Create app.py

app.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import gradio as gr
+
+
+df=pd.read_excel("Salary.xlsx")
+df.head(3)
+
+y_dep=df.Salary
+x_ind=df.drop(["Salary","Student"],axis=1)
+
+x_train, x_test, y_train, y_test = train_test_split(x_ind,y_dep, test_size = 0.2,random_state=1)
+
+model = LinearRegression() 
+model.fit(x_train, y_train) 
+y_pred = model.predict(x_test)
+
+def salary(school_ranking,gpa,experience):
+    input1=np.array([school_ranking,gpa,experience])
+    output1=model.predict([input1])
+    return output1[0].round()
+
+interface = gr.Interface(fn = salary,
+inputs=[gr.inputs.Number(default=1, label="School Ranking"), gr.inputs.Slider(1,10,step=0.1,label = "G.P.A"),gr.inputs.Slider(1,15,step=1,label = "Experience")], 
+outputs = [gr.outputs.Textbox( label="Expected Salary")],description="SALARY PREDICTION")
+
+interface.launch()