app.py

Files regarding the radio

app.py

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+import gradio as gr
+
+input_module1 = gr.inputs.Slider(-124.35, -114.35, step=5, label = "Longitude")
+
+input_module2 = gr.inputs.Slider(32, 41, step=5, label = "Latitude")
+
+input_module3 = gr.inputs.Slider(1, 52, step=5, label = "Housing_median_age (Year)")
+
+input_module4 = gr.inputs.Slider(1, 39996, step=5, label = "Total_rooms")
+
+input_module5 = gr.inputs.Slider(1, 6441, step=5, label = "Total_bedrooms")
+
+input_module6 = gr.inputs.Slider(3, 35678, step=5, label = "Population")
+
+input_module7 = gr.inputs.Slider(1, 6081, step=5, label = "Households")
+
+input_module8 = gr.inputs.Slider(0, 15, step=5, label = "Median_income")
+
+examples = [["Longitude[-118,-123,-154,-129,-121]"], ["Latitude[32,23,12,16,17]"],["Housing_median_age (Year)[32,23,1,16,49]"], ["Total_rooms[32,23,1,16,49]"],["Total_bedrooms[32,23,1,16,49]"],["Population[3204,234563,3531,53316,35649]"], ["Households[3204,4563,3531,3316,5349]"], ["Median_income[4,11,6,3,14]"]]
+
+# 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
+import pandas as pd
+import numpy as np
+
+housing = pd.read_csv('/housing-2.csv')
+
+## 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
+
+## Step 1: training the data using decision tree algorithm
+from sklearn.tree import DecisionTreeRegressor ## import the DecisionTree Function
+tree_reg = DecisionTreeRegressor(random_state=42) ## 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
+
+def predict_house(input1, input2, input3, input4, input5, input6, input7, input8):
+  input_features = list(input1, input2, input3, input4, input5, input6, input7, input8)
+  scaler = MinMaxScaler() ## define the transformer
+  scaler.fit(input_features)
+  input_features_normalized = scaler.transform(input_features)
+  output_predictions = tree_reg.predict(input_features_normalized)
+  
+  import matplotlib as plt
+  from numpy import asarray
+  from PIL import Image
+  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.plot(input_features[0], input_features[1], 'r*', markersize=25)
+  graph = plt.show()
+  return output_predictions[0], graph
+
+# Step 6.4: Put all three component together into the gradio's interface function 
+#gr.Label('CSCI4750/5750 Demo 3: Web Application for Housing Price Prediction')
+#gr.HTML(show_label= 'CSCI4750/5750 Demo 3: Web Application for Housing Price Prediction')
+gr.Interface(fn=predict_house, 
+             inputs=[input_module1, input_module2, input_module3,
+                     input_module4, input_module5, input_module6,
+                     input_module7, input_module8], 
+             outputs=[output_module1, output_module2], examples = examples ).launch()