Upload app.py

app.py

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+
+import gradio as gr
+from joblib import load
+import pandas as pd
+
+dv , model = load("train_model.joblib")
+
+
+# creating a predict function to be passed into gradio UI
+def predict(age, job, marital, education, default, housing,
+            loan, contact, month,day_of_week,campaign,pdays,
+            previous,poutcome,cons_price_idx,cons_conf_idx,emp_var_rate):
+  
+    customer = {
+        'age': age,
+        'job': job,
+        'marital': marital,
+        'education': education,
+        'default': default, 
+        'housing': housing,
+        'loan': loan,
+        'contact': contact,
+        'month': month,
+        'day_of_week': day_of_week,
+        'campaign': campaign,
+        'pdays': pdays,
+        'previous': previous,
+        'poutcome': poutcome,
+        'cons_price_idx': cons_price_idx,
+        'cons_conf_idx': cons_conf_idx,
+        'emp_var_rate': emp_var_rate
+    }
+    
+    print(customer)
+    df_transformed = dv.transform([customer])
+    prediction = model.predict_proba(df_transformed)[:,1]
+    
+#     Desposited = prediction >= 0.50
+    
+#     result = {
+#         "deposit_probability": float(prediction),
+#         "Deposited": bool(Deposited)
+#     } 
+    print(f' The probabilty of depositing in the bank is  : {str(prediction)}')
+    
+    return str(prediction)
+
+age = gr.inputs.Slider(minimum=1,default = 35, maximum=100, label = 'age') #default=data['age'].mean()
+job = gr.inputs.Dropdown(choices=["housemaid", "services","admin.","blue-collar","technician",
+                                 "retired","management","unemployed","self-employed","unknown",
+                                  "entrepreneur","student"],label = 'job')
+marital = gr.inputs.Dropdown(choices=["married", "single","divorced","unknown"],label = 'marital')
+education = gr.inputs.Dropdown(choices=["basic.4y", "high.school","basic.6y","basic.9y","professional.course",
+                                 "unknown","university.degree","illiterate"],label = 'education')
+default = gr.inputs.Dropdown(choices=["yes", "no","unknown"],label = 'default')
+housing = gr.inputs.Dropdown(choices=["yes", "no","unknown"],label = 'housing')
+loan = gr.inputs.Dropdown(choices=["yes", "no","unknown"],label = 'loan')
+contact = gr.inputs.Dropdown(choices=["telephone", "cellular"],label = 'contact')
+month = gr.inputs.Dropdown(choices=['may', 'jun', 'jul', 'aug', 'oct', 'nov', 'dec',
+                                    'mar', 'apr','sep'],label = 'month')
+day_of_week = gr.inputs.Dropdown(choices=['mon', 'tue', 'wed', 'thu', 'fri'],label = 'day_of_week')
+campaign = gr.inputs.Slider(minimum=1,default = 2, maximum=56, label = 'campaign') 
+pdays = gr.inputs.Slider(minimum=0,default = 0, maximum=27, label = 'pdays') 
+previous = gr.inputs.Slider(minimum=0,default = 0, maximum=7, label = 'previous') 
+poutcome = gr.inputs.Dropdown(choices=["nonexistent", "failure","success"],label = 'poutcome')
+cons_price_idx = gr.inputs.Slider(minimum=92,default = 94, maximum=95, label = 'cons_price_idx')
+cons_conf_idx = gr.inputs.Slider(minimum=-51,default = -42, maximum=-27, label = 'cons_conf_idx')
+emp_var_rate = gr.inputs.Slider(minimum=4964,default = 5191, maximum=5228, label = 'emp_var_rate')
+
+
+
+
+
+iface = gr.Interface(predict,[age, job, marital, education, default, housing,
+            loan, contact, month,day_of_week,campaign,pdays,
+            previous,poutcome,cons_price_idx,cons_conf_idx,emp_var_rate],
+             outputs = "number",
+            interpretation="default"
+             )
+iface.launch(share=True)
+
+
+
+
+