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aihab-uk/api-usage-demo

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App README

AI-Hab API usage Demonstrator

This is a minimal demonstrator streamlit web application that classifies habitats into UKHab Level 3 categories using the AI-Hab computer vision model. This app serves as an example of integrating the API into an app.

The API codebase is available here: https://github.com/NERC-CEH/aihab-api

The API is in development and is currently hosted on the UKCEH Posit Connect server, it is only accessible via authentication.

Features

—Camera capture or file upload: Take a photo directly in the app or upload an existing image.
—API-powered predictions: Sends the image to a Posit Connect API for habitat classification.
—UKHab-assisted predictions: Shows top predictions with confidence, hierarchy, and UKHab habitat definitions (when data/ukhab.json is available).
—Manual observation submission to Hugging Face bucket: After prediction, submit the observation to upload image and metadata to aihab-uk/habitatimages.
—Metadata capture: Captures datetime, location, top-3 predictions with confidence, selected habitat label, optional level-4 refinement, observer name, and user comment.
—Observer name persistence: Persists observer full name in browser local storage for convenience across observations.
—Expandable API response: Inspect full JSON responses directly in the app.

Requirements

—Python 3.8+
—Streamlit
—requests
—python-dotenv
—huggingface_hub
—streamlit_js_eval
—streamlit-folium
—folium

Installation

1.Clone this repository:

bash

   git clone https://github.com/<your-username>/<your-repo>.git
   cd <your-repo>

1.Install dependencies:

bash

   pip install -r requirements.txt

1.Create a .env file with your API details:

bash

   API_KEY=<your-api-key>
   API_URL=<your-api-url>

1.Add your Hugging Face token in your environment configuration:

bash

   HF_AUTH_TOKEN=<your-huggingface-token>

Running the App

Run the Streamlit app locally:

bash

streamlit run app.py

The app will open in your browser at http://localhost:8501.

First-Time Use

For a smooth first run:

1.Open the app in a modern browser such as Chrome or Edge.
2.Choose Take a photo or Upload a photo on the home screen.
3.If your browser asks for camera or location access, allow it if you want to use those features. Location is optional and can be edited manually before submission.
4.Review the AI prediction, then confirm or correct the habitat label before submitting.

If the app starts but predictions fail, check that API_URL, API_KEY, and HF_AUTH_TOKEN are all set correctly in your environment.

Environment Variables

—API_KEY: Your API key for authenticating with the API.
—API_URL: Base URL of the API.
—HF_AUTH_TOKEN: Hugging Face token used for bucket uploads.
—HF_BUCKET_PREFIX (optional): Prefix/folder inside the fixed bucket aihab-uk/habitatimages.

Hugging Face Bucket Upload

After the prediction response is shown, go to Submit observation and press Submit observation to upload to hf://buckets/aihab-uk/habitatimages.

Files are organized as:

—images/<image-file>
—metadata/<image-file-stem>.json

The metadata JSON contains:

—Image file name
—Datetime
—Latitude and longitude (when available)
—Top 3 AI-predicted habitats and confidence values
—Habitat label selected by the user
—User comment

UKHab Data Fallback

If data/ukhab.json is missing or invalid, the app still runs and prediction/submission flows continue to work. In that case, UKHab sidebar guidance, level-4 refinement options, and prediction descriptions are not shown.

Project Structure

├── app.py                # Main Streamlit app
├── static/
│   ├── img/
│   │   ├── logos etc.
├── .env                  # Environment variables (not committed)
└── requirements.txt      # Python dependencies

Troubleshooting

If you see import errors when starting the app, reinstall dependencies with:

bash

pip install -r requirements.txt

If the camera or location does not work, check your browser site permissions and refresh the page.