Project Title: Schneider Freight Load Search

Repository Link

GitHub Repository

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Set-up Steps

Prerequisites

• Python 3.9 or above
• Git
• AWS CLI configured with proper credentials
• Flask installed
• Required Python libraries (listed in requirements.txt)

Steps to Set Up the Project Locally

1. Clone the Repository:

   git clone https://github.com/saarthakaggarwal/schneider_team_2.git
   cd schneider_team_2

2. Set Up a Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate   # macOS/Linux
   venv\Scripts\activate    # Windows

3. Install Dependencies:

   pip install -r requirements.txt

4. Set Up Environment Variables:

   • Create a .env file in the root directory with the following content:

     SECRET_KEY=your_secret_key_here
     GOOGLE_MAPS_API_KEY=your_google_maps_api_key_here
     AWS_ACCESS_KEY_ID=your_aws_access_key_id_here
     AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key_here
     

5. Run the Application:

   python3 app.py

6. Access the Application: Open your browser and navigate to: http://127.0.0.1:5000

7. Working app:

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Overview of How the Code Works

This project provides a web-based application for searching freight loads, designed to integrate with Schneider’s FreightPower system.

Key Features

1. Data Handling:

   • Loads data from AWS DynamoDB tables (load_posting and load_stop).
   • Processes and structures data into pandas DataFrames for efficient querying.

2. Search Functionality:

   • Allows users to filter loads based on origin, destination, radius, weight, and transport type.
   • Provides pagination for search results.

3. Route Visualization:

   • Uses Google Maps API to display pickup and delivery routes for selected loads.

4. Dashboard:

   • Provides insights into active loads, top pickup/destination locations, and average load weights.

5. Interactive Features:

   • Users can dynamically adjust search parameters and view alternate route suggestions.

Code Structure

• app.py: Main Flask application containing routes, data handling, and API integrations.
• utils.py: Helper functions for geolocation and data processing.
• templates/: HTML templates for rendering the web interface.
• static/: Static assets (CSS, JS, images).
• requirements.txt: Python dependencies.

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What Works & What Doesn’t

What Works

• Data Integration:
  • Successfully retrieves and processes data from AWS DynamoDB.
• Search Functionality:
  • Filtering by origin, destination, weight, and transport type is operational.
• Google Maps Integration:
  • Displays accurate routes for pickup and delivery locations.
• Dashboard Metrics:
  • Provides useful insights on active loads and locations.

What Doesn’t Work

• Radius-Based Filtering:
  • Occasionally fails if coordinates for cities are missing or incorrect.
• Error Handling:
  • Limited handling of AWS connection issues or invalid user input.
• Performance:
  • Slower response times for large datasets due to repeated DataFrame operations.

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What Would You Work on Next

1. Enhance Error Handling:

   • Improve handling for missing or invalid data in DynamoDB.
   • Add user-friendly error messages for API and data processing errors.

2. Optimize Performance:

   • Cache frequently accessed data.
   • Use more efficient database queries and avoid excessive DataFrame operations.

3. Expand Features:

   • Allow users to upload custom load data via CSV.
   • Integrate with other Schneider systems for real-time updates.

4. Deployment:

   • Deploy the application on AWS using a WSGI server (e.g., Gunicorn) for production use.

5. Testing:

   • Add comprehensive unit and integration tests.
   • Set up CI/CD pipelines for automated testing and deployment.

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Additional Notes

For any questions or issues, please reach out via the repository's issue tracker.