Add application file

app.py

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+import gradio as gr
+
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+
+housing = pd.read_csv("housing.csv")
+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
+
+import numpy as np
+def predict_price(input1, input2, input3, input4, input5, input6, input7, input8):
+  features = np.array([[float(input1), float(input2), float(input3), float(input4), float(input5), float(input6), float(input7), float(input8)]])
+  print("recived features are: ", features)
+  price = lin_reg.predict(features)
+  return price
+
+input_module1 = gr.inputs.Textbox(label = "Input Feature 1")
+input_module2 = gr.inputs.Textbox(label = "Input Feature 2")
+input_module3 = gr.inputs.Textbox(label = "Input Feature 3")
+input_module4 = gr.inputs.Textbox(label = "Input Feature 4")
+input_module5 = gr.inputs.Textbox(label = "Input Feature 5")
+input_module6 = gr.inputs.Textbox(label = "Input Feature 6")
+input_module7 = gr.inputs.Textbox(label = "Input Feature 7")
+input_module8 = gr.inputs.Textbox(label = "Input Feature 8")
+
+output_module1 = gr.outputs.Textbox(label = "Output Text")
+
+gr.Interface(fn=predict_price,
+             inputs=[input_module1, input_module2, input_module3,
+                     input_module4, input_module5, input_module6,
+                     input_module7, input_module8],
+             outputs=[output_module1]
+            ).launch()