Update app.py

app.py

@@ -1,29 +1,38 @@
-from fastapi import FastAPI
-import joblib
-import pandas as pd
-
-app = FastAPI()
-
-# 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("/")
-def root():
-    return {"status": "ML backend running"}
-
-@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)
-    }
+from fastapi import FastAPI
+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("/")
+def root():
+    return {"status": "ML backend running"}
+
+@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)
+    }