walgar/ckd

1from fastapi import FastAPI, HTTPException2from pydantic import BaseModel3import joblib4import os5import pandas as pd6 7# ✅ Load the model with correct path8MODEL_PATH = os.path.join(os.path.dirname(__file__), "random_forest_model_compatible.pkl")9 10try:11    model = joblib.load(MODEL_PATH)12    print("✅ Model loaded successfully.")13except Exception as e:14    print(f"❌ Failed to load model: {e}")15    raise16 17# ✅ Define FastAPI app18app = FastAPI(title="CKD Prediction API")19 20# ✅ Input schema (must match the model's features)21class CKDInput(BaseModel):22    age: float23    blood_pressure: float24    specific_gravity: float25    albumin: float26    sugar: float27    red_blood_cells: int28    pus_cell: int29    pus_cell_clumps: int30    bacteria: int31    blood_glucose_random: float32    blood_urea: float33    serum_creatinine: float34    sodium: float35    potassium: float36    haemoglobin: float37    packed_cell_volume: float38    white_blood_cell_count: float39    red_blood_cell_count: float40    hypertension: int41    diabetes_mellitus: int42    coronary_artery_disease: int43    appetite: int44    peda_edema: int45    aanemia: int46 47# ✅ Prediction endpoint48@app.post("/predict")49async def predict_ckd(data: CKDInput):50    try:51        # Convert to DataFrame52        input_df = pd.DataFrame([data.dict()])53 54        # Predict55        prediction = model.predict(input_df)[0]56 57        # Interpret result58        result = "ckd" if prediction == 1 else "nockd"59 60        return {"prediction": result}61 62    except Exception as e:63        raise HTTPException(status_code=500, detail=f"Prediction error: {e}")64 65# Root endpoint (optional)66@app.get("/")67def read_root():68    return {"message": "Welcome to CKD Prediction API!"}69