Create app.py

app.py

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+import gradio as gr
+
+
+def predict(fea1, fea2, fea3, fea4, fea5, fea6, fea7, fea8):
+  import numpy as np
+
+  # 1. load features
+  test_feature = np.array([[fea1, fea2, fea3, fea4, fea5, fea6, fea7, fea8]])
+  
+  # 2. scale the test features
+  # 2.1 Load scaler file 
+  scaler = load('scaler.joblib')
+
+  # 2.2 normalize test features using statistics from training data
+  test_feature_normalized = scaler.transform(test_feature)
+  
+  print("shape: ", test_feature_normalized.shape)
+
+  # 3. Make a prediction 
+  # 3.1 load the machine learning 
+  model = load('knn_model.joblib')
+
+  # 3.2 call model to get prediction
+
+  pred = model.predict(test_feature_normalized)
+
+  return pred 
+
+input_module1 = gr.Slider(-124.35, -114.35, step = 0.5, label = "Longitude")
+input_module2 = gr.Slider(32, 41, step = 0.5, label = "Latitude")
+input_module3 = gr.Slider(1, 52, step = 1, label = "Housing Median Age (Year)")
+input_module4 = gr.Slider(1, 39320, step = 5, label = "Total Rooms")
+input_module5 = gr.Slider(1, 6445, step = 5, label = "Total Bedrooms")
+input_module6 = gr.Slider(3, 35682, step = 5, label = "Population")
+input_module7 = gr.Slider(1, 6081, step = 5, label = "Households")
+input_module8 = gr.Slider(0, 15, step = 0.5, label = "Median Income")
+
+output_module1 = gr.Textbox(label = "Predicted Housing Prices")
+
+gr.Interface(fn = predict,
+             inputs = [input_module1, input_module2, input_module3, input_module4, input_module5, input_module6, input_module7, input_module8],
+             outputs = [output_module1]).launch()