First commit

Defined file structure and completed EDA

src/api/app.py

@@ -0,0 +1,96 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import pandas as pd
+import numpy as np
+import joblib
+from pathlib import Path
+from typing import Optional
+
+from config import MODELS_DIR
+from data_preprocessing import prepare_data
+
+app = FastAPI(title="Fraud Detection API",
+             description="API for detecting fraudulent transactions",
+             version="1.0.0")
+
+class Transaction(BaseModel):
+    trans_date_trans_time: str
+    cc_num: str
+    merchant: str
+    category: str
+    amt: float
+    first: str
+    last: str
+    gender: str
+    street: str
+    city: str
+    state: str
+    zip: str
+    lat: float
+    long: float
+    city_pop: int
+    job: str
+    dob: str
+    trans_num: str
+    unix_time: int
+    merch_lat: float
+    merch_long: float
+
+class PredictionResponse(BaseModel):
+    is_fraud: bool
+    fraud_probability: float
+    confidence: str
+
+def load_model():
+    """Load the trained model and preprocessor."""
+    try:
+        model = joblib.load(MODELS_DIR / "fraud_model.joblib")
+        preprocessor = joblib.load(MODELS_DIR / "preprocessor.joblib")
+        return model, preprocessor
+    except FileNotFoundError:
+        raise HTTPException(status_code=500, detail="Model not found. Please train the model first.")
+
+def get_confidence_level(probability: float) -> str:
+    """Convert probability to confidence level."""
+    if probability >= 0.9:
+        return "Very High"
+    elif probability >= 0.7:
+        return "High"
+    elif probability >= 0.5:
+        return "Medium"
+    else:
+        return "Low"
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Fraud Detection API"}
+
+@app.post("/predict", response_model=PredictionResponse)
+async def predict(transaction: Transaction):
+    """Predict whether a transaction is fraudulent."""
+    try:
+        # Load model and preprocessor
+        model, preprocessor = load_model()
+        
+        # Convert transaction to DataFrame
+        transaction_dict = transaction.dict()
+        df = pd.DataFrame([transaction_dict])
+        
+        # Prepare data for prediction
+        X, _, _ = prepare_data(df, preprocessor=preprocessor)
+        
+        # Make prediction
+        probability = model.predict_proba(X)[0, 1]
+        is_fraud = probability >= 0.5
+        
+        return PredictionResponse(
+            is_fraud=bool(is_fraud),
+            fraud_probability=float(probability),
+            confidence=get_confidence_level(probability)
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)