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| import pickle | |
| def make_prediction(al, type, dr, sn, percent, tmax): | |
| if percent == 0: | |
| return "There is not any improvement in shear strength" | |
| else: | |
| if al == "Decision Tree" : | |
| with open("Decision_Tree.pkl", "rb") as f: | |
| clf = pickle.load(f) | |
| preds = clf.predict([[type, dr, sn, percent]]) | |
| return f"""Shear strength after improvement: {round((preds.squeeze() * tmax),2)} | |
| Improvement percentage: {round((preds.squeeze()-1)*100,2)}%""" | |
| elif al == "Random Forest" : | |
| with open("random_forest.pkl", "rb") as f: | |
| clf = pickle.load(f) | |
| preds = clf.predict([[type, dr, sn, percent]]) | |
| return f"""Shear strength after improvement: {round((preds.squeeze() * tmax),2)} | |
| Improvement percentage: {round((preds.squeeze()-1)*100,2)}%""" | |
| elif al == "XGBoost" : | |
| with open("XGBoost.pkl", "rb") as f: | |
| clf = pickle.load(f) | |
| preds = clf.predict([[type, dr, sn, percent]]) | |
| return f"""Shear strength after improvement: {round((preds.squeeze() * tmax),2)} | |
| Improvement percentage: {round((preds.squeeze()-1)*100,2)}%""" | |
| elif al == "AdaBoost" : | |
| with open("AdaBoost.pkl", "rb") as f: | |
| clf = pickle.load(f) | |
| preds = clf.predict([[type, dr, sn, percent]]) | |
| return f"""Shear strength after improvement: {round((preds.squeeze() * tmax),2)} | |
| Improvement percentage: {round((preds.squeeze()-1)*100,2)}%""" | |
| import gradio as gr | |
| #Create the input component for Gradio since we are expecting 5 inputs | |
| Algorithm = gr.Radio(["Decision Tree", "Random Forest", "XGBoost", "AdaBoost"], value="Decision Tree", label="Algorithm") | |
| pet_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") | |
| PPERCENT= gr.Slider(0, 2, step=0.1, label="PET Percent", info="Range between 0 to 2") | |
| ShearStrength= 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 | |
| output = gr.Textbox() | |
| app = gr.Interface(fn = make_prediction, inputs=[Algorithm, pet_type, DR, SN, PPERCENT, ShearStrength], outputs=output, | |
| title= "Soil Improvement Using PET Calculator", | |
| description = """ | |
| This app is based on the paper 'Development of a non-destructive method to assess the shear strength of a sandy soil reinforced with polyethylene-terephthalate (PET)' | |
| Authors: Masoud Samaei1, Roohollah Shirani Faradonbeh1,*, Morteza Alinejad Omran2, Nima Rahimpour3, Amir Bazrafshan Moghaddam2 | |
| 1,* WA School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kalgoorlie, WA 6430, Australia | |
| 2 Department of Civil Engineering, Shahrood University of Technology, Shahrood, Iran | |
| 3 Faculty of Engineering, University of Tabriz, Tabriz, Iran | |
| * Corresponding Author—Email: roohollah.shiranifaradonbeh@curtin.edu.au | |
| """, | |
| live=False) | |
| app.launch() | |