Delete app.py

app.py

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-# -*- coding: utf-8 -*-
-"""Lab2222.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1OUGOeTdmMbccW_st3Ao8nHDR5wm_VUNg
-"""
-
-from google.colab import drive
-
-drive.mount("/content/ML_Course")
-
-cd /content/ML_Course/MyDrive/ML_Course
-
-import pandas as pd
-housing = pd.read_csv("housing.csv")
-housing.head(n = 5)
-
-housing.columns
-
-housing.describe()
-
-housing.info()
-
-# Commented out IPython magic to ensure Python compatibility.
-# %matplotlib inline
-import matplotlib.pyplot as plt
-housing.hist(bins=50, figsize=(20,15))
-plt.show()
-
-# to make this notebook's output identical at every run
-import numpy as np
-np.random.seed(10)
-
-# For illustration only. Sklearn has train_test_split()
-def split_train_test(data, test_ratio):
-    shuffled_indices = np.random.permutation(len(data))
-    test_set_size = int(len(data) * test_ratio)
-    test_indices = shuffled_indices[:test_set_size]
-    train_indices = shuffled_indices[test_set_size:]
-    return data.iloc[train_indices], data.iloc[test_indices]
-
-# run the function to get the train & test set
-train_set, test_set = split_train_test(housing, 0.2)
-
-train_set.info()
-
-test_set.info()
-
-from sklearn.model_selection import train_test_split
-train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10)
-
-train_set.info()
-
-test_set.info()
-
-test_set.to_csv('blind_test.csv', index = False)
-
-train_set.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, 
-    s=train_set["population"]/100, label="population", figsize=(10,7),
-    c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=True,
-    sharex=False)
-plt.legend()
-plt.show()
-
-train_set.info()
-
-train_set[train_set.isna().any(axis=1)]
-
-train_set_clean = train_set.dropna(subset=["total_bedrooms"])
-train_set_clean
-
-train_set_clean.info()
-
-train_labels = train_set_clean["median_house_value"].copy() # get labels for output label Y
-train_features = train_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set
-train_features.info()
-
-train_features.head()
-
-train_features.columns
-
-train_features.info()
-
-train_features.describe()
-
-train_labels
-
-train_features.hist(bins=50, figsize=(12,9))
-
-train_features.describe()
-
-from sklearn.preprocessing import MinMaxScaler
-scaler = MinMaxScaler() ## define the transformer
-scaler.fit(train_features) ## call .fit() method to calculate the min and max value for each column in dataset
-
-print("Min of each column: ",scaler.data_min_)
-print("Max of each column: ",scaler.data_max_)
-
-train_features.describe()
-
-train_features_normalized = scaler.transform(train_features)
-train_features_normalized
-
-pd.DataFrame(train_features_normalized).hist(bins=50, figsize=(12,9))
-plt.show()
-
-## 1. split data to get train and test set
-from sklearn.model_selection import train_test_split
-train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10)
-
-## 2. clean the missing values
-train_set_clean = train_set.dropna(subset=["total_bedrooms"])
-train_set_clean
-
-## 2. derive training features and training labels 
-train_labels = train_set_clean["median_house_value"].copy() # get labels for output label Y
-train_features = train_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set
-
-
-## 4. scale the numeric features in training set
-from sklearn.preprocessing import MinMaxScaler
-scaler = MinMaxScaler() ## define the transformer
-scaler.fit(train_features) ## call .fit() method to calculate the min and max value for each column in dataset
-
-train_features_normalized = scaler.transform(train_features)
-train_features_normalized
-
-from sklearn.linear_model import LinearRegression ## import the LinearRegression Function
-lin_reg = LinearRegression() ## Initialize the class
-lin_reg.fit(train_features_normalized, train_labels) # feed the training data X, and label Y for supervised learning
-# feed the training data X, and label Y for supervised learning
-
-training_predictions = lin_reg.predict(train_features_normalized)
-training_predictions.shape
-
-train_labels
-
-## plot scatter plot 
-import matplotlib.pyplot as plt
-plt.scatter(training_predictions, train_labels )
-plt.xlabel('training_predictions', fontsize=15,color="red")
-plt.ylabel('train_label', fontsize=15,color="green")
-plt.title('Scatter plot for training_predictions and train_label', fontsize=15)
