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Dockerfile

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+FROM python:3.10
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install -r requirements.txt
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import joblib
+import numpy as np
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI(title="Web Attack Detection API")
+
+# Allow browser requests
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Expected number of features
+EXPECTED_FEATURES = 35
+
+# Request schema
+class InputData(BaseModel):
+    features: list[float]
+
+# 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": "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)[0]
+
+    # Convert prediction to readable output
+    if prediction == -1:
+        result = "attack detected"
+    else:
+        result = "normal request"
+
+    return {
+        "prediction": result,
+        "raw_prediction": int(prediction)
+    }

requirements.txt

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+fastapi
+uvicorn
+numpy
+pandas
+joblib
+scikit-learn==1.6.1
+python-multipart
+pydantic