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Dockerfile

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+FROM python:3.10
+
+WORKDIR /code
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

diabetes_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f697fe210ee5bc5907e10f03ec13a084a6e69187ac15d82e6a455e0343d68b6
+size 1127

main.py

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+# ====================================================
+# main.py - Diabetes Prediction API (Production)
+# ====================================================
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import numpy as np
+import joblib
+
+# -----------------------------
+# Load Trained Model & Scaler
+# -----------------------------
+# Ensure these files are in the same directory as this script
+scaler = joblib.load("scaler.pkl")
+best_model = joblib.load("diabetes_model.pkl")
+
+# -----------------------------
+# Initialize FastAPI app
+# -----------------------------
+app = FastAPI(
+    title="Diabetes Prediction API",
+    description="Predicts diabetes based on patient data using trained ML model",
+    version="1.0"
+)
+
+# -----------------------------
+# Enable CORS for all origins (for testing)
+# -----------------------------
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Adjust for production to restrict origins
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# -----------------------------
+# Define Input Data Model
+# -----------------------------
+class InputData(BaseModel):
+    Age: float
+    Sex: float
+    BMI: float
+    Glucose: float
+    BloodPressure: float
+    Insulin: float
+    Increased_Thirst: float
+    Increased_Hunger: float
+    Fatigue_Tiredness: float
+    Blurred_Vision: float
+    Unexplained_Weight_Loss: float
+
+# -----------------------------
+# Prediction Endpoint
+# -----------------------------
+@app.post("/predict")
+def predict(data: InputData):
+    # Convert input to numpy array
+    features = np.array([[
+        data.Age,
+        data.Sex,
+        data.BMI,
+        data.Glucose,
+        data.BloodPressure,
+        data.Insulin,
+        data.Increased_Thirst,
+        data.Increased_Hunger,
+        data.Fatigue_Tiredness,
+        data.Blurred_Vision,
+        data.Unexplained_Weight_Loss
+    ]])
+
+    # Scale the input features
+    features_scaled = scaler.transform(features)
+
+    # Make prediction
+    prediction = best_model.predict(features_scaled)[0]
+
+    # Map prediction to human-readable label
+    result = "Diabetes" if prediction == 1 else "No Diabetes"
+
+    return {"prediction": result}
+
+# -----------------------------
+# Run the API locally (development only)
+# -----------------------------
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True)

requirements.txt

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+# Install Python packages
+pip install fastapi uvicorn scikit-learn numpy joblib pandas
+
+# Or create requirements.txt
+echo "fastapi==0.104.0
+uvicorn[standard]==0.24.0
+scikit-learn==1.3.0
+numpy==1.24.0
+joblib==1.3.0
+pandas==2.0.3" > requirements.txt
+
+pip install -r requirements.txt

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6d4b82ba818bb9a0f1d2e939c36ca625f46e74bd112b9486a84501a6c956afb
+size 1359