-plt.xlim(0,np.max(training_predictions)) # remove the predictions that have negative prices
-plt.show()
-
-import numpy as np
-np.corrcoef(training_predictions, train_labels)
-
-import pandas as pd 
-prediction_summary = pd.DataFrame({'predicted_label':training_predictions, 'actual_label':train_labels})
-prediction_summary
-
-prediction_summary['error'] = prediction_summary['actual_label'] - prediction_summary['predicted_label']
-prediction_summary
-
-from sklearn.metrics import mean_squared_error
-lin_mse = mean_squared_error(train_labels, training_predictions)
-lin_rmse = np.sqrt(lin_mse)
-lin_rmse
-
-## Step 1: training the data using decision tree algorithm
-from sklearn.tree import DecisionTreeRegressor ## import the DecisionTree Function
-tree_reg = DecisionTreeRegressor(random_state=10) ## Initialize the class
-tree_reg.fit(train_features_normalized, train_labels) # feed the training data X, and label Y for supervised learning
-
-### Step 2: make a prediction using tree model
-training_predictions_trees = tree_reg.predict(train_features_normalized)
-training_predictions_trees
-
-## Step 3: visualize the scatter plot between predictions and actual labels
-import matplotlib.pyplot as plt
-plt.scatter(training_predictions_trees, train_labels )
-plt.xlabel('training_predictions_trees', fontsize=15,color="red")
-plt.ylabel('train_label', fontsize=15,color="green")
-plt.title('Scatter plot for training_predictions_trees and train_label', fontsize=15)
-plt.xlim(0,np.max(training_predictions_trees)) # remove the predictions that have negative prices
-plt.show()
-
-from sklearn.metrics import mean_squared_error
-tree_mse = mean_squared_error(train_labels, training_predictions_trees)
-tree_rmse = np.sqrt(tree_mse)
-tree_rmse
-
-## 1. clean the missing values in test set
-test_set_clean = test_set.dropna(subset=["total_bedrooms"])
-test_set_clean
-
-## 2. derive test features and test labels. In this case, test labels are only used for evaluation
-test_labels = test_set_clean["median_house_value"].copy() # get labels for output label Y
-test_features = test_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set
-
-
-## 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_features)
-test_features_normalized
-
-### Step 5: make a prediction using tree model
-test_predictions_trees = tree_reg.predict(test_features_normalized)
-test_predictions_trees
-
-from sklearn.metrics import mean_squared_error
-test_tree_mse = mean_squared_error(test_labels, test_predictions_trees)
-test_tree_rmse = np.sqrt(test_tree_mse)
-test_tree_rmse
-
-# Step 1: install Gradio
-!pip install --quiet gradio
-
-# Step 2: import library
-import gradio as gr
-print(gr.__version__)
-
-# Step 3.1: Define a simple "Hello World" function
-# requirement: input is text, output is text
-def greet(name):
-      return "Hello " + name + "!!"
-
-# Step 3.2: Define the input component (text style) and output component (text style) to create a simple GUI
-import gradio as gr
-input_module = gr.inputs.Textbox(label = "Input Text")
-output_module = gr.outputs.Textbox(label = "Output Text")
-
-# Step 3.3: Put all three component together into the gradio's interface function 
-gr.Interface(fn=greet, inputs=input_module, outputs=output_module).launch()
-
-# Step 5.1: Define a simple "image-to-text" function
-# requirement: input is text, output is text
-
-def caption(image):
-    return "Image is processed!!"
-
-# Step 5.2: Define the input component (image style) and output component (text style) to create a simple GUI
-import gradio as gr
-input_module = gr.inputs.Image(label = "Input Image")
-
-output_module = gr.outputs.Textbox(label = "Output Text")
-
-# Step 5.3: Put all three component together into the gradio's interface function 
-gr.Interface(fn=caption, inputs=input_module, outputs=output_module).launch()
-
-# Step 6.1: Define different input components
-import gradio as gr
-
-# a. define text data type
-input_module1 = gr.inputs.Textbox(label = "Input Text")
-
-# b. define image data type
-input_module2 = gr.inputs.Image(label = "Input Image")
-
-# c. define Number data type
-input_module3 = gr.inputs.Number(label = "Input Number")
-
-# d. define Slider data type
-input_module4 = gr.inputs.Slider(1, 100, step=5, label = "Input Slider")
-
-# e. define Checkbox data type
-input_module5 = gr.inputs.Checkbox(label = "Does it work?")
