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Dockerfile

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+# Use Python 3.13.5 slim image
+# The slim image keeps our container small
+FROM python:3.13.5-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies (if needed)
+RUN apt-get update && apt-get install -y \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements and install Python dependencies
+# `--no-cache-dir` prevents pip from storing cached packages, further reducing size
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY app/ ./app/
+COPY models/ ./models/
+
+# Expose port
+EXPOSE 8000
+
+# Run the application
+CMD [ "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000" ]

app/main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import pickle
+import numpy as np
+import os
+
+# ---DEFINE INPUT DATA SCHEMA---
+# FastAPI uses Pydantic for request validation. 
+# Meaning it automatically validates incoming data and provides clear error messages if something’s wrong.
+class PatientData(BaseModel): 
+    age: float
+    sex: float
+    bmi: float
+    bp: float  # blood pressure
+    s1: float  # serum measurement 1
+    s2: float  # serum measurement 2
+    s3: float  # serum measurement 3
+    s4: float  # serum measurement 4
+    s5: float  # serum measurement 5
+    s6: float  # serum measurement 6 
+
+# The example values help API users understand the expected input format. 
+# Note that the diabetes dataset features are already normalized.
+    class Config: 
+        schema_extra = {
+            "example": {
+                "age": 0.05,
+                "sex": 0.05,
+                "bmi": 0.06,
+                "bp": 0.02,
+                "s1": -0.04,
+                "s2": -0.04,
+                "s3": -0.02,
+                "s4": -0.01,
+                "s5": 0.01,
+                "s6": 0.02                
+            }
+        }
+
+# ---INITIALIZE FASTAPI APP---
+app = FastAPI(
+    title="Diabetes Progression Predictor",
+    description="Predicts diabetes progression score from physiological features",
+    version="1.0.0"
+)
+
+# ---LOAD THE TRAINED MODEL---
+model_path = os.path.join("models", "diabetes_model.pkl")
+with open(model_path, "rb") as f: 
+    model = pickle.load(f)
+
+# ---CREATE ENDPOINT---
+@app.post("/predict")
+def predict_progression(patient: PatientData): 
+    """
+    Predict diabetes progression score. 
+    """
+    # Convert input to numpy array
+    features = np.array([[
+        patient.age, 
+        patient.sex, 
+        patient.bmi, 
+        patient.bp,
+        patient.s1,
+        patient.s2,
+        patient.s3,
+        patient.s4,
+        patient.s5,
+        patient.s6,
+    ]])
+
+    # Make prediction
+    prediction = model.predict(features)[0]
+
+    # Return result with additional context
+    return {
+        "predicted_progression_score": round(prediction, 2),
+        "interpretation": get_interpretation(prediction)
+    }
+
+def get_interpretation(score): 
+    """
+    Provide human-readdable interpretation of the score.
+    """
+    if score < 100: 
+        return "Below average progression"
+    elif score < 150: 
+        return "Average progression"
+    else: 
+        return "Above average progression"
+    
+@app.get("/")
+def health_check(): 
+    return {
+        "status": "healthy",
+        "model": "diabetes_progression_v1"
+    }

models/diabetes_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3866b799fa4ca1901363061898e8902f6d21262aff5f221e9ff11701cc705933
+size 2312740

requirements.txt

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+annotated-types==0.7.0
+anyio==4.9.0
+click==8.2.1
+fastapi==0.115.12
+h11==0.16.0
+idna==3.10
+joblib==1.5.1
+numpy==2.3.0
+pandas==2.3.0
+pydantic==2.11.7
+pydantic_core==2.33.2
+python-dateutil==2.9.0.post0
+pytz==2025.2
+scikit-learn==1.7.0
+scipy==1.15.3
+six==1.17.0
+sniffio==1.3.1
+starlette==0.46.2
+threadpoolctl==3.6.0
+typing-inspection==0.4.1
+typing_extensions==4.14.0
+tzdata==2025.2
+uvicorn==0.34.3