Merge pull request #1 from openbiodiversity/feat/gee

.gitignore

@@ -1,2 +1,3 @@
 .venv
 __pycache__/
+service_account.json

README.md

@@ -0,0 +1,58 @@
+# Bioscore calculator app
+
+This is a simple guide to help you set up and run a Gradio app.
+
+## Prerequisites
+
+- Python 3 installed on your system
+- venv module for creating a virtual environment (usually comes with Python)
+
+## Installation
+
+Clone the repository to your local machine:
+```bash
+git clone https://github.com/your-username/gradio-app.git
+cd gradio-app
+```
+
+Set up the service account credentials:
+- Obtain a service account key file (in JSON format) with the necessary permissions to access any external services required by your Gradio app.
+- Save the service account key file as `service_account.json` in the project directory.
+
+Create and activate a virtual environment:
+```bash
+python3 -m venv venv
+source venv/bin/activate
+```
+
+Install the required Python packages:
+```bash
+pip3 install -r requirements.txt
+```
+
+## Running the App
+
+To start the Gradio app, execute the following command:
+
+```bash
+gradio app.py
+```
+
+The app will start running, and you should see output similar to the following:
+
+```
+Running on http://127.0.0.1:7860
+Open your web browser and visit http://127.0.0.1:7860 to access the Gradio app.
+```
+
+## Customization
+
+Feel free to modify the app.py file to customize the behavior and appearance of your Gradio app. You can add or remove input and output interfaces, change their appearance, or include additional functionality as per your requirements.
+
+## Feedback
+
+If you encounter any issues or have any questions or suggestions, please don't hesitate to open an issue on the GitHub repository. We appreciate your feedback and contributions!
+
+## License
+
+This project is licensed under the MIT License.

app.py

@@ -1,7 +1,15 @@
 import gradio as gr
 import plotly.graph_objects as go
 from datasets import load_dataset
+import ee
+# import geemap
 
+# GEE
+service_account = 'climatebase-july-2023@ee-geospatialml-aquarry.iam.gserviceaccount.com'
+credentials = ee.ServiceAccountCredentials(service_account, 'service_account.json')
+ee.Initialize(credentials)
+
+# Gradio dataset
 dataset = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train")
 df = dataset.to_pandas()
 
@@ -43,10 +51,12 @@ def filter_map(min_price, max_price, boroughs):
 with gr.Blocks() as demo:
  with gr.Column():
  with gr.Row():
- min_price = gr.Number(value=250, label="Minimum Price")
- max_price = gr.Number(value=1000, label="Maximum Price")
- boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Boroughs:")
+ min_price = gr.Number(value=250, label="Project Name")
+ max_price = gr.Number(value=1000, label="Project Description")
+ boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Methodology:")
  btn = gr.Button(value="Update Filter")
+ btn = gr.Button(value="Save")
+ btn = gr.Button(value="Run")
  map = gr.Plot().style()
  demo.load(filter_map, [min_price, max_price, boroughs], map)
  btn.click(filter_map, [min_price, max_price, boroughs], map)

requirements.txt

@@ -1,3 +1,8 @@
 datasets
 plotly
 gradio
+duckdb==0.8.1 
+geemap
+segment-geospatial
+geojson
+yaml