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0be8da9 b5e0ed9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # 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'
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)
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