Shoe Dashboard

Dashboard for analyzing running shoe usage and distance using Streamlit and Strava API.

Demo: https://shoedash.streamlit.app/

Features

• Overview Stats: number of shoes, activities, total and average mileage
• Weekly Mileage Chart: shoes used per week
• Shoe Cards: shoe total and average, mileage, activities, distance cohorts
• Charts: YOY culmulative distance, YOY weekly/monthly distance, distance cohorts
• Filter data by start/end year and shoes

Requirements

• Python 3.12
• Streamlit
• Pandas

Setup

1. Create .env from .env.example

2. Setup demo endpoints using nPoint for athlete, activities, and gear data. Update .env with DEMO_PROFILE_URL, DEMO_ACTIVITY_URL, DEMO_GEAR_URL

   Demo Athlete:

   {
      "id": 1,
      "username": "foobar",
      "firstname": "Foo",
      "lastname": "Bar",
      "shoes": [
         {
            "id": "shoe1",
            "name": "Shoe Name",
            "distance": 1000000,
            "converted_distance": 621.0
         }
      ]
   }
   

   Demo Activities:

   [
      {
         "id": 1,
         "name": "Activity 1",
         "sport_type": "Run",
         "start_date_local": "2025-05-10T10:11:22Z",
         "distance": 5000,
         "gear_id": "shoe1",
      },
      {
         "id": 2,
         "name": "Activity 2",
         "sport_type": "Run",
         "start_date_local": "2025-05-11T08:17:11Z",
         "distance": 5000,
         "gear_id": "shoe2",
      }
   ]
   

   Demo Gear:

   [
      {
         "id": "shoe2",
         "name": "Retired Shoe",
         "retired": true,
         "distance": 684361,
         "converted_distance": 425.2,
      }
   ]
   

3. Create Strava application and update .env with STRAVA_CLIENT_ID, STRAVA_CLIENT_SECRET

4. Install dependencies

   pip install -r requirements.txt

5. Run streamlit

   streamlit run main.py

6. View app at http://localhost:8501