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| import pickle | |
| import numpy as np | |
| import gradio as gr | |
| import sklearn | |
| filename= "model.pkl" | |
| model_ = pickle.load(open(filename, 'rb')) | |
| def crop_yield(Planted,Size_of_land, Bags, Soil_Type, Type_of_Seed, Color_of_Seeds): | |
| prediction = model_.predict([[Planted,Size_of_land, Bags, Soil_Type, Type_of_Seed,Color_of_Seeds]]) | |
| return prediction | |
| #create input and output objects | |
| #input object1 | |
| input1 = gr.inputs.Number(label="Planted") | |
| #input object 2 | |
| input2 = gr.inputs.Number(label="Size of land (Hectare)") | |
| #input object3 | |
| input3 = gr.inputs.Number(label="Number of Bags") | |
| #input object 3 | |
| input4 = gr.inputs.Number(label="Soil Type") | |
| #input object 3 | |
| input5 = gr.inputs.Number(label="Type of Seed") | |
| #input object 3 | |
| input6 = gr.inputs.Number(label="Color of Seeds") | |
| #output object | |
| output = gr.outputs.Textbox(label= "Number of Yields") | |
| #create interface | |
| gui = gr.Interface(fn=crop_yield, | |
| inputs=[input1, input2, input3, input4, input5, input6], | |
| outputs=output).launch() |