Update app.py

app.py

@@ -15,12 +15,14 @@ def predict_1(SOC: float):
     prediction = 1.58 + np.exp(-0.07*SOC)
     return prediction.round(2)
 
-def predict_2(Cy: float, SOC: float):
-    prediction = 2.03 - 0.008 * Cy - 0.008 * SOC
+def predict_2(SOC: float, Cy: float):
+    prediction = 2.03 - 0.008*Cy - 0.008*SOC
     return prediction.round(2)
 
-def predict_3(Cy: float, SOC: float):
-    prediction = 1.53 - 0.076 * SOC + 0.004 * Cy
+def predict_3(SOC: float, Cy: float, eCa: float, eMg: float eK: float, eAlH: float):
+    ECEC = eCa + eMg + eK + eNa + eAlH
+    xK = eK/ECEC
+    prediction = 1.53 - 0.076*SOC + 0.004*Cy - 2.04*xK
     return prediction.round(2)
     
 def predict_4(pH: float, EC: float, CCE: float, SOC: float, Sa: float, Si: float,
@@ -33,7 +35,7 @@ def predict_4(pH: float, EC: float, CCE: float, SOC: float, Sa: float, Si: float
     xAlH = eAlH/ECEC
     BS1 = (eCa + eMg + eK + eNa)/CEC
     BS2 = (eCa + eMg + eK + eNa)/ECEC
-    input_features = np.array([[pH,	EC,	CCE,	SOC,	Sa,	Si,	Cy,	CEC, ECEC,	xCa,	xMg,	xK,	xNa,	xAlH,	BS1,	BS2]])
+    input_features = np.array([[pH,	EC,	CCE, SOC, Sa, Si, Cy, CEC, ECEC, xCa, xMg, xK, xNa, xAlH, BS1, BS2]])
     input_features_scale = s_c.transform(input_features)
     prediction = rf.predict(input_features_scale)[0].round(2)
     return prediction
@@ -72,7 +74,7 @@ with gr.Blocks() as demo:
 
     submit_1.click(predict_1, inputs=[SOC], outputs=[output])
     submit_2.click(predict_2, inputs=[SOC, Cy], outputs=[output])
-    submit_3.click(predict_3, inputs=[SOC, Cy], outputs=[output])
+    submit_3.click(predict_3, inputs=[SOC, Cy, eCa, eMg, eK, eAlH], outputs=[output])
     submit_4.click(predict_4, inputs=[pH , EC, CCE, SOC, Sa, Si, Cy, CEC, eCa, eMg, eK, eNa, eAlH], outputs=[output])
 
 demo.launch(share=False, debug=False)