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File size: 1,984 Bytes
ffb9283 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | 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 """
<!DOCTYPE html>
<html>
<head>
<title>ML Backend Status</title>
<style>
body { font-family: sans-serif; text-align: center; padding-top: 50px; background-color: #f0f2f5; }
h1 { color: #333; }
.status { color: green; font-weight: bold; }
a { text-decoration: none; color: white; background-color: #007bff; padding: 10px 20px; border-radius: 5px; }
a:hover { background-color: #0056b3; }
</style>
</head>
<body>
<h1>ML Backend is <span class="status">RUNNING</span></h1>
<p>The API is active and ready to accept requests.</p>
<br>
<a href="/docs">View API Documentation</a>
</body>
</html>
"""
@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)
}
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