journal_rio.txt (file): Similar file for Rio de Janeiro.
  • journal_sidney.txt (file): Journal entries for Sydney.
  • .csvs: Various data files in CSV format, likely containing restaurant details or itinerary information.
• code/ (folder): Houses Jupyter Notebooks for data analysis.
  • helper_functions.py (file): Contains core functions used across notebooks for common tasks.
  • api_usage_monitor.py (file): Tracks your OpenAI API usage, ensuring responsible use of the platform.

This structure facilitates easy access to data and code, promoting efficient workflow and collaboration.

Setup

1. Clone the repository:

   git clone https://github.com/miqueasmd/AIPythonDeepLearningAI.git
   AIPythonDeepLearningAI

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   • Create a .env file in the project root directory.
   • Add your OpenAI API key to the .env file:

     OPENAI_API_KEY=your_openai_api_key_here
     BASE_PATH=your_base_path_here
     

Core Functions

Journal Analysis

Restaurant Data Processing

Itinerary Planning

Generate detailed itinerary

Generate city-specific plans

API Usage Monitoring

Usage Notes

1. API Costs: The project uses OpenAI's API which has associated costs. Monitor usage through the provided tools.
2. Rate Limits: Includes retry mechanisms for API rate limits.
3. Data Storage: Journal entries and restaurant data should be stored in the /data directory.
4. Interactive Features: Many features are designed for Jupyter notebook integration.

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Acknowledgements

This project is inspired by the DeepLearning.AI courses. Please visit DeepLearning.AI for more information and resources.