initial commit

mhv.py

@@ -1,39 +0,0 @@
-# -*- coding: utf-8 -*-
-"""mhv.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1LmvAjCNd9_oRUyNiEVGGtMWE12gY7tox
-"""
-
-!pip install --quiet gradio
-
-import gradio as gr
-input_long=gr.inputs.Slider(-124.35, -114.34, step=0.1,label="longitude")
-input_lati=gr.inputs.Slider(32, 41, step=0.5,label="latitude")
-input_hmg=gr.inputs.Slider(1, 55, step=1,label="Housing_median_age (Year)")
-input_tr=gr.inputs.Slider(1, 40000, step=12,label="Total_rooms")
-input_tb=gr.inputs.Slider(1, 6500, step=5,label="Total_Bedrooms")
-input_p=gr.inputs.Slider(3, 35678, step=5,label="Population")
-input_hh=gr.inputs.Slider(1, 6081, step=5,label="Households")
-input_mi=gr.inputs.Slider(0, 15, step=1,label="Median_income")
-output_mhv = gr.outputs.Textbox(label = "Predicted housing price")  
-output_mhv1 = gr.outputs.Image(label = "Median house value")
-
-
-
-
-
-def house_price(input_long, input_lati, input_hmg, input_tr, input_tb, input_p, input_hh, input_mi ):
-    import numpy as np
-    import pandas as pd
-    ## processing inputs
-
-    ## return outputs
-    output1 = 111253
-    output2 = np.random.rand(5,5)
-    return output1,output2
-
-iface=gr.Interface( title="house Prediction",description="This model gives the estimated price of a house." ,fn=house_price,inputs=[input_long, input_lati, input_hmg, input_tr, input_tb, input_p, input_hh, input_mi], outputs=[output_mhv, output_mhv1] )
-iface.launch()