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web: gunicorn soil_nutrients.wsgi | ||
web: waitress-serve --port=$PORT soil_nutrients.wsgi:application |
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import pandas as pd | ||
import pickle | ||
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train_feature=['B2Reft2','B3Reft3','B4Reft4','B5Reft5','B6Reft6','B7Reft7','Area','SoilType','LandClass'] | ||
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base_path = "trained_model" #add your drive model folder path here | ||
####Evaluation with Random Sample | ||
model1= pickle.load(open(base_path+'/model_rf_Ph.pkl','rb')) | ||
model2= pickle.load(open(base_path+'/model_rf_K.pkl','rb')) | ||
model3= pickle.load(open(base_path+'/model_rf_B.pkl','rb')) | ||
model4= pickle.load(open(base_path+'/model_rf_Ca.pkl','rb')) | ||
model5= pickle.load(open(base_path+'/model_rf_Mg.pkl','rb')) | ||
model6= pickle.load(open(base_path+'/model_rf_Mn.pkl','rb')) | ||
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test_case=[[0.14,0.14,0.14,0.14,0.14,0.14,0,2,2]] | ||
test_case=pd.DataFrame(test_case, columns = train_feature ) | ||
# print(test_case) | ||
Ph_pred = model1.predict(test_case) | ||
K_pred = model2.predict(test_case) | ||
B_pred = model3.predict(test_case) | ||
Ca_pred = model4.predict(test_case) | ||
Mg_pred = model5.predict(test_case) | ||
Mn_pred = model6.predict(test_case) | ||
print("{0}, {1}, {2}, {3}, {4}, {5} ".format(Ph_pred,K_pred,B_pred,Ca_pred,Mg_pred,Mn_pred)) |
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