-
-# f. define Radio data type
-input_module6 = gr.inputs.Radio(choices=["park", "zoo", "road"], label = "Input Radio")
-
-# g. define Dropdown data type
-input_module7 = gr.inputs.Dropdown(choices=["park", "zoo", "road"], label = "Input Dropdown")
-
-# 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
-
-# Step 6.3: Define a new function that accommodates the input modules.
-def multi_inputs(input1, input2, input3, input4, input5, input6, input7 ):
-    import numpy as np
-    ## processing inputs
-
-    ## return outputs
-    output1 = "Processing inputs and return outputs" # text output example
-    output2 = np.random.rand(6,6) # image-like array output example
-    return output1,output2
-
-# Step 6.4: Put all three component together into the gradio's interface function 
-gr.Interface(fn=multi_inputs, 
-             inputs=[input_module1, input_module2, input_module3,
-                     input_module4, input_module5, input_module6,
-                     input_module7], 
-             outputs=[output_module1, output_module2]
-            ).launch()
-
-# Step 6.1: Define different input components
-import gradio as gr
-
-# a. define text data type
-input_module1 = gr.inputs.Slider(-124.35,-114.35, step =0.5,label = "Longitude")
-
-# b. define image data type
-input_module2 = gr.inputs.Slider(32,41, step =0.5,label = "Latitude")
-
-# c. define Number data type
-input_module3 = gr.inputs.Slider(1,52, step = 1,label = "Housing_median_age(Year)")
-
-# d. define Slider data type
-input_module4 = gr.inputs.Slider(1, 40000, step=1, label = "Total_rooms")
-
-# e. define Checkbox data type
-input_module5 = gr.inputs.Slider(1, 6441,label = "Total_bedrooms")
-
-# f. define Radio data type
-input_module6 = gr.inputs.Slider(1,6441,step = 1,label = "Population")
-
-# g. define Dropdown data type
-input_module7 = gr.inputs.Slider(1,6081,step = 1,label = "Households")
-
-input_module8 = gr.inputs.Slider(0,15,step = 1,label = "Median_income")
-
-# Step 6.2: Define different output components
-# a. define text data type
-output_module1 = gr.outputs.Textbox(label = "Predicted Housing Prices")
-
-# b. define image data type
-output_module2 = gr.outputs.Image(label = "Output Image")
-
-# you can define more output components
-
-train_set.columns
-
-#save machinel earning model to local drive
-import pickle
-#save 
-with open('tree_reg.pkl','wb') as f:
-  pickle.dump(tree_reg,f)
-
-ls
-
-# Step 6.3: Define a new function that accommodates the input modules.
-def machine_learning_model(input1, input2, input3, input4, input5, input6, input7, input8):
-    print('Start ML process')
-    import numpy as np
-    import pandas as pd
-    print(input1, input2, input3, input4, input5, input6, input7, input8)
-    #1. process the user submission
-    new_feature = np.array([[input1, input2, input3, input4, input5, input6, input7, input8]])
-    print(new_feature)
-    
-    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
-
-    ## 2. derive test features and test labels. In this case, test labels are only used for evaluation
-    #test_labels = test_set_clean["median_house_value"].copy() # get labels for output label Y
-    #test_features = test_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set
-
-    test_features_normalized = scaler.transform(test_set_clean)
-    print("test_features_normalized: ", test_features_normalized)
-    
-    with open('tree_reg.pkl','rb') as f:
-      tree_reg = pickle.load(f)
-    print("Start processing")
-
-    output1 = 'This is the output'
-    output2 = np.random.rand(28,28)
-
-    #2. follow the data preprocessing steps as we have done in the test data
-    #2.2 Check missing values in total_bedrroms
-    # 2.2 feature normalization
-
-    #3. load pre trained machine learning
-
-    
-    #4 apply loaded modeld
-    test_predictions_trees = tree_reg.predict(test_features_normalized)
-    print("Predicition is :",test_predictions_trees)
-    
-    import matplotlib.pyplot as plt
-
-    train_set.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, 
-    s=train_set["population"]/100, label="population", figsize=(10,7),
-    c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=True,
-    sharex=False)
-    plt.legend()
-    
-    #plt.show()
-    plt.xlim(-124.35,-114.35)
-    plt.ylim(32,41)
-    plt.plot([input1],[input2],marker = "X",markersize = 20, markeredgecolor="yellow", markerfacecolor="black")
-    plt.savefig('test.png')
-    #5 send back the prediciton
-    return test_predictions_trees,'test.png'
-
-gr.Interface(fn=machine_learning_model, 
-             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)