Create app.py

app.py

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+# Step 6.1: Define different input components
+import gradio as gr
+
+# a. define text data type
+input_module1 = gr.inputs.Slider(1, 100, step=5, label = "Longitude")
+
+# b. define image data type
+input_module2 = gr.inputs.Slider(1, 100, step=5, label = "Latitude")
+
+# c. define Number data type
+input_module3 = gr.inputs.Slider(1, 100, step=5, label = "Feature3")
+
+# d. define Slider data type
+input_module4 = gr.inputs.Slider(1, 100, step=5, label = "Feature4")
+
+# e. define Checkbox data type
+input_module5 = gr.inputs.Slider(1, 100, step=5, label = "Feature5")
+
+# f. define Radio data type
+input_module6 = gr.inputs.Slider(1, 100, step=5, label = "Feature6")
+
+# g. define Dropdown data type
+input_module7 = gr.inputs.Slider(1, 100, step=5, label = "Feature7")
+
+# g. define Dropdown data type
+input_module8 = gr.inputs.Slider(1, 100, step=5, label = "Feature8")
+
+
+# Step 6.2: Define different output components
+# a. define text data type
+output_module1 = gr.outputs.Textbox(label = "Output Text")
+
+# b. define image data type
+output_module2 = gr.outputs.Image(label = "Output Image")
+
+# you can define more output components
+
+def machine_learning_pipeline(input1, input2, input3, input4, input5, input6, input7, input8):
+  print("Start ml processing")
+
+  import numpy as np
+  import pandas as pd
+
+  print(input1, input2, input3, input4, input5, input6, input7, input8)
+
+  ### 1. process the user submission, collect the features and save them into one numpy array
+  new_feature = np.array([[input1, input2, input3, input4, input5, input6, input7, input8]])
+  print(new_feature)
+
+  ### 2. follow the data preprocessing steps as we have done in trainig data 
+  ### 2.1 check missing values in total_bedroom 
+
+  ### 2.2 feature normalization
+  test_set = pd.DataFrame(new_feature, columns = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',
+       'total_bedrooms', 'population', 'households', 'median_income'])
+
+  ## 1. clean the missing values in test set
+  test_set_clean = test_set.dropna(subset=["total_bedrooms"])
+  test_set_clean
+  
+  ### reload the scaler from the local file
+  
+  import pickle
+  
+  with open('minmax_scaler.pkl', 'rb') as f:
+      scaler = pickle.load(f)
+
+
+  ## 4. scale the numeric features in test set. 
+  ## important note: do not apply fit function on the test set, using same scalar from training set
+  test_features_normalized = scaler.transform(test_set_clean)
+  print("test_features_normalized: ",test_features_normalized)
+
+  ### 3. load the pre-train machine learning 
+
+
+  # load
+  with open('tree_reg.pkl', 'rb') as f:
+      tree_reg = pickle.load(f)
+
+
+  ### 4. apply the loaded model on the features to make a prediction 
+  ### Step 5: make a prediction using tree model
+  test_predictions_trees = tree_reg.predict(test_features_normalized)
+  print("test_predictions_trees: ",test_predictions_trees)
+
+  ### 5. send back the prediction 
+
+  print("Start processing")
+  import numpy as np
+  #output1 = 'This is the output'
+  output2 = np.random.rand(28,28)
+
+  import matplotlib.pyplot as plt  
+  plt.plot([input1], [input2])
+  plt.savefig('test.png')
+
+  return test_predictions_trees, 'test.png'