Create app.py

app.py

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+import numpy as np
+import pandas as pd
+from sklearn.neighbors import KNeighborsRegressor
+from joblib import dump
+import gradio
+
+## Building a Fubction for prediction:
+
+def predictPrice(input1, input2, input3, input4, input5, input6, input7, input8):
+  features = [input1, input2, input3, input4, input5, input6, input7, input8]
+  scaler.fit(features)
+  features_array = np.array(features).reshape(1, -1)
+  prediction = KNN_Regressor.predict(features_array)
+  return prediction
+
+
+## Buidling inputs and outputs:
+
+input1 = gr.Slider(-124.35, -114.31, step=5, label = "Longitude")
+input2 = gr.Slider(32.54, 41.95, step=5, label = "Latitude")
+input3 = gr.Slider(1, 52.0, step=5, label = "Housing_median_age (Year)")
+input4 = gr.Slider(1, 39320.0, step=5, label = "Total_rooms")
+input5 = gr.Slider(1, 6445.0, step=5, label = "Total_bedrooms")
+input6 = gr.Slider(1, 35682.0, step=5, label = "Population")
+input7 = gr.Slider(1, 6082.0, step=5, label = "Households")
+input8 = gr.Slider(0, 15.0, step=5, label = "Median_income")
+
+output1 = gr.Textbox(label = "House Value")
+
+
+##title Putting it all together:
+
+gr.Interface(fn=predictPrice, inputs=[input1, input2, input3, input4, input5, input6, input7, input8],
+             outputs=output1).launch(show_error=True)