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  1. app.py +59 -38
app.py CHANGED
@@ -1,38 +1,59 @@
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- from fastapi import FastAPI
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- from fastapi.middleware.cors import CORSMiddleware
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- import joblib
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- import pandas as pd
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-
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- app = FastAPI()
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-
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- app.add_middleware(
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- CORSMiddleware,
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- allow_origins=["*"], # Allows all origins
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- allow_credentials=True,
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- allow_methods=["*"], # Allows all methods
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- allow_headers=["*"], # Allows all headers
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- )
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-
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- # Load models once at startup
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- future_spend_model = joblib.load("future_spend_7d.pkl")
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- spike_model = joblib.load("spike_probability.pkl")
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- acc_model = joblib.load("acceleration.pkl")
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- FEATURES = joblib.load("model_features.pkl")
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-
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- @app.get("/")
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- def root():
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- return {"status": "ML backend running"}
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-
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- @app.post("/predict")
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- def predict(payload: dict):
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- X = pd.DataFrame([payload], columns=FEATURES)
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-
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- future_spend = future_spend_model.predict(X)[0]
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- spike_prob = spike_model.predict_proba(X)[0][1]
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- acceleration = acc_model.predict(X)[0]
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-
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- return {
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- "future_7d_spend": round(float(future_spend), 2),
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- "spike_probability": round(float(spike_prob), 3),
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- "acceleration": round(float(acceleration), 2)
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ from fastapi import FastAPI
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+ from fastapi.responses import HTMLResponse
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+ from fastapi.middleware.cors import CORSMiddleware
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+ import joblib
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+ import pandas as pd
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+
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+ app = FastAPI()
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+
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+ app.add_middleware(
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+ CORSMiddleware,
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+ allow_origins=["*"], # Allows all origins
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+ allow_credentials=True,
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+ allow_methods=["*"], # Allows all methods
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+ allow_headers=["*"], # Allows all headers
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+ )
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+
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+ # Load models once at startup
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+ future_spend_model = joblib.load("future_spend_7d.pkl")
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+ spike_model = joblib.load("spike_probability.pkl")
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+ acc_model = joblib.load("acceleration.pkl")
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+ FEATURES = joblib.load("model_features.pkl")
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+
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+ @app.get("/", response_class=HTMLResponse)
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+ def root():
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+ return """
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+ <!DOCTYPE html>
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+ <html>
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+ <head>
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+ <title>ML Backend Status</title>
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+ <style>
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+ body { font-family: sans-serif; text-align: center; padding-top: 50px; background-color: #f0f2f5; }
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+ h1 { color: #333; }
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+ .status { color: green; font-weight: bold; }
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+ a { text-decoration: none; color: white; background-color: #007bff; padding: 10px 20px; border-radius: 5px; }
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+ a:hover { background-color: #0056b3; }
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+ </style>
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+ </head>
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+ <body>
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+ <h1>ML Backend is <span class="status">RUNNING</span></h1>
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+ <p>The API is active and ready to accept requests.</p>
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+ <br>
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+ <a href="/docs">View API Documentation</a>
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+ </body>
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+ </html>
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+ """
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+
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+ @app.post("/predict")
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+ def predict(payload: dict):
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+ X = pd.DataFrame([payload], columns=FEATURES)
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+
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+ future_spend = future_spend_model.predict(X)[0]
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+ spike_prob = spike_model.predict_proba(X)[0][1]
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+ acceleration = acc_model.predict(X)[0]
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+
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+ return {
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+ "future_7d_spend": round(float(future_spend), 2),
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+ "spike_probability": round(float(spike_prob), 3),
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+ "acceleration": round(float(acceleration), 2)
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+ }