Update app.py

app.py

@@ -11,7 +11,19 @@ with open('my-standard-scaler.pkl', 'rb') as file:
     s_c = pickle.load(file)
 
 # Definir la función de predicción
-def predict(pH: float, EC: float, CCE: float, SOC: float, Sa: float, Si: float,
+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
+    return prediction.round(2)
+
+def predict_3(Cy: float, SOC: float):
+    prediction = 1.53 − 0.076 * SOC + 0.004 * Cy
+    return prediction.round(2)
+    
+def predict_4(pH: float, EC: float, CCE: float, SOC: float, Sa: float, Si: float,
             Cy: float, CEC: float, eCa: float, eMg: float, eK: float, eNa: float, eAlH: float):
     ECEC = eCa + eMg + eK + eNa + eAlH
     xCa = eCa/ECEC
@@ -47,9 +59,20 @@ with gr.Blocks() as demo:
             eCa = gr.Number(label="eCa (--)", value=19.44, interactive=True)
             eNa = gr.Number(label="eNa (--)", value=0.15, interactive=True)
 
-    submit = gr.Button(value='Predecir')
+    with gr.Row():
+        with gr.Column():
+            submit_1 = gr.Button(value='Abdelbaki')
+        with gr.Column():
+            submit_2 = gr.Button(value='Benites')
+        with gr.Column():
+            submit_3 = gr.Button(value='MLRegression')
+        with gr.Column():
+            submit_4 = gr.Button(value='Random Forest')
     output = gr.Textbox(label=": soil bulk density", interactive=False)
-    
-    submit.click(predict, inputs=[pH , EC, CCE, SOC, Sa, Si, Cy, CEC, eCa, eMg, eK, eNa, eAlH], outputs=[output])
+
+    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_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)