Upload app code and model

Dockerfile

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+
+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["python", "app.py"]

app.py

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+
+import gradio as gr
+import numpy as np
+import joblib
+
+# Load your trained model
+model = joblib.load("model_xgboost.pkl")
+
+def predict(age, sex, bmi, children, smoker, region):
+    sex = 1 if sex == "male" else 0
+    smoker_bin = 1 if smoker == "yes" else 0
+
+    region_northwest = 1 if region == "northwest" else 0
+    region_southeast = 1 if region == "southeast" else 0
+    region_southwest = 1 if region == "southwest" else 0
+
+    bmi_smoker = bmi * smoker_bin
+    obese = 1 if bmi >= 30 else 0
+
+    input_vector = np.array([[age, sex, bmi, children, smoker_bin,
+                              region_northwest, region_southeast, region_southwest,
+                              bmi_smoker, obese]])
+    
+    prediction = model.predict(input_vector)[0]
+    return f"${prediction:,.2f}"
+
+inputs = [
+    gr.Slider(18, 65, value=35, label="Age"),
+    gr.Radio(["male", "female"], label="Sex"),
+    gr.Slider(15.0, 55.0, step=0.1, value=30.0, label="BMI"),
+    gr.Slider(0, 5, step=1, value=1, label="Number of Children"),
+    gr.Radio(["yes", "no"], label="Smoker"),
+    gr.Dropdown(["northwest", "southeast", "southwest", "northeast"], label="Region"),
+]
+
+output = gr.Textbox(label="Predicted Insurance Charges")
+
+gr.Interface(fn=predict, inputs=inputs, outputs=output, title="Medical Insurance Cost Predictor").launch()

model_xgboost.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:02aee364eaec3c9ffbf797aed71d75166c5479ac5446276cbda18f7177ba2942
+size 119262

requirements.txt

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+gradio
+xgboost
+numpy
+joblib