Update app.py

app.py

@@ -3,7 +3,6 @@ import os
 import uuid
 import joblib
 import json
-
 import gradio as gr
 import pandas as pd
 
@@ -11,7 +10,7 @@ from huggingface_hub import CommitScheduler
 from pathlib import Path
 
 
-# Preparing the logging functionality
+# logging
 log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
 log_folder = log_file.parent
 
@@ -23,65 +22,87 @@ scheduler = CommitScheduler(
      every=2
  )
 
-charges_predictor = joblib.load('model.joblib')
+charges_predictor = joblib.load("model.joblib")
+
+
+# Inputs
+
+age_input = gr.Number(label="Age")
+bmi_input = gr.Number(label="BMI")
+children_input = gr.Number(label="Children")
+
+sex_input = gr.Dropdown(
+    ["male", "female"],
+    label="Sex"
+)
+
+smoker_input = gr.Dropdown(
+    ["yes", "no"],
+    label="Smoker"
+)
+
+region_input = gr.Dropdown(
+    ["southeast", "southwest", "northeast", "northwest"],
+    label="Region"
+)
+
+
+model_output = gr.Textbox(label="Predicted Insurance Cost")
 
-age_input = gr.Number(label='Age')
-bmi_input = gr.Number(label='BMI')
-children_input = gr.Number(label='Children')
-sex_input = gr.Dropdown(['male', 'female', 'N/A'], value='N/A', label='Sex')
-smoker_input = gr.Dropdown(['yes', 'no', 'N/A'], value='N/A', label="Smoker")
-region_input = gr.Dropdown(['southeast', 'southwest', 'northeast', 'northwest', 'N/A'], 
-                           value='N/A', label='Region')
 
+# prediction function
 
-model_output = gr.Label(label='Charges')
+def predict_charges(age, bmi, children, sex, smoker, region):
 
-# The function runs when 'Submit' is clicked or when a API request is made
-def predict_charges(age, bmi, children, sex, smoker, region, prediction):
     sample = {
-        'age': age,
-        'bmi': bmi,
-        'children': children,
-        'sex': sex,
-        'smoker': smoker,
-        'region': region,
-        'prediction': prediction
+        "age": age,
+        "bmi": bmi,
+        "children": children,
+        "sex": sex,
+        "smoker": smoker,
+        "region": region
     }
+
     data_point = pd.DataFrame([sample])
-    print('data point: ', data_point)
-    prediction = charges_predictor.predict(data_point).tolist()
 
+    prediction = charges_predictor.predict(data_point).tolist()
 
+    # logging
     with scheduler.lock:
-          with log_file.open("a") as f:
-              f.write(json.dumps(
-                 {
-                     'age': age,
-                     'bmi': bmi,
-                     'children': children,
-                     'sex': sex,
-                     'smoker': smoker,
-                     'region': region,
-                     'prediction': prediction[0]
-                  }
-              ))
-              f.write("\n")
-
-    return prediction[0]
-
-
-# Setting up UI components for input and output
+        with log_file.open("a") as f:
+            f.write(json.dumps({
+                "age": age,
+                "bmi": bmi,
+                "children": children,
+                "sex": sex,
+                "smoker": smoker,
+                "region": region,
+                "prediction": prediction[0]
+            }))
+            f.write("\n")
+
+    return f"${prediction[0]:,.2f}"
+
+
+# UI
+
 demo = gr.Interface(
     fn=predict_charges,
-    inputs=[age_input, bmi_input,
-            children_input, sex_input, smoker_input, region_input],
+    inputs=[
+        age_input,
+        bmi_input,
+        children_input,
+        sex_input,
+        smoker_input,
+        region_input
+    ],
     outputs=model_output,
     title="HealthyLife Insurance Charge Prediction",
-    description="This API allows you to predict the appropiate charges for each patient",
+    description="Predict the insurance medical charges based on patient information",
     flagging_mode="manual",
     concurrency_limit=8
 )
 
-# Launch with a load balancer
+
 demo.queue()
-demo.launch(share=False)
+demo.launch()