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| import pickle | |
| import pandas as pd | |
| import numpy as np | |
| import xgboost as xgb | |
| import gradio as gr | |
| import pathlib | |
| #plt = platform.system() | |
| #if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath | |
| model_path = "model.None" | |
| model = xgb.Booster() | |
| model.load_model(model_path) | |
| dv_path = "dv.bin" | |
| with open(dv_path, 'rb') as f_out: | |
| dv = pickle.load(f_out) | |
| scaler_path = "scaler.bin" | |
| with open(scaler_path, 'rb') as f_out: | |
| scaler = pickle.load(f_out) | |
| def preprocess(data): | |
| """Preprocessing of the data""" | |
| # turn json input to dataframe | |
| data = pd.DataFrame([data]) | |
| # define numerical and categorical features | |
| numerical = ["X1", "X2", "X3", "X4", "X5", "X7"] | |
| categorical = ["X6", "X8"] | |
| # preprocess numerical features | |
| X_num = scaler.transform(data[numerical]) | |
| # preprocess categorical features | |
| data[categorical] = data[categorical].astype("string") | |
| X_dicts = data[categorical].to_dict(orient="records") | |
| X_cat = dv.transform(X_dicts) | |
| # concatenate both | |
| X = np.concatenate((X_num, X_cat), axis=1) | |
| return X | |
| def predict(X): | |
| """make predictions""" | |
| pred = model.predict(X) | |
| print('prediction', pred[0]) | |
| return float(pred[0]) | |
| def main(X1,X2,X3,X4,X5,X6,X7,X8): | |
| """request input, preprocess it and make prediction""" | |
| input_data = { | |
| "X1": X1, | |
| "X2": X2, | |
| "X3": X3, | |
| "X4": X4, | |
| "X5": X5, | |
| "X6": X6, | |
| "X7": X7, | |
| "X8": X8 | |
| } | |
| features = preprocess(input_data) | |
| features_2 = xgb.DMatrix(features) | |
| pred = predict(features_2) | |
| result = {'heat load': pred} | |
| return pred | |
| def classify_image(img): | |
| pred,idx,probs = learn.predict(img) | |
| return dict(zip(categories,map(float,probs))) | |
| #create input and output objects | |
| #input | |
| input1 = gr.inputs.Number() | |
| input2 = gr.inputs.Number() | |
| input3 = gr.inputs.Number() | |
| input4 = gr.inputs.Number() | |
| input5 = gr.inputs.Number() | |
| input6 = gr.inputs.Number() | |
| input7 = gr.inputs.Number() | |
| input8 = gr.inputs.Number() | |
| #output object | |
| output = gr.outputs.Textbox() | |
| intf = gr.Interface(title = "Energy Efficiency", | |
| description = "The objective of this project is to predict the Heating Load based on various building features.", | |
| fn=main, | |
| inputs=[input1,input2,input3,input4,input5,input6,input7,input8], | |
| outputs=[output], | |
| live=True, | |
| enable_queue=True | |
| ) | |
| intf.launch() |