Initial commit

README.md

@@ -1,8 +1,8 @@
 ---
-title: Ericeilander Insurance Charge Prediction
-emoji: ⚡
-colorFrom: yellow
-colorTo: red
+title: InsuranceCostPrediction
+emoji: 📊
+colorFrom: purple
+colorTo: gray
 sdk: gradio
 sdk_version: 4.38.1
 app_file: app.py

app.py

@@ -0,0 +1,101 @@
+# Import the 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
+
+
+# Run the training script placed in the same directory as app.py
+# The training script will train and persist a linear regression
+# model with the filename 'model.joblib'
+#hugging face HATES !python but might be okay with python
+# python /train.py
+exec(open("train.py").read())
+
+
+# Load the freshly trained model from disk
+insurance_model = 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(age, bmi, children, sex, smoker, region):
+
+    input = {
+        "age": age,
+        "bmi": bmi,
+        "children": children,
+        "sex": sex,
+        "smoker": smoker,
+        "region": region
+    }
+
+    input_df = pd.DataFrame([input])
+    prediction = insurance_model.predict(input_df).to_list()
+
+    # 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
+
+    # this was already in here so i'm leaving it for loggin
+    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]
+
+
+
+# Set up UI components for input and output
+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")
+region_input = gr.Number(['southwest', 'southeast', 'northwest', 'northeast'], label="region")
+output = gr.Label(label="Insurance Price")
+
+# Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
+# demo was the existing name in this file, so using that.
+demo = gr.Interface(
+    fn=predict,
+    inputs=[age_input, bmi_input, children_input, sex_input, smoker_input, region_input],
+    outputs=output,
+    title="HealthyLife Insurance Charge Prediction",
+    description="This API allows you to predict insurance prices for HealthlyLife Insurance",
+    allow_flagging="auto",
+    concurrency_limit=8
+)
+
+# Launch with a load balancer
+# these two lines were in the file already
+demo.queue()
+demo.launch(share=False)

insurance.csv

@@ -0,0 +1,1339 @@
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model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f326b6522adb874ee2f939fd0bfa208dee5023aeab46ece9fea508d2aeb9b9b8
+size 276100

requirements.txt

@@ -0,0 +1,6 @@
+scikit-learn==1.2.2
+pandas==2.0.3
+numpy==1.26.4
+joblib==1.4.2
+gradio==4.38.1
+huggingface_hub==0.23.4

train.py

@@ -0,0 +1,71 @@
+
+import joblib
+import pandas as pd
+
+from sklearn.datasets import fetch_openml
+
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import make_column_transformer
+from sklearn.pipeline import make_pipeline, Pipeline
+from sklearn.model_selection import train_test_split, RandomizedSearchCV, cross_val_score
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.ensemble import RandomForestRegressor
+
+print("Loading the csv")
+df = pd.read_csv("insurance.csv")
+
+numerical_columns = ['age', 'bmi', 'children']
+categorical_columns = ['sex', 'smoker', 'region']
+target = 'charges'
+
+
+print("Splitting the data")
+x = df[numerical_columns + categorical_columns]
+y = df[target]
+
+x_train, x_test, y_train, y_test = train_test_split(
+    x, y,
+    test_size=0.2,
+    random_state=1 #IMPORTANT use the same random state every time
+)
+
+print("Building the pipeline")
+numerical_pipeline = Pipeline([
+    ('scaler', StandardScaler())
+])
+categorical_pipeline = Pipeline([
+    ('onehot', OneHotEncoder(handle_unknown='ignore'))
+])
+
+preprocessor = make_column_transformer(
+    (numerical_pipeline, numerical_columns),
+    (categorical_pipeline, categorical_columns)
+)
+
+rfg = RandomForestRegressor();
+
+params = {
+    'criterion': ['squared_error', 'absolute_error'],
+    'n_estimators': [1, 2, 3, 5, 8, 13, 21, 34],
+    'max_depth': [1, 2, 3, 5, 8, 13, 21],
+    'max_features': ['sqrt', 'log2']
+}
+
+randomSearchCV = RandomizedSearchCV(
+    rfg,
+    params,
+    cv=2,
+    refit=True # important for pipelines to refit w/ hyper parameters as needed
+)
+
+pipeline = make_pipeline(preprocessor, randomSearchCV)
+
+print("Fitting the pipeline and doing metrics")
+pipeline.fit(x_train, y_train)
+predictions = pipeline.predict(x_test)
+print(f"RMSE: {mean_squared_error(y_test, predictions, squared=False)}")
+print(f"R-squared: {r2_score(y_test, predictions)}")
+
+print("Saving the model")
+joblib.dump(pipeline, "model.joblib")