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app.py

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+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+import pandas as pd
+import tarfile
+import urllib.request
+
+
+DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/"
+HOUSING_PATH = os.path.join("datasets", "housing")
+HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"
+
+def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):
+    if not os.path.isdir(housing_path):
+        os.makedirs(housing_path)
+    tgz_path = os.path.join(housing_path, "housing.tgz")
+    urllib.request.urlretrieve(housing_url, tgz_path)
+    housing_tgz = tarfile.open(tgz_path)
+    housing_tgz.extractall(path=housing_path)
+    housing_tgz.close()
+
+
+
+def load_housing_data(housing_path=HOUSING_PATH):
+    csv_path = os.path.join(housing_path, "housing.csv")
+    return pd.read_csv(csv_path)
+
+
+#1. Download the data
+
+fetch_housing_data()
+
+housing_pd = load_housing_data()
+housing_pd.head()
+
+## tentatively drop categorical feature
+housing = housing_pd.drop('ocean_proximity', axis=1)
+housing
+
+
+
+#2. Prepare the Data for Machine Learning Algorithms
+## 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
+
+#3. Training ML model on the Training Set
+
+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
+
+
+
+### visualize the data
+def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
+    path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
+    print("Saving figure", fig_id, ' to ',path)
+    if tight_layout:
+        plt.tight_layout()
+    plt.savefig(path, format=fig_extension, dpi=resolution)
+    
+PROJECT_ROOT_DIR='./'
+IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images")
+os.makedirs(IMAGES_PATH, exist_ok=True)
+
+images_path = os.path.join(PROJECT_ROOT_DIR, "images", "end_to_end_project")
+os.makedirs(images_path, exist_ok=True)
+DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/"
+filename = "california.png"
+print("Downloading", filename)
+url = DOWNLOAD_ROOT + "images/end_to_end_project/" + filename
+urllib.request.urlretrieve(url, os.path.join(images_path, filename))
+
+
+### written by Jie
+def draw_map_customize(longitude,latitude, fig_id='test',fig_extension='png' ):
+    import matplotlib.image as mpimg
+    california_img=mpimg.imread(os.path.join(images_path, filename))
+    ax = housing.plot(kind="scatter", x="longitude", y="latitude", figsize=(10,7),
+                      s=housing['population']/100, label="Population",
+                      c="median_house_value", cmap=plt.get_cmap("jet"),
+                      colorbar=False, alpha=0.4)
+    plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5,
+               cmap=plt.get_cmap("jet"))
+    plt.ylabel("Latitude", fontsize=18)
+    plt.xlabel("Longitude", fontsize=18)
+
+    plt.xticks(fontsize=18, rotation=0)
+    plt.yticks(fontsize=18, rotation=0)
+
+
+    plt.plot(longitude,latitude, "ro", alpha=0.7, marker=r'$\clubsuit$', markersize=30)
+
+    plt.annotate("Your location is here", xy=(longitude,latitude), xytext=(longitude+1,latitude+1), fontsize=20,
+            arrowprops=dict(arrowstyle="->"))
+    
+    
+    prices = housing["median_house_value"]
+    tick_values = np.linspace(prices.min(), prices.max(), 11)
+    cbar = plt.colorbar(ticks=tick_values/prices.max())
+    cbar.ax.set_yticklabels(["$%dk"%(round(v/1000)) for v in tick_values], fontsize=14)
+    cbar.set_label('Median House Value', fontsize=16)
+    
+    plt.legend(fontsize=16)
+    save_fig(fig_id)
+    #plt.show()
+    
+    path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
+    return path
+
+
+
+def get_sample_data(num_data):
+    sample_data = []
+    for i in range(num_data):
+      samp = housing.sample(1)
+      longitude = float(samp['longitude'].values[0])
+      latitude = float(samp['latitude'].values[0])
+      housing_median_age = float(samp['housing_median_age'].values[0])
+      total_rooms = float(samp['total_rooms'].values[0])
+      total_bedrooms = float(samp['total_bedrooms'].values[0])
+      population = float(samp['population'].values[0])
+      households = float(samp['households'].values[0])
+      median_income = float(samp['median_income'].values[0])
+
+      sample_data.append([longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income]) 
+    return sample_data
+
+
+def predict_price(longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income):
+    # initialize data of lists.
+    data = {'longitude':float(longitude),
+            'latitude':float(latitude),
+            'housing_median_age':float(housing_median_age),
+            'total_rooms':float(total_rooms),
+            'total_bedrooms':float(total_bedrooms),
+            'population':float(population),
+            'households':float(households),
+            'median_income':float(median_income),
+    }
+    test_features = pd.DataFrame(columns=['longitude', 'latitude', 'housing_median_age', 'total_rooms',
+                      'total_bedrooms', 'population', 'households', 'median_income'])
+    # Create DataFrame
+    test_features = test_features.append(data,ignore_index=True)
+    test_features = test_features.dropna(subset=["total_bedrooms"])
+
+    ## 3. 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
+
+    pred = lin_reg.predict(test_features_normalized)[0]
+    
+    map_file = draw_map_customize(longitude,latitude, fig_id='test',fig_extension='png' )
+
+    return pred,map_file
+
+
+### configure inputs/outputs
+
+
+set_longitude = gr.inputs.Slider(-124.350000, -114.310000, step=0.5, default=-120, label = 'Longitude')
+set_latitude = gr.inputs.Slider(32, 41, step=0.5, default=33, label = 'Latitude')
+set_housing_median_age = gr.inputs.Slider(1, 52, step=1, default=10, label = 'Housing_median_age (Year)')
+set_total_rooms = gr.inputs.Slider(1, 40000, step=5, default=10000, label = 'Total_rooms')
+set_total_bedrooms = gr.inputs.Slider(1, 6445, step=5, default=5000, label = 'Total_bedrooms')
+set_population = gr.inputs.Slider(3, 35682, step=5, default=10, label = 'Population')
+set_households = gr.inputs.Slider(1, 6082, step=5, default=10, label = 'Households')
+set_median_income = gr.inputs.Slider(0, 15, step=0.5, default=10, label = 'Median_income')
+
+
+
+set_label = gr.outputs.Textbox(label="Predicted Housing Prices")
+
+# define output as the single class text
+set_out_images = gr.outputs.Image(label="Closest Neighbors")
+
+
+### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider
+interface = gr.Interface(fn=predict_price, 
+                         inputs=[set_longitude, set_latitude,set_housing_median_age,set_total_rooms,set_total_bedrooms,set_population,set_households,set_median_income], 
+                         outputs=[set_label,set_out_images],
+                         examples_per_page = 2,
+                         examples = get_sample_data(10), 
+                         title="CSCI4750/5750 Demo 3: Web Application for Housing Price Prediction", 
+                         description= "Click examples below for a quick demo",
+                         theme = 'huggingface',
+                         layout = 'vertical'
+                         )
+interface.launch(debug=True)