Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import xgboost as xgb
+import pandas as pd
+
+import random
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split,cross_val_score
+from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
+from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
+from sklearn.neighbors import KNeighborsClassifier,KNeighborsRegressor
+from sklearn.svm import SVC,SVR
+from sklearn import datasets
+
+
+
+
+df_strength=pd.read_csv("datasetnew1.csv")
+
+import random
+
+x_strength=df_strength.iloc[:,:-1]
+y_strength=df_strength.iloc[:,-1]
+
+
+x_strength=df_strength.iloc[:,:-1]
+y_strength=df_strength.iloc[:,-1]
+from sklearn.model_selection import train_test_split
+x_train, x_test, y_train, y_test=train_test_split(x_strength,y_strength, test_size=0.2, random_state=20)
+
+clf = xgb.XGBRegressor(colsample_bytree= 1.0, learning_rate= 0.06596, max_depth= 36, min_child_weight= 1.0, n_estimators= 750, subsample= 0.2)
+clf.fit(x_train, y_train)
+
+
+
+
+import gradio as gr
+
+def OPS_LWAC(OPC, SCM_FA, SCM_SF, w_b, FA, CA, OPS, SP, T):
+    # Turning the arguments into a numpy array
+    x = np.array([OPC, SCM_FA, SCM_SF, w_b, FA, CA, OPS, SP, T])
+    prediction = clf.predict(x.reshape(1, -1))
+    return prediction
+
+inputs = [
+    gr.inputs.Number(label="cement content (OPC, unit: kg/m3)"),
+    gr.inputs.Number(label="fly ash content (SCM_FA, unit: kg/m3)"),
+    gr.inputs.Number(label="silica fume content (SCM_SF, unit: kg/m3)"),
+    gr.inputs.Number(label="water to binder ratio (w_b, unit: -)"),
+    gr.inputs.Number(label="fine aggregate content (FA, unit: kg/m3)"),
+    gr.inputs.Number(label="coarse aggregate content (CA, unit: kg/m3)"),
+    gr.inputs.Number(label="oil palm shell content (OPS, unit: kg/m3)"),
+    gr.inputs.Number(label="superplasticizer content (SP, unit: kg/m3)"),
+    gr.inputs.Number(label="curing time (T, unit: d)")
+]
+outputs = gr.outputs.Textbox(label="Estimated compressive strength of OPS-based LWAC (tu, unit: MPa)")
+
+app = gr.Interface(fn=OPS_LWAC, inputs=inputs, outputs=outputs, description="Estimation of compressive strength of OPS-based lightweight concrete")
+app.launch()