Create app.py

app.py

@@ -0,0 +1,45 @@
+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"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)}"
+        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"Improvement percentage would be around {(preds.squeeze()-1)*100}% and shear strength improvement predicted by Random Forest is {(preds * tmax).squeeze()}"
+        elif al == "XGBoost" :
+            with open("XGBoost.pkl", "rb") as f:
+                clf  = pickle.load(f)
+                preds = clf.predict([[type, dr, sn, percent]])
+            return f"Improvement percentage would be around {(preds.squeeze()-1)*100}% and shear strength improvement predicted by XGBoost is {(preds * tmax).squeeze()}"
+        elif al == "AdaBoost" :
+            with open("AdaBoost.pkl", "rb") as f:
+                clf  = pickle.load(f)
+                preds = clf.predict([[type, dr, sn, percent]])
+            return f"Improvement percentage would be around {(preds.squeeze()-1)*100}% and shear strength improvement predicted by AdaBoost is {(preds * tmax).squeeze()}"
+
+
+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)")
+SN = gr.Slider(50, 150, step=50, label="Normal Stresses (Sn)")
+PPERCENT= gr.Slider(0, 2, step=0.1, label="PET Percent")
+ShearStrength= gr.Slider(36.5, 144, step=0.1, label="Shear Strength")
+
+# We create the output
+output = gr.Textbox()
+
+
+app = gr.Interface(fn = make_prediction, inputs=[Algorithm, pet_type, DR, SN, PPERCENT, ShearStrength], outputs=output, live=True)
+app.launch()