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| import pickle | |
| import gradio as gr | |
| #from sklearn.metrics import classification_report | |
| #from sklearn.model_selection import train_test_split | |
| #from sklearn.linear_model import LogisticRegression | |
| def make_prediction(age1,gender1,occupation1,line_of_work1,time_bp1,time_dp1,travel_time1,easeof_online1,home_env1,prod_inc1,sleep_bal1,new_skill1,fam_connect1,relaxed1,self_time1,like_hw1,dislike_hw1,certaindays_hw1): | |
| #print(age1+gender1+occupation1+line_of_work1+time_bp1+time_dp1+travel_time1+easeof_online1+home_env1+prod_inc1+sleep_bal1+new_skill1+fam_connect1+relaxed1+self_time1+like_hw1+dislike_hw1+certaindays_hw1) | |
| print(age1+gender1+occupation1+line_of_work1+certaindays_hw1) | |
| if age1=="18": | |
| age1=int(1) | |
| elif age1=="19-25": | |
| age1=int(0) | |
| elif age1=="33-40": | |
| age1=int(2) | |
| elif age1=="60+": | |
| age1=int(3) | |
| elif age1=="26-32": | |
| age1=int(4) | |
| elif age1=="40-50": | |
| age1=int(5) | |
| else: age1=int(6) | |
| print(str(age1)) | |
| if gender1=="Male": | |
| gender1=int(0) | |
| elif gender1=="Female": | |
| gender1=int(1) | |
| else: gender1=int(2) | |
| print(str(gender1)) | |
| if occupation1=="Student in College": | |
| occupation1=int(0) | |
| elif occupation1=="Student in School": | |
| occupation1=int(1) | |
| elif occupation1=="Working Professional": | |
| occupation1=int(2) | |
| elif occupation1=="Entrepreneur": | |
| occupation1=int(3) | |
| elif occupation1=="Retired/Senior Citizen": | |
| occupation1=int(4) | |
| elif occupation1=="Homemaker": | |
| occupation1=int(5) | |
| else: occupation1=int(6) | |
| print(str(occupation1)) | |
| if line_of_work1=="Teaching": | |
| line_of_work1=int(0) | |
| elif line_of_work1=="Engineering": | |
| line_of_work1=int(1) | |
| elif line_of_work1=="Management": | |
| line_of_work1=int(2) | |
| elif line_of_work1=="APSPDCL": | |
| line_of_work1=int(3) | |
| elif line_of_work1=="Architecture": | |
| line_of_work1=int(4) | |
| elif line_of_work1=="Other": | |
| line_of_work1=int(5) | |
| else: line_of_work1=int(6) | |
| print(str(line_of_work1)) | |
| if certaindays_hw1=="Yes": | |
| certaindays_hw1=int(0) | |
| elif certaindays_hw1=="No": | |
| certaindays_hw1=int(1) | |
| else: certaindays_hw1=int(2) | |
| print(str(certaindays_hw1)) | |
| with open("covid_psyc_model.pkcls", "rb") as f: | |
| #Then feeds our data into the model, then sets the "preds" variable to the prediction output for our class variable, which is price. | |
| clf = pickle.load(f) | |
| preds=clf.predict([[age1,gender1,occupation1,line_of_work1,time_bp1,time_dp1,travel_time1,easeof_online1,home_env1,prod_inc1,sleep_bal1,new_skill1,fam_connect1,relaxed1,self_time1,like_hw1,dislike_hw1,certaindays_hw1]]) | |
| if preds == 0: | |
| return "Complete Physical Attendance" | |
| elif preds== 1: | |
| return "Work/study from home" | |
| else: | |
| return "Please check and re-enter your inputs" | |
| #Finally, we send the prediction to the website. | |
| return preds | |
| HasAge=gr.Dropdown(["18","19-25","26-32","33-40","40-50","50-60","60+"],label="Please select your age range") | |
| HasGender=gr.Dropdown(["Male","Female","Prefer not to say"],label="Please select your gender") | |
| HasOccupation=gr.Dropdown(["Student in College", "Student in School", "Working Professional", | |
| "Entrepreneur", "Retired/Senior Citizen", "Homemaker", | |
| "Currently Out of Work", | |
| "Medical Professional aiding efforts against COVID-19"],label="Please select your occupation") | |
| HasLineOfWork=gr.Dropdown(["Teaching","Engineering","Management","APSPDCL","Architecture","Other","Government Employee"],label="Please select your line of work") | |
| HasTimeBP=gr.Slider(minimum=4,maximum=12,step=1,label="How many hours before pandemic did the employee work?") | |
| HasTimeDP=gr.Slider(minimum=4,maximum=12,step=1,label="How many hours after pandemic did the employee work?") | |
| HasTravelTime=gr.Slider(minimum=0.5,maximum=3,step=0.5,label="How many hours does the employee travel for work?") | |
| HasEaseOfOnline=gr.Slider(minimum=1,maximum=5,step=1,label="On a scale of 1-5, how easy does the employee find to work online?") | |
| HasHomeEnv=gr.Slider(minimum=1,maximum=5,step=1,label="On a scale of 1-5, how comfortable is the employee in home environment?") | |
| HasProdInc=gr.Slider(minimum=-1,maximum=1,step=0.5,label="On a scale of -1 to 1, how productive is the employee?") | |
| HasSleepBal=gr.Slider(minimum=-1,maximum=1,step=0.5,label="On a scale of -1 to 1, how is the employee's sleep cycle?") | |
| HasNewSkill=gr.Slider(minimum=-1,maximum=1,step=0.5,label="On a scale of -1 to 1, how likely is it that the employee learnt a new skill?") | |
| HasFamConnect=gr.Slider(minimum=-1,maximum=1,step=0.5,label="On a scale of -1 to 1, how is the employee's family connection impacted?") | |
| HasRelaxed=gr.Slider(minimum=-1,maximum=1,step=0.5,label="On a scale of -1 to 1, how relaxed does the employee feel?") | |
| HasSelfTime=gr.Slider(minimum=-1,maximum=1,step=0.5,label="On a scale of -1 to 1, how likely does the employee feel the presence of self-time?") | |
| HasLikeHW=gr.Slider(minimum=0,maximum=1,step=0.1,label="On a scale of 0 to 1, how much does the employee like working from home?") | |
| HasDislikeHW=gr.Slider(minimum=0,maximum=1,step=0.1,label="On a scale of 0 to 1, how much does the employee dislike working from home?") | |
| HasCertainDaysHW=gr.Dropdown(["Yes","No","Maybe"],label="Is the employee okay to work in a hybrid setting?") | |
| output = gr.Textbox(label="Employee Preference Prediction:") | |
| app = gr.Interface(fn = make_prediction, inputs=[HasAge,HasGender,HasOccupation,HasLineOfWork,HasTimeBP,HasTimeDP,HasTravelTime,HasEaseOfOnline,HasHomeEnv,HasProdInc,HasSleepBal,HasNewSkill,HasFamConnect,HasRelaxed,HasSelfTime,HasLikeHW,HasDislikeHW,HasCertainDaysHW], outputs=output) | |
| app.launch() |