Update app.py

app.py

@@ -1,97 +1,102 @@
 # Import the libraries
 
-import os 
+import os
 import uuid
 import joblib
 import json
 
 import gradio as gr
-import pandas as pd 
+import pandas as pd
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
 
 # Run the training script placed in the same directory as app.py
-# The training script will train and persist a linear regression
+# The training script will train and persist a logistic regression
 # model with the filename 'model.joblib'
 
 os.system("python train.py")
 
-
 # Load the freshly trained model from disk
 
-insurance_charge_predictor = joblib.load("model.joblib")
+insurance_charge_predictor = joblib.load('model.joblib')
 
 # Prepare the logging functionality
-# log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
-# log_folder = log_file.parent
-
-# scheduler = CommitScheduler(
-#     repo_id="insurance-charge-mlops-logs",  # provide a name "insurance-charge-mlops-logs" for the repo_id
-#     repo_type="dataset",
-#     folder_path=log_folder,
-#     path_in_repo="data",
-#     every=2
-# )
-
-# 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_charge(age, sex, bmi, children, somker, region):
-    smaple = {'age': age,
-              'bmi': bmi,
-              'children': children,
-              'sex': sex,
-              'smoker': smoker,
-              'region': region,
-              'prediction': prediction[0]
-             }
-    data_point = pd.DataFrame([sample])
-    prediction = insurance_charge_predicter.predict(data_point).tolist()
 
-    # 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
-    # access
-
-    # 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")
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
 
-    return prediction[0]
+scheduler = CommitScheduler(
+    repo_id="insurance-charge-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
 
+# Define the predict function that runs when 'Submit' is clicked or when a API request is made
+def predict_insurance_charge(age, bmi, children,sex, smoker, region):
+    sample = {
+        'age': age,
+        'bmi': bmi,
+        'children': children,
+        'sex': sex,
+        'smoker': smoker,
+        'region': region
+    }
+    
+    data_point = pd.DataFrame([sample])
+    prediction = insurance_charge_predictor.predict(data_point).tolist()
 
+    # While the prediction is made, log both the inputs and outputs to a local log file
+    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
+    # access
+    
+    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 round(prediction[0],2)
 
 # Set up UI components for input and output
 
-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", "northwest", "northeast"], label = "region")
-model_output = gr.Label( label = " Insurance Chaeges")
+age_input = gr.Number(label='age')
+bmi_input = gr.Number(label='bmi')
+children_input = gr.Number(label='children')
+sex_input = gr.Dropdown(['female','male'],label='sex')
+smoker_input = gr.Dropdown(['yes','no'],label='smoker')
+region_input = gr.Dropdown(
+    ['southeast', 'southwest', 'northwest', 'northeast'],
+    label='region'
+)
 
-# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
+model_output = gr.Label(label="Insurance Charges")
+
+# Create the interface
 demo = gr.Interface(
     fn=predict_insurance_charge,
-    inputs=[age_input, bmi_input, children_input, sex_input, smoker_input, region_input],
-    outputs = model_output,
-    title = "Healthy Insurence Candidate Perdiction",
-    description = "This API will predict and estimate isurance charges based on candidate's attributes",
-    examples = [[33,33.44,5,"male","no", "southeast"],
-                [40,38.20,2,"female","no", "northwest"],
-                [52,36.20,0,"male","no", "northwest"]],
-    concurrency_limit = 16
-
+    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 estimating insurance charges based on customer attributes",
+    examples=[[33,33.44,5,'male','no','southeast'],
+              [58,25.175,0,'male','no','northeast'],
+              [52,38.380,2,'female','no','northeast']],
+    concurrency_limit=16
 )
 
 # Launch with a load balancer
 demo.queue()
 demo.launch(share=False)
+