Create app.py

app.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.linear_model import LinearRegression
+import gradio as gr
+
+longitude = gr.inputs.Textbox(label = "Longitude")
+latitude = gr.inputs.Textbox(label = "Latitude")
+housing_median_age = gr.inputs.Textbox(label = "Housing median age")
+total_rooms = gr.inputs.Textbox(label = "total rooms")
+total_bedrooms = gr.inputs.Textbox(label = "total bedrooms")
+population = gr.inputs.Textbox(label = "population")
+households = gr.inputs.Textbox(label = "housholds")
+median_income = gr.inputs.Textbox(label = "median income")
+output_house_value = gr.inputs.Textbox(label = "predicted house value")
+
+def process_function(longitude,latitude,housing_medain_age,total_rooms,total_bedrooms,population,households,median_income):
+  housing=pd.read_csv('/content/drive/MyDrive/housing.csv')
+  train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10)
+  train_set_clean = train_set.dropna(subset=["total_bedrooms"])
+  train_labels = train_set_clean["median_house_value"].copy()
+  train_features = train_set_clean.drop("median_house_value", axis=1)
+  scaler = MinMaxScaler()
+  scaler.fit(train_features)
+  train_features_normalized = scaler.transform(train_features)
+  lin_reg=LinearRegression()
+  lin_reg.fir(train_features_normalized,train_labels)
+  new_features=np.array([[longitude,latitude,housing_medain_age,total_rooms,population,households,median_income]])
+  new_features_normalized=scaler.transform(new_features)
+  output_house_value=lin_reg.predict(new_features_normalized)
+  return output_house_value
+myexamples=[["-116.52", "33.82", "21.0", "10227.0", "2315.0", "3623.0","1734.0", "2.5212"]]
+iface = gr.Interface(
+    fn=process_function,
+    inputs=[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income],
+    outputs=output_house_value,
+    examples=myexamples,
+)
+iface.launch(share=True, debug=True)