first commit

.gitignore

@@ -0,0 +1,3 @@
+.env
+.streamlit/secrets.toml
+venv

.streamlit/config.toml

@@ -0,0 +1,8 @@
+[theme]
+base="light"
+primaryColor="slateBlue"
+baseRadius="full"
+
+
+[server]
+maxUploadSize = 20

README.md

@@ -0,0 +1,68 @@
+# 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 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.
+* **Expandable API response**: Inspect full JSON responses directly in the app.
+
+## Requirements
+
+* Python 3.8+
+* [Streamlit](https://streamlit.io)
+* `requests`
+* `python-dotenv`
+
+## Installation
+
+1. Clone this repository:
+
+   ```bash
+   git clone https://github.com/<your-username>/<your-repo>.git
+   cd <your-repo>
+   ```
+
+2. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Create a `.env` file with your API details:
+
+   ```bash
+   API_KEY=<your-api-key>
+   API_URL=<your-api-url>
+   ```
+
+## Running the App
+
+Run the Streamlit app locally:
+
+```bash
+streamlit run app.py
+```
+
+The app will open in your browser at `http://localhost:8501`.
+
+## Environment Variables
+
+* `API_KEY`: Your API key for authenticating with the API.
+* `API_URL`: Base URL of the API.
+
+## Project Structure
+
+```
+├── app.py                # Main Streamlit app
+├── static/
+│   ├── img/
+│   │   ├── logos etc.
+├── .env                  # Environment variables (not committed)
+└── requirements.txt      # Python dependencies
+```

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+import requests
+import json
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Load the API key from environment variables
+api_key = os.getenv("API_KEY",None)
+base_url = os.getenv("API_URL",None)
+url = f"{base_url}/predict"  # Adjust if upload endpoint is different
+
+headers = {
+    "Authorization": f"Key {api_key}",
+    "accept": "application/json"
+}
+
+st.set_page_config(page_title="AI-Hab Habitat Classifier", page_icon="static/img/ai-hab-logo-transparent.png", layout="centered")
+st.title("AI-Hab Habitat Classifier")
+
+# Take photo or upload
+img = st.camera_input("Take a photo of the habitat you wish to identify to UKHab Level 3") or st.file_uploader("Or upload a photo", type=["png", "jpg", "jpeg"])
+
+if img:
+
+    # Send to API
+    with st.spinner("Analyzing habitat..."):
+        files = {"file": ("habitat.jpg", img.getvalue(), "image/jpeg")} 
+        resp = requests.post(url, headers=headers, files=files)
+        data = resp.json()
+
+    # Display results
+    predictions = data["results"]["ukhab"]
+
+    st.image(img, caption="Uploaded image")
+
+    st.warning("**Note:** The model is still in development and may not be accurate.")
+
+    # Show secondary predictions
+    st.write("## Predictions")
+    for pred in predictions[0:]:
+        with st.container(border=True):
+            st.subheader(f"{pred['code']} - {pred['name']}")
+            if(pred['confidence']> 0.5):
+                st.badge(f"**Confidence:** {pred['confidence']:.2%}",color="green")
+            else:
+                st.badge(f"**Confidence:** {pred['confidence']:.2%}",color="orange")
+            st.write("" + " > ".join([h['name'] for h in pred['primary_habitat_hierarchy']]))
+
+    st.write("## API Response")
+    with st.expander("Show response data"):
+        st.code(json.dumps(data, indent=2), language="json")
+
+
+
+st.divider()
+
+col1, col2 = st.columns(2)
+with col1:
+    st.write(" ")
+    st.image("static/img/UKCEH.png")
+with col2:
+    st.image("static/img/University-of-Lincoln.png")
+
+st.write("AI-Hab is a habitat classification model developed by the UK Centre for Ecology & Hydrology and the University of Lincoln. It is based on the UKHab Habitat Classification system and uses computer vision to classify habitats from images.")       
+