Update app.py

app.py

@@ -1,28 +1,61 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 import joblib
 import numpy as np
+from fastapi.middleware.cors import CORSMiddleware
 
-app = FastAPI()
+app = FastAPI(title="Web Attack Detection API")
 
-# Request body structure
+# Allow browser requests
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Expected number of features
+EXPECTED_FEATURES = 7
+
+# Request schema
 class InputData(BaseModel):
     features: list[float]
 
-# Load trained model
-model = joblib.load("web_attack_detection_model.pkl")
+# Load model
+try:
+    model = joblib.load("web_attack_detection_model.pkl")
+except Exception as e:
+    raise RuntimeError(f"Model failed to load: {e}")
 
 @app.get("/")
 def home():
-    return {"message": "ML Model API is running"}
+    return {"message": "Web attack detection model running"}
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
 
 @app.post("/predict")
 def predict(data: InputData):
 
+    if len(data.features) != EXPECTED_FEATURES:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Expected {EXPECTED_FEATURES} features"
+        )
+
     input_data = np.array(data.features).reshape(1, -1)
 
-    prediction = model.predict(input_data)
+    prediction = model.predict(input_data)[0]
+
+    # Convert prediction to readable output
+    if prediction == -1:
+        result = "attack detected"
+    else:
+        result = "normal request"
 
     return {
-        "prediction": prediction.tolist()
+        "prediction": result,
+        "raw_prediction": int(prediction)
     }