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| import gradio as gr | |
| import pandas as pd | |
| import joblib | |
| filename = 'anxiousAmazon.sav' | |
| rf = joblib.load(filename) | |
| forest_name = "ciro-c/emobot" | |
| def calc_preds(df): | |
| pred = rf.predict(df) | |
| if pred == 1: | |
| return 'Has anxiety' | |
| else: | |
| return 'Don\'t have anxiety' | |
| def process_inputs(Gender, Age, Major, Year, CGPA, Marriage, Depression, Panic, Treatment): | |
| df = pd.DataFrame(columns=['Marriage', 'Major', 'Depression', 'Panic', 'Treatment', 'Gender', 'CGPA', 'Year', 'Age']); | |
| df.loc[0] = [Marriage, Major, Depression, Panic, Treatment,Gender, CGPA, Year, Age]; | |
| df['Marriage'] = df['Marriage'].map({'Yes': 1, 'No': 0}) | |
| df['Depression'] = df['Depression'].map({'Yes': 1, 'No': 0}) | |
| df['Anxiety'] = df['Anxiety'].map({'Yes': 1, 'No': 0}) | |
| df['Panic'] = df['Panic'].map({'Yes': 1, 'No': 0}) | |
| df['Treatment'] = df['Treatment'].map({'Yes': 1, 'No': 0}) | |
| df['Gender'] = df['Gender'].map({'Female': 0, 'Male': 1}) | |
| df['CGPA'] = df['CGPA'].map({'3.00 - 3.49':3,'3.50 - 4.00':4,'2.50 - 2.99':2,'2.00 - 2.49':1,'0 - 1.99':0}) | |
| df['Major'] = df['Major'].map({ | |
| 'ENGINEERING':16, | |
| 'ISLAMIC EDUCATION':23, | |
| 'ACCOUNTING ':0, | |
| 'BCS':3, | |
| 'BIT':7, | |
| 'LAWS':30, | |
| 'MATHEMATHICS':33, | |
| 'PENDIDIKAN ISLAM':36, | |
| 'HUMAN RESOURCES':20, | |
| 'IRKHS':22, | |
| 'PSYCHOLOGY':38, | |
| 'KENMS':25, | |
| 'ENM':17, | |
| 'MARINE SCIENCE':32, | |
| 'KOE':27, | |
| 'BANKING STUDIES':2, | |
| 'BUSINESS ADMINISTRATION':8, | |
| 'LAW':29, | |
| 'KIRKHS':26, | |
| 'USULUDDIN ':41, | |
| 'TAASL':40, | |
| 'ENGINE':15, | |
| 'ALA':1, | |
| 'BIOMEDICAL SCIENCE':5, | |
| 'BENL':4, | |
| 'IT':24, | |
| 'CTS':10, | |
| 'ENGIN':14, | |
| 'ECONS':13, | |
| 'MHSC':34, | |
| 'MALCOM':31, | |
| 'KOP':28, | |
| 'HUMAN SCIENCES ':21, | |
| 'BIOTECHNOLOGY':6, | |
| 'COMMUNICATION ':9, | |
| 'DIPLOMA NURSING':11, | |
| 'PENDIDIKAN ISLAM ':37, | |
| 'RADIOGRAPHY':39, | |
| 'FIQH FATWA ':19, | |
| 'DIPLOMA TESL':12, | |
| 'FIQH':18, | |
| 'NURSING ':35 | |
| }) | |
| return calc_preds(df) | |
| gender_input = gr.Radio(choices=["Female", "Male"], label="Gender") | |
| age_input = gr.Number(label="Age") | |
| major_input = gr.Radio(choices=['ENGINEERING', 'ISLAMIC EDUCATION', 'BIT', 'LAWS', 'MATHEMATHICS', 'PENDIDIKAN ISLAM', 'BCS', 'HUMAN RESOURCES', 'IRKHS', 'PSYCHOLOGY', 'KENMS', 'ACCOUNTING ', 'ENM', 'MARINE SCIENCE', 'KOE', 'BANKING STUDIES', 'BUSINESS ADMINISTRATION', 'LAW', 'KIRKHS', 'USULUDDIN ', 'TAASL', 'ENGINE', 'ALA', 'BIOMEDICAL SCIENCE', 'BENL', 'IT', 'CTS', 'ENGIN', 'ECONS', 'MHSC', 'MALCOM', 'KOP', 'HUMAN SCIENCES ', 'BIOTECHNOLOGY', 'COMMUNICATION ', 'DIPLOMA NURSING', 'PENDIDIKAN ISLAM ', 'RADIOGRAPHY', 'FIQH FATWA ', 'DIPLOMA TESL', 'FIQH', 'NURSING '], label="Major") | |
| year_input = gr.Radio(choices=['YEAR 1', 'YEAR 2', 'YEAR 3', 'YEAR 4'], label="Year") | |
| cgpa_input = gr.Radio(choices=['3.00 - 3.49', '3.50 - 4.00', '2.50 - 2.99', '2.00 - 2.49', '0 - 1.99'], label="CGPA") | |
| marriage_input = gr.Radio(choices=["Yes", "No"], label="Marriage") | |
| depression_input = gr.Radio(choices=["Yes", "No"], label="Depression") | |
| panic_input = gr.Radio(choices=["Yes", "No"], label="Panic") | |
| treatment_input = gr.Radio(choices=["Yes", "No"], label="Treatment") | |
| interface = gr.Interface( | |
| fn=process_inputs, | |
| inputs=[gender_input, age_input, major_input, year_input, cgpa_input, marriage_input, depression_input, panic_input, treatment_input], | |
| outputs="text" | |
| ) | |
| interface.launch() |