Upload Dockerfile, requirements, and streamlit_app.py

Dockerfile

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+
+# Use the official Streamlit image
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy app files
+COPY . .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose Streamlit default port
+EXPOSE 7860
+
+# Run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.enableCORS=false"]
+

requirements.txt

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+streamlit
+requests
+

src/app.py

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+
+import streamlit as st
+import requests
+
+st.set_page_config(page_title="Medical Insurance Cost Predictor", layout="centered")
+
+st.title("💊 Medical Insurance Cost Predictor")
+
+st.markdown("Enter the details below to estimate your medical insurance cost:")
+
+# Input fields
+age = st.number_input("Age", min_value=18, max_value=100, value=30)
+sex = st.selectbox("Sex", ["male", "female"])
+bmi = st.number_input("BMI", min_value=10.0, max_value=50.0, value=25.0)
+children = st.number_input("Number of Children", min_value=0, max_value=10, value=1)
+smoker = st.selectbox("Smoker", ["yes", "no"])
+region = st.selectbox("Region", ["northeast", "northwest", "southeast", "southwest"])
+
+# Derived features
+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 = 1 if smoker == "yes" and bmi > 30 else 0
+obese = 1 if bmi > 30 else 0
+
+if st.button("Predict Insurance Cost"):
+    input_data = {
+        "age": age,
+        "sex": sex,
+        "bmi": bmi,
+        "children": children,
+        "smoker": smoker,
+        "region_northwest": region_northwest,
+        "region_southeast": region_southeast,
+        "region_southwest": region_southwest,
+        "bmi_smoker": bmi_smoker,
+        "obese": obese
+    }
+
+    try:
+        # Replace this URL with your actual backend Space URL
+        backend_url = "https://<your-backend-username>-insurance-cost-predictor-xgb.hf.space/predict"
+        response = requests.post(backend_url, json=input_data)
+
+        if response.status_code == 200:
+            prediction = response.json()["predicted_insurance_cost"]
+            st.success(f"💰 Estimated Insurance Cost: ${prediction}")
+        else:
+            st.error(f"❌ Error from backend: {response.text}")
+
+    except Exception as e:
+        st.error(f"Exception occurred: {e}")
+