Update app.py

app.py

@@ -30,7 +30,7 @@ x_train, x_test, y_train, y_test=train_test_split(x_strength,y_strength, test_si
 
 learner = DecisionTreeRegressor(criterion='friedman_mse', max_depth=3)
 
-#clf = NGBRegressor (learning_rate= 0.07079690881517825, minibatch_frac= 0.6472100547852997, n_estimators= 548, Base=learner)
+
 clf = NGBRegressor (learning_rate= 0.0708, minibatch_frac= 0.6472, n_estimators= 548, Base=learner)
 clf.fit(x_train, y_train)
 
@@ -47,7 +47,9 @@ def OPS_LWAC(OPC, SCM_FA, SCM_SF, w_b, FA, CA, OPS, SP, T):
     #value2 = bb[0]
     #prediction = clf.predict(x.reshape(1, -1))
     #-3*y_dists.params["scale"]+y_dists.params["loc"]
-    return [aa, bb]
+    result_string = "[CS: {:.2f}; σ: {:.2f}]".format(aa, bb)
+    return result_string
+    #return [aa, bb]
 
 inputs = [
     gr.inputs.Number(label="cement content (OPC, unit: kg/m3)"),
@@ -60,7 +62,7 @@ inputs = [
     gr.inputs.Number(label="superplasticizer content (SP, unit: kg/m3)"),
     gr.inputs.Number(label="curing time (T, unit: d)")
 ]
-outputs = gr.outputs.Textbox(label="Estimated compressive strength of OPS-based LWAC (tu, unit: MPa)")
+outputs = gr.outputs.Textbox(label="Estimated mean prediction and σ value (unit: MPa)")
 
 app = gr.Interface(fn=OPS_LWAC, inputs=inputs, outputs=outputs, description="Estimation of compressive strength of OPS-based lightweight concrete")
 app.launch()