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)