initial commit

mhv.py

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+
+
+!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
+
+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] ).launch()