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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
import os
# -----------------------------
# Load Trained Model & Scaler
# -----------------------------
MODEL_FILE = "diabetes_model.pkl"
SCALER_FILE = "scaler.pkl"
if not os.path.exists(MODEL_FILE) or not os.path.exists(SCALER_FILE):
raise FileNotFoundError("Model or scaler file not found. Make sure both exist in the same directory.")
scaler = joblib.load(SCALER_FILE)
best_model = joblib.load(MODEL_FILE)
# -----------------------------
# 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
# -----------------------------
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # For production, replace '*' with allowed domains
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):
try:
# 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 features
features_scaled = scaler.transform(features)
# Predict
prediction = best_model.predict(features_scaled)[0]
result = "Diabetes" if prediction == 1 else "No Diabetes"
return {"prediction": result}
except Exception as e:
return {"error": str(e)}
# -----------------------------
# Run the API locally
# -----------------------------
if __name__ == "__main__":
import uvicorn
uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True)
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