Upload app.py

app.py

@@ -10,12 +10,12 @@ import pandas as pd
 from huggingface_hub import CommitScheduler
 from pathlib import Path
 
-import subprocess
+# print('*** Running train.py ***')
+# import subprocess
 
-# Run the training script
-#print('*** Running train.py ***')
-#subprocess.run(["python", "train.py"])
-#print('done!')
+# # Run the training script
+# subprocess.run(["python", "train.py"])
+# print('*** done! ***')
 
 # Configure the logging functionality
 log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
@@ -59,11 +59,13 @@ model_output = gr.Label(label="charges")
 
 def predict_insurance_charges(age, bmi, children, sex, smoker, region):
     
+    #Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region'], dtype='object')
+
     sample = {
         'age': age,
+        'sex': sex,
         'bmi': bmi,
         'children': children,
-        'sex': sex,
         'smoker': smoker,
         'region': region
     }
@@ -76,17 +78,17 @@ def predict_insurance_charges(age, bmi, children, sex, smoker, region):
         with log_file.open("a") as f:
             f.write(json.dumps(
                 {
-                    'Age': age,
-                    'BMI': bmi,
-                    'Children': children,
-                    'Sex': sex,
-                    'Smoker': smoker,
-                    'Region': region,
+                    'age': age,
+                    'sex': sex,
+                    'bmi': bmi,
+                    'children': children,
+                    'smoker': smoker,
+                    'region': region,
                     'prediction': prediction[0]
                 }
             ))
             f.write("\n")
-            
+
     return prediction[0]
 
 gr_interface = gr.Interface(
@@ -106,6 +108,3 @@ gr_interface = gr.Interface(
 
 gr_interface.queue()
 gr_interface.launch(share=False)
-
-
-