Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import numpy as np
+from pykrige.ok import OrdinaryKriging
+
+
+def fonction_api(lat, lon, debit_min, date):
+    # requete d'acces à x; y ,z:debit
+    echelle = 10
+    x = np.array(x)
+    y = np.array(y)
+    z = np.array(z)
+    grid_x_min = x.min()
+    grid_x_max = x.max()
+    grid_y_min = y.min()
+    grid_y_max = y.max()
+    delta_x, delta_y = (grid_x_max - grid_x_min) / \
+        echelle, (grid_y_max - grid_y_min)/echelle
+    grid_x = np.linspace(grid_x_min, grid_x_max, echelle, dtype="float64")
+    grid_y = np.linspace(grid_y_min, grid_y_max, echelle, dtype="float64")
+    OK = OrdinaryKriging(x, y, z, variogram_model="exponential",)
+    zstar, ss = OK.execute("grid", grid_x, grid_y)
+    mask = zstar > debit_min  # mask debit sup à debit_min
+    lat_pred = []
+    lon_pred = []
+    debit_pred = []
+    for i in range(echelle):
+        for j in range(echelle):
+            if mask[i, j] == True:
+                lat_pred.append(
+                    ((grid_x_min + (i-1)*delta_x)+(grid_x_min + i*delta_x))/2)
+                lon_pred.append(
+                    ((grid_y_min + (j-1)*delta_y)+(grid_y_min + j*delta_y))/2)
+                debit_pred.append(zstar[i, j])
+    data = {}
+    for i in range(len(lat_pred)):
+        data["point " + str(i)] = [lat_pred[i], lon_pred[i], debit_pred[i]]
+    return data
+
+
+demo = gr.Interface(fn=fonction_api, inputs=[
+                    "number", "number", "number", "text"], outputs="json")
+demo.launch()