Update app.py

app.py

@@ -1,49 +1,29 @@
 # Importing libraries
-import os
-import uuid
 import joblib
-import json
 import gradio as gr
 import pandas as pd
 
-from huggingface_hub import CommitScheduler
-from pathlib import Path
-
-
-log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
-log_folder = log_file.parent
-
-scheduler = CommitScheduler(
-     repo_id="Insurance_Charge_Prediction_Project",
-     repo_type="dataset",
-     folder_path=log_folder,
-     path_in_repo="data",
-     every=2
-)
-
 charges_predictor = joblib.load("model.joblib")
 
 
-# Inputs
-
 age_input = gr.Number(label="Age", value=25)
 bmi_input = gr.Number(label="BMI", value=25)
 children_input = gr.Number(label="Children", value=0)
 
 sex_input = gr.Dropdown(
-    ["male", "female"],
+    ["male","female"],
     value="male",
     label="Sex"
 )
 
 smoker_input = gr.Dropdown(
-    ["yes", "no"],
+    ["yes","no"],
     value="no",
     label="Smoker"
 )
 
 region_input = gr.Dropdown(
-    ["southeast", "southwest", "northeast", "northwest"],
+    ["southeast","southwest","northeast","northwest"],
     value="southeast",
     label="Region"
 )
@@ -52,7 +32,7 @@ region_input = gr.Dropdown(
 model_output = gr.Textbox(label="Predicted Insurance Cost")
 
 
-def predict_charges(age, bmi, children, sex, smoker, region):
+def predict_charges(age,bmi,children,sex,smoker,region):
 
     sample = {
         "age": age,
@@ -65,20 +45,7 @@ def predict_charges(age, bmi, children, sex, smoker, region):
 
     data_point = pd.DataFrame([sample])
 
-    prediction = charges_predictor.predict(data_point).tolist()
-
-    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")
+    prediction = charges_predictor.predict(data_point)
 
     return f"${prediction[0]:,.2f}"
 
@@ -94,12 +61,8 @@ demo = gr.Interface(
         region_input
     ],
     outputs=model_output,
-    title="HealthyLife Insurance Charge Prediction",
-    description="Predict the insurance medical charges based on patient information",
-    flagging_mode="manual",
-    concurrency_limit=8
+    title="Insurance Charge Prediction",
+    description="Predict insurance medical charges based on user information"
 )
 
-
-demo.queue()
 demo.launch()