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app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+
+
+age_input = gr.Number(label="Age")
+duration_input = gr.Number(label='Duration(Sec)')
+cc_contact_freq_input = gr.Number(label='CC Contact Freq')
+days_since_pc_input = gr.Number(label='Days Since PC')
+pc_contact_freq_input = gr.Number(label='Pc Contact Freq')
+job_input = gr.Dropdown(['admin.', 'blue-collar', 'technician', 'services', 'management',
+       'retired', 'entrepreneur', 'self-employed', 'housemaid', 'unemployed',
+       'student', 'unknown'],label="Job")
+marital_input = gr.Dropdown(['married', 'single', 'divorced', 'unknown'],label='Marital Status')
+education_input = gr.Dropdown(['experience', 'university degree', 'high school', 'professional.course',
+       'Others', 'illiterate'],label='Education')
+defaulter_input = gr.Dropdown(['no', 'unknown', 'yes'],label='Defaulter')
+home_loan_input = gr.Dropdown(['yes', 'no', 'unknown'],label='Home Loan')
+personal_loan_input = gr.Dropdown(['yes', 'no', 'unknown'],label='Personal Loan')
+communication_type_input = gr.Dropdown(['cellular', 'telephone'],label='Communication Type')
+last_contacted_input = gr.Dropdown(['may', 'jul', 'aug', 'jun', 'nov', 'apr', 'oct', 'mar', 'sep', 'dec'],label='Last Contacted')
+day_of_week_input = gr.Dropdown(['thu', 'mon', 'wed', 'tue', 'fri'],label='Day of Week')
+pc_outcome_input = gr.Dropdown(['nonexistent', 'failure', 'success'], label='PC Outcome')
+
+o = gr.Textbox()
+
+# load the model
+model = joblib.load('model.joblib')
+i2l = ['Not subscribed', 'Subscribed']
+def fn(age, duration, cc_contact_freq, days_since_pc, pc_contact_freq, job, marital_status, education, 
+                         defaulter, home_loan, personal_loan, communication_type, last_contacted, 
+                         day_of_week, pc_outcome):
+    sample = {
+        'Age': age,
+        'Duration(Sec)': duration,
+        'CC Contact Freq': cc_contact_freq,
+        'Days Since PC': days_since_pc,
+        'PC Contact Freq': pc_contact_freq,
+        'Job': job,
+        'Marital Status': marital_status,
+        'Education': education,
+        'Defaulter': defaulter,
+        'Home Loan': home_loan,
+        'Personal Loan': personal_loan,
+        'Communication Type': communication_type,
+        'Last Contacted': last_contacted,
+        'Day of Week': day_of_week,
+        'PC Outcome': pc_outcome,
+    }
+    print(f'sample: {sample}')
+    data_point = pd.DataFrame([sample])
+    print(f'{data_point}')
+    result = model.predict(data_point)
+    result = result[0]
+    print(result)
+    return i2l[result]
+
+
+interface = gr.Interface(fn, 
+                         inputs = [age_input,
+                        duration_input,
+                        cc_contact_freq_input,
+                        days_since_pc_input,
+                        pc_contact_freq_input,
+                        job_input,
+                        marital_input,
+                        education_input,
+                        defaulter_input,
+                        home_loan_input,
+                        personal_loan_input,
+                        communication_type_input,
+                        last_contacted_input,
+                        day_of_week_input,
+                        pc_outcome_input], 
+                         outputs = o)
+
+interface.launch(share=True)

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c3c382c7233f0463a9c2698c7190fa6a89f2704433ae79735d9a6a1acfb5529
+size 8439

requirements.txt

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+joblib
+scikit-learn==1.2.2