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import os
import numpy as np
from flask import Flask, request, jsonify
import rasterio
from rasterio.fill import fillnodata
from sklearn.preprocessing import StandardScaler
app = Flask(__name__)
time_steps_image = 4
target_img_size = (224, 224)
def preprocess_dem(dem_path):
with rasterio.open(dem_path) as src:
dem_data = src.read(1)
filled_dem = fillnodata(dem_data, mask=src.read_masks(1))
processed_dem_path = "processed_dem.tif"
with rasterio.open(
processed_dem_path, "w", **src.meta
) as dest:
dest.write(filled_dem, 1)
return processed_dem_path
def simulate_flood(dem_path, lake_elevation, output_path):
with rasterio.open(dem_path) as src:
dem_data = src.read(1)
meta = src.meta
flood_map = np.where(dem_data <= lake_elevation, 1, 0)
with rasterio.open(output_path, "w", **meta) as dest:
dest.write(flood_map.astype(rasterio.uint8), 1)
return output_path
@app.route('/predict_flood', methods=['POST'])
def predict_flood():
try:
dem_file = request.files['dem']
lake_elevation = float(request.form['lake_elevation'])
dem_path = os.path.join("uploads", "input_dem.tif")
os.makedirs("uploads", exist_ok=True)
dem_file.save(dem_path)
processed_dem_path = preprocess_dem(dem_path)
output_dir = "outputs"
os.makedirs(output_dir, exist_ok=True)
flood_map_path = os.path.join(output_dir, "flood_map.tif")
simulate_flood(processed_dem_path, lake_elevation, flood_map_path)
return jsonify({"message": "Flood map generated", "flood_map_path": flood_map_path})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
app.run(debug=True)
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