Update app.py

app.py

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+import gradio as gr
+
+input_module1 = gr.inputs.Slider(1, 100, step=5, label = "Longitude")
+input_module2 = gr.inputs.Slider(1, 100, step=5, label = "Latitude")
+input_module3 = gr.inputs.Slider(1, 100, step=5, label = "Housing_median_age (Year)")
+input_module4 = gr.inputs.Slider(1, 100, step=5, label = "Total_rooms")
+input_module5 = gr.inputs.Slider(1, 100, step=5, label = "Total_bedrooms")
+input_module6 = gr.inputs.Slider(1, 100, step=5, label = "Population")
+input_module7 = gr.inputs.Slider(1, 100, step=5, label = "Households")
+input_module8 = gr.inputs.Slider(1, 100, step=5, label = "Median_income")
+
+# 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):
+  import numpy as np
+  import pandas as pd
+  new_feature = np.array([[input1, input2, input3, input4, input5, input6, input7, input8]])
+  test_set = pd.DataFrame(new_feature, columns = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',
+       'total_bedrooms', 'population', 'households', 'median_income'])
+  test_set_clean = test_set.dropna(subset=["total_bedrooms"])
+  import pickle 
+  with open('minmax_scaler.pkl', 'rb') as f:
+      scaler = pickle.load(f)
+  test_features_normalized = scaler.transform(test_set_clean)
+  with open('tree_reg.pkl', 'rb') as f:
+      tree_reg = pickle.load(f)
+  test_predictions_trees = tree_reg.predict(test_features_normalized)
+  import matplotlib.pyplot as plt  
+  plt.scatter([input1], [input2])
+  plt.savefig('scatterplot.png')
+  return test_predictions_trees[0], 'scatterplot.png'
+  
+gr.Interface(fn=machine_learning_pipeline, 
+             inputs = [input_module1, input_module2, input_module3, 
+                                          input_module4, input_module5, input_module6,
+                                          input_module7, input_module8], 
+             outputs = [output_module1, output_module2]).launch(debug=True)
+