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""" Gradio app for biomass and carbon stock estimation. """ |
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from pathlib import Path |
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import joblib |
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import numpy as np |
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import pandas as pd |
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import gradio as gr |
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from utils import check_inside_civ, initialize_ee, extract_from_gee |
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initialize_ee() |
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def load_pipeline(): |
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""" Load the best model pipeline. """ |
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path = Path("data/best_model_c_pipeline.pkl") |
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if not path.exists(): |
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raise RuntimeError("Model not found: data/best_model_c_pipeline.pkl") |
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return joblib.load(path) |
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PIPE = load_pipeline() |
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def predict_agbd(df_row: pd.DataFrame) -> float: |
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""" Predict AGBD from a DataFrame row. """ |
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y = PIPE.predict(df_row) |
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return float(np.asarray(y).ravel()[0]) |
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def carbon_from_agbd(agbd_t_ha: float, cf: float = 0.5) -> float: |
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""" Convert AGBD to carbon stock. """ |
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return float(agbd_t_ha * cf) |
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def run_all(latitude: float, longitude: float): |
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""" Run the app. """ |
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inside = check_inside_civ(latitude, longitude) |
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if not inside: |
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return ("n/a", "n/a", "Choose a location inside Côte d'Ivoire.") |
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try: |
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data, err = extract_from_gee(latitude, longitude) |
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if err is not None or data is None: |
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return ("n/a", "n/a", "n/a") |
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cloud_cov = data["cloud"] |
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estimation_date = data["estimation_date"] |
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X = data["X"] |
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agb = predict_agbd(X) |
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carbon = carbon_from_agbd(agb) |
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return (f"{estimation_date}", |
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f"{cloud_cov:.2f} %", |
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f"{agb:.2f} t/ha", |
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f"{carbon:.2f} tC/ha") |
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except Exception as e: |
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return ("error", "", str(e)) |
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with gr.Blocks(theme=gr.themes.Ocean(), fill_height=True) as demo: |
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gr.Markdown("🌴 **BCSE** : Biomass and Carbon Stock Estimation using ML and satellite images (Sentinel-1/2, DEM) 🌴") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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lat = gr.Number(label="lat", value=5.280498, precision=6) |
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lon = gr.Number(label="lon", value=-4.089883, precision=6) |
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with gr.Row(): |
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clear = gr.Button("Clear") |
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submit = gr.Button("Submit", variant="primary") |
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with gr.Column(scale=1): |
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est_date = gr.Textbox(label="Date of estimation") |
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cloud = gr.Textbox(label="Cloud coverage") |
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agbd = gr.Textbox(label="Above ground biomass density (AGBD) t/ha") |
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cstock = gr.Textbox(label="Carbon stock density tC/ha") |
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def _on_submit(la, lo): |
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return run_all(la, lo) |
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submit.click(_on_submit, [lat, lon], [est_date, cloud, agbd, cstock]) |
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def _on_clear(): |
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return None, None, None, None, None |
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clear.click(_on_clear, outputs=[lat, lon, est_date, cloud, agbd, cstock]) |
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if __name__ == "__main__": |
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demo.launch(share=True) |
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