# import pickle
import gradio as gr
import numpy as np
import xgboost as xgb

model1=xgb.XGBRegressor()
model2=xgb.XGBRegressor()

model1.load_model('model1.json')
model2.load_model('model2.json')

# pred = model1.predict([[1],[2],[3],[4],[5],[6]])
# print(pred[0])


def greed(Measure,Line_Item_Quantity,Line_Item_Value,Weight,Freight_Cost,Line_Item_Insurance):
    input_array=np.array([[Measure,Line_Item_Quantity,Line_Item_Value,Weight,Freight_Cost,Line_Item_Insurance]])
    pred1=model1.predict(input_array)
    pred2=model2.predict(input_array)
    return pred1,pred2

# print(greed(1,2,3,4,5,6))
# model=gr.outputs(greed(1,2,3,4,5,6))

demo = gr.Interface(
    fn=greed,
    inputs=[gr.inputs.Number(),gr.inputs.Number(),gr.inputs.Number(),gr.inputs.Number(),gr.inputs.Number(),gr.inputs.Number()],
    outputs=["number","number"],
)
demo.launch(share=True)

# print(model)

# gr.outputs.