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from fastapi import FastAPI
from fastapi.responses import JSONResponse
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel
import numpy as np
import joblib
import torch
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from xgboost import XGBClassifier
from pytorch_tabnet.tab_model import TabNetClassifier
from pathlib import Path
import os
import sys
BASE_DIR = Path(__file__).resolve().parents[1]
if str(BASE_DIR) not in sys.path:
sys.path.insert(0, str(BASE_DIR))
# CONFIG
MODEL_DIR = BASE_DIR/"models"
FUSION_PATH = BASE_DIR/"fusion"/"fusion_model_metadata.joblib"
FEATURES = [
"rainfall_mm", "humidity_pct", "temp_c", "vegetation_index",
"elevation_m", "proximity_to_water_km", "wind_speed_mps",
"surface_water_presence", "daylight_hours", "season_label"
]
# LOAD MODELS
print(" Loading models...")
cat_model = CatBoostClassifier()
cat_model.load_model(f"{MODEL_DIR}/catboost.cbm")
xgb_model = joblib.load(f"{MODEL_DIR}/xgboost.joblib")
lgb_model = joblib.load(f"{MODEL_DIR}/lightgbm.joblib")
tabnet_model = TabNetClassifier()
tabnet_model.load_model(f"{MODEL_DIR}/tabnet.zip.zip")
tabnet_scaler = joblib.load(f"{MODEL_DIR}/tabnet_scaler.joblib")
fusion_meta = joblib.load(FUSION_PATH)
print(" All models and fusion metadata loaded successfully.")
# FASTAPI APP
app = FastAPI(title="Malaria Risk Prediction API", version="1.0")
# INPUT SCHEMA
class MalariaInput(BaseModel):
rainfall_mm: float
humidity_pct: float
temp_c: float
vegetation_index: float
elevation_m: float
proximity_to_water_km: float
wind_speed_mps: float
surface_water_presence: float
daylight_hours: float
season_label: int # 0 = Dry, 1 = Rainy
# PREDICTION ENDPOINT
@app.post("/predict/")
def predict(data: MalariaInput):
try:
x = np.array([[getattr(data, f) for f in FEATURES]], dtype=float)
x_scaled = tabnet_scaler.transform(x)
# Individual model probabilities
preds = {
"catboost": float(cat_model.predict_proba(x)[0][1]),
"xgboost": float(xgb_model.predict_proba(x)[0][1]),
"lightgbm": float(lgb_model.predict_proba(x)[0][1]),
"tabnet": float(tabnet_model.predict_proba(torch.tensor(x_scaled).float().numpy())[0][1]),
}
# Fusion weights
models_in_fusion = fusion_meta.get("models", [])
weights_data = fusion_meta.get("weights", [])
weights = {m: float(w) for m, w in zip(models_in_fusion, weights_data)}
# Weighted ensemble score
risk_score = float(sum(preds[m] * weights[m] for m in preds if m in weights))
#risk_percentage = round(risk_score * 100, 2)
# Label decision
risk_label = "High" if risk_score >= 0.60 else "Medium"
response = {
#"input_features": data.dict(),
"model_outputs": preds,
# "fusion_weights": weights,
"risk_score": round(risk_score, 3),
"risk_label": risk_label
}
return JSONResponse(content=jsonable_encoder(response))
except Exception as e:
return JSONResponse(
status_code=500,
content={"error": str(e), "message": "Prediction failed."}
)
# ROOT ENDPOINT
@app.get("/")
def root():
return {"message": " Malaria Risk Prediction API is running!"}
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