gradio stuff

app.py

@@ -36,7 +36,14 @@ scheduler = CommitScheduler(
 # Define the predict function which will take features, convert to dataframe and make predictions using the saved model
 # the functions runs when 'Submit' is clicked or when a API request is made
 def predict(age, bmi, children, sex, smoker, region):
-
+    # input2 = {
+    #     "age": 10,
+    #     "bmi": 30,
+    #     "children": 6,
+    #     "sex": 'female',
+    #     "smoker": 'yes',
+    #     "region": 'northwest'
+    # }
     input = {
         "age": age,
         "bmi": bmi,
@@ -47,7 +54,7 @@ def predict(age, bmi, children, sex, smoker, region):
     }
 
     input_df = pd.DataFrame([input])
-    prediction = insurance_model.predict(input_df).to_list()
+    prediction = insurance_model.predict(input_df)
 
     # While the prediction is made, log both the inputs and outputs to a  log file
     # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
@@ -77,8 +84,8 @@ def predict(age, bmi, children, sex, smoker, region):
 age_input = gr.Number(label="Age")
 bmi_input = gr.Number(label="BMI")
 children_input = gr.Slider(minimum=0.0, maximum=15.0, step=1.0, label="Children")
-sex_input = gr.Dropdown(['male', 'female'], label="sex")
-smoker_input = gr.Checkbox(['no', 'yes'], label="smoker")
+sex_input = gr.Radio(['male', 'female'], label="sex")
+smoker_input = gr.Radio(choices=['yes', 'no'], label="smoker")
 region_input = gr.Dropdown(['southwest', 'southeast', 'northwest', 'northeast'], label="region")
 output = gr.Label(label="Insurance Price")
 
@@ -97,4 +104,5 @@ demo = gr.Interface(
 # Launch with a load balancer
 # these two lines were in the file already
 demo.queue()
+# demo.launch(share=True, debug=True)
 demo.launch(share=False)