Update app.py

app.py

@@ -8,7 +8,8 @@ def make_prediction(al, type, dr, sn, percent, tmax):
             with open("Decision_Tree.pkl", "rb") as f:
                 clf  = pickle.load(f)
                 preds = clf.predict([[type, dr, sn, percent]])
-            return f"Improvement percentage by Decision Tree is predicted around {round((preds.squeeze()-1)*100,2)}% and shear strength after improvement is {round((preds.squeeze() * tmax),2)}"
+            return f"""Shear strength after improvement: {round((preds.squeeze() * tmax),2)}
+            Improvement percentage is {round((preds.squeeze()-1)*100,2)}%"""
         elif al == "Random Forest" :
             with open("random_forest.pkl", "rb") as f:
                 clf  = pickle.load(f)
@@ -55,179 +56,3 @@ Authors: Masoud Samaei1, Roohollah Shirani Faradonbeh1,*, Morteza Alinejad Omran
                    live=False)
 app.launch()
 
-
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-def make_prediction2(parameter, Type, dr, sn, percent, tmax):
-    with open("Decision_Tree.pkl", "rb") as f1:
-        clf1  = pickle.load(f1)
-    with open("random_forest.pkl", "rb") as f2:
-        clf2  = pickle.load(f2)
-    with open("XGBoost.pkl", "rb") as f3:
-        clf3  = pickle.load(f3)
-    with open("AdaBoost.pkl", "rb") as f4:
-        clf4  = pickle.load(f4)
-
-    predses1=np.array([])
-    predses2=np.array([])
-    predses3=np.array([])
-    predses4=np.array([])
-    
-    # fig = plt.figure()
-    
-    if parameter=="PET Type":
-        Types = np.array([])
-        for Type in range(1,4):
-            Types = np.append(Types, Type)
-          
-            preds1 = clf1.predict([[Type, dr, sn, percent]])
-            preds1 =  (round((preds1.squeeze() * tmax),2))
-            predses1 = np.append(predses1, preds1)
-            
-            preds2 = clf2.predict([[Type, dr, sn, percent]])
-            preds2 =(round((preds2.squeeze() * tmax),2))
-            predses2= np.append(predses2, preds2)
-            
-            preds3 = clf3.predict([[Type, dr, sn, percent]])
-            preds3 =(round((preds3.squeeze() * tmax),2))
-            predses3= np.append(predses3, preds3)
-            
-            preds4 = clf4.predict([[Type, dr, sn, percent]])
-            preds4 =(round((preds4.squeeze() * tmax),2))
-            predses4= np.append(predses4, preds4)
-            
-        plt.xlim(0, 4)     # set the xlim to left, right
-        plt.ylim(tmax*0.8, 1.5*tmax)
-        plt.scatter(Types, predses1, color='k', label='Decision Tree')
-        plt.scatter(Types, predses2, color='b', label='Random Forest')
-        plt.scatter(Types, predses3, color='r', label='XGBoost')
-        plt.scatter(Types, predses4, color='y', label='AdaBoost')
-        plt.ylabel("Shear Strength", fontweight='bold')
-        plt.axhline(y=tmax, color='r', linestyle='-')
-        plt.xlabel("PET Type", fontweight='bold')
-        plt.legend(loc='lower right')
-        plt.show()
-
-
-    elif parameter=="Dr":
-        drs = np.array([])
-        for dr in np.arange(0.55,1,0.2):
-            drs = np.append(drs, dr)
-          
-            preds1 = clf1.predict([[Type, dr, sn, percent]])
-            preds1 =  (round((preds1.squeeze() * tmax),2))
-            predses1 = np.append(predses1, preds1)
-            
-            preds2 = clf2.predict([[Type, dr, sn, percent]])
-            preds2 =(round((preds2.squeeze() * tmax),2))
-            predses2= np.append(predses2, preds2)
-            
-            preds3 = clf3.predict([[Type, dr, sn, percent]])
-            preds3 =(round((preds3.squeeze() * tmax),2))
-            predses3= np.append(predses3, preds3)
-            
-            preds4 = clf4.predict([[Type, dr, sn, percent]])
-            preds4 =(round((preds4.squeeze() * tmax),2))
-            predses4= np.append(predses4, preds4)
-            
-        plt.xlim(0, 1.5)     # set the xlim to left, right
-        plt.ylim(tmax*0.9, 1.1*tmax)
-        plt.scatter(drs, predses1, color='k', label='Decision Tree')
-        plt.scatter(drs, predses2, color='b', label='Random Forest')
-        plt.scatter(drs, predses3, color='r', label='XGBoost')
-        plt.scatter(drs, predses4, color='y', label='AdaBoost')
