import gradio as gr

# a. define text data type
input_module1 = gr.inputs.Textbox(label = "Feature1")
input_module2 = gr.inputs.Textbox(label = "Feature2")
input_module3 = gr.inputs.Textbox(label = "Feature3")
input_module4 = gr.inputs.Textbox(label = "Feature4")
input_module5 = gr.inputs.Textbox(label = "Feature5")
input_module6 = gr.inputs.Textbox(label = "Feature6")
input_module7 = gr.inputs.Textbox(label = "Feature7")
input_module8 = gr.inputs.Textbox(label = "Feature8")



# Step 6.2: Define different output components
# a. define text data type
output_module1 = gr.outputs.Textbox(label = "Predicted housing prices")


def predict_housing_price(input_module1,input_module2,input_module3,input_module4,input_module5,input_module6,input_module7,input_module8 ):
 
 import pandas as pd
 import numpy as np
 import sklearn

 housing=pd.read_csv("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

 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



 test_feature= np.array([[input_module1, input_module2, input_module3,
                     input_module4, input_module5, input_module6,
                     input_module7,input_module8]])
 normalized_test_feature= scaler.transform(test_feature)
 training_predictions = lin_reg.predict(normalized_test_feature)
 training_predictions
 return training_predictions
#gardio Interface
gr.Interface(fn=predict_housing_price, 
             inputs=[input_module1, input_module2, input_module3,
                     input_module4, input_module5, input_module6,
                     input_module7,input_module8], 
             outputs=[output_module1]
            ).launch()