from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
import joblib
import pandas as pd
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # Allows all origins
allow_credentials=True,
allow_methods=["*"], # Allows all methods
allow_headers=["*"], # Allows all headers
)
# Load models once at startup
future_spend_model = joblib.load("future_spend_7d.pkl")
spike_model = joblib.load("spike_probability.pkl")
acc_model = joblib.load("acceleration.pkl")
FEATURES = joblib.load("model_features.pkl")
@app.get("/", response_class=HTMLResponse)
def root():
return """
ML Backend Status
ML Backend is RUNNING
The API is active and ready to accept requests.
View API Documentation
"""
@app.post("/predict")
def predict(payload: dict):
X = pd.DataFrame([payload], columns=FEATURES)
future_spend = future_spend_model.predict(X)[0]
spike_prob = spike_model.predict_proba(X)[0][1]
acceleration = acc_model.predict(X)[0]
return {
"future_7d_spend": round(float(future_spend), 2),
"spike_probability": round(float(spike_prob), 3),
"acceleration": round(float(acceleration), 2)
}