-        plt.ylabel("Shear Strength", fontweight='bold')
-        plt.xlabel("Dr", fontweight='bold')
-        plt.axhline(y=tmax, color='r', linestyle='-')
-        plt.legend(loc='lower right')
-        plt.show()
-
-
-    elif parameter=="Sn":
-        snss = np.array([])
-        for sn in np.arange(50,155,50):  
-            snss = np.append(snss, sn)
-            preds1 = clf1.predict([[Type, dr, sn, percent]])
-            preds1 =  (round((preds1.squeeze() * tmax),2))
-            predses1 = np.append(predses1, preds1)
-            
-            preds2 = clf2.predict([[Type, dr, sn, percent]])
-            preds2 =(round((preds2.squeeze() * tmax),2))
-            predses2= np.append(predses2, preds2)
-            
-            preds3 = clf3.predict([[Type, dr, sn, percent]])
-            preds3 =(round((preds3.squeeze() * tmax),2))
-            predses3= np.append(predses3, preds3)
-            
-            preds4 = clf4.predict([[Type, dr, sn, percent]])
-            preds4 =(round((preds4.squeeze() * tmax),2))
-            predses4= np.append(predses4, preds4)
-        
-        plt.xlim(40, 160)     # set the xlim to left, right
-        plt.ylim(tmax*0.8, 1.5*tmax)
-        plt.scatter(snss, predses1, color='k', label='Decision Tree')
-        plt.scatter(snss, predses2, color='b', label='Random Forest')
-        plt.scatter(snss, predses3, color='r', label='XGBoost')
-        plt.scatter(snss, predses4, color='y', label='AdaBoost')
-        plt.ylabel("Shear Strength", fontweight='bold')
-        plt.xlabel("Normal Stress (Sn)", fontweight='bold')
-        plt.axhline(y=tmax, color='r', linestyle='-')
-        plt.legend(loc='lower right')
-        plt.show()  
-        
-    elif parameter=="PET percantage":
-        percents= np.array([])
-        for percent in np.arange(0.1,2.05,0.1):          
-            percents = np.append(percents, percent)
-            preds1 = clf1.predict([[Type, dr, sn, percent]])
-            preds1 =  (round((preds1.squeeze() * tmax),2))
-            predses1 = np.append(predses1, preds1)
-            
-            preds2 = clf2.predict([[Type, dr, sn, percent]])
-            preds2 =(round((preds2.squeeze() * tmax),2))
-            predses2= np.append(predses2, preds2)
-            
-            preds3 = clf3.predict([[Type, dr, sn, percent]])
-            preds3 =(round((preds3.squeeze() * tmax),2))
-            predses3= np.append(predses3, preds3)
-            
-            preds4 = clf4.predict([[Type, dr, sn, percent]])
-            preds4 =(round((preds4.squeeze() * tmax),2))
-            predses4= np.append(predses4, preds4)
-        
-        plt.xlim(0, 2.5)     # set the xlim to left, right
-        plt.ylim(tmax*0.6, 1.5*tmax)
-        plt.scatter(percents, predses1, color='k', label='Decision Tree')
-        plt.scatter(percents, predses2, color='b', label='Random Forest')
-        plt.scatter(percents, predses3, color='r', label='XGBoost')
-        plt.scatter(percents, predses4, color='y', label='AdaBoost')
-        plt.ylabel("Shear Strength", fontweight='bold')
-        plt.xlabel("PET percentage", fontweight='bold')
-        plt.axhline(y=tmax, color='r', linestyle='-')
-        plt.legend(loc='lower right')
-        plt.show()  
-        
-    return plt
-
-
-
-#
-    
-parameter = gr.Radio(["PET Type", "Dr", "Sn", "PET percantage"], value="PET Type", label="Select the parameter")
-Type = gr.Slider(1, 3, value=1, step=1, label="PET type", info="Choose 1 for 1*1 PET, 2 for 1*5 PET, and 3 for fiber PET")
-dr = gr.Slider(0.55, 0.95, step=0.2, label="Relative Density (Dr)", info="Range between 0.55 to 0.95")
-sn = gr.Slider(50, 150, step=50, label="Normal Stresses (Sn)", info="Range between 50 to 150")
-percent= gr.Slider(0, 2, step=0.1, label="PET Percent", info="Range between 0 to 2")
-tmax= gr.Slider(36.5, 144, step=0.1, label="Shear Strength (plain soil without PET)", info="Range between 36 to 144")
-
-# We create the output
-
-app = gr.Interface(fn = make_prediction2, inputs=[parameter, Type, dr, sn, percent, tmax], outputs=['plot'], 
-                   title= "Parametric Analysis",
-                   description = """ abc
-""",
-                   live=False)
-app.launch()