Create app.py

app.py

@@ -0,0 +1,57 @@
+def house_price_prediction(ft1,ft2,ft3,ft4,ft5,ft6,ft7,ft8):
+#  output=1
+ import pandas as pd
+ housing=pd.read_csv("/content/drive/MyDrive/Colab Notebooks/lab 03/housing.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
+ #model training
+ 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
+ #model prediction
+ import numpy as np
+ test_features=np.array([[ft1,ft2,ft3,ft4,ft5,ft6,ft7,ft8]])
+ training_predictions = lin_reg.predict(test_features)
+ return training_predictions
+
+  #return output
+
+
+import gradio as gr
+
+ip1 = gr.inputs.Slider(-124.35, -114.35, step=5, label = "Longitude")
+ip2 = gr.inputs.Slider(32,41, step=5, label = "Latitude")
+ip3 = gr.inputs.Slider(1,52, step=5, label = "Housing_median_age (Year)")
+ip4 = gr.inputs.Slider(1,39996, step=5, label = "Total_rooms")
+ip5 = gr.inputs.Slider(1,6441, step=5, label = "Total_bedrooms")
+ip6 = gr.inputs.Slider(3,35678, step=5, label = "Population")
+ip7 = gr.inputs.Slider(1,6081, step=5, label = "Households")
+ip8 = gr.inputs.Slider(0,15, step=5, label = "Median_income")
+op_module = gr.outputs.Textbox(label = "Output")
+
+gr.Interface(fn=house_price_prediction, 
+             inputs=[ip1, ip2, ip3,
+                     ip4, ip5, ip6,
+                     ip7,ip8], 
+             outputs=[op_module]
+            ).launch(debug= True)
+op_module = gr.outputs.Textbox(label = "Output")