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from fastapi import FastAPI, HTTPException |
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from pydantic import BaseModel |
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import pandas as pd |
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from typing import Optional |
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import joblib |
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import os |
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app = FastAPI() |
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TFIDF_VECTORIZER_PATH = "models/tfidf_vectorizer.pkl" |
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MODELS_PATH = "models/xgb_models.pkl" |
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LABEL_ENCODERS_PATH = "models/label_encoders.pkl" |
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tfidf_vectorizer = joblib.load(TFIDF_VECTORIZER_PATH) |
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models = joblib.load(MODELS_PATH) |
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label_encoders = joblib.load(LABEL_ENCODERS_PATH) |
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class TransactionData(BaseModel): |
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Transaction_Id: str |
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Hit_Seq: int |
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Hit_Id_List: str |
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Origin: str |
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Designation: str |
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Keywords: str |
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Name: str |
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SWIFT_Tag: str |
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Currency: str |
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Entity: str |
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Message: str |
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City: str |
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Country: str |
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State: str |
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Hit_Type: str |
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Record_Matching_String: str |
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WatchList_Match_String: str |
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Payment_Sender_Name: Optional[str] = "" |
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Payment_Reciever_Name: Optional[str] = "" |
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Swift_Message_Type: str |
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Text_Sanction_Data: str |
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Matched_Sanctioned_Entity: str |
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Is_Match: int |
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Red_Flag_Reason: str |
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Risk_Level: str |
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Risk_Score: float |
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Risk_Score_Description: str |
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CDD_Level: str |
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PEP_Status: str |
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Value_Date: str |
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Last_Review_Date: str |
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Next_Review_Date: str |
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Sanction_Description: str |
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Checker_Notes: str |
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Sanction_Context: str |
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Maker_Action: str |
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Customer_ID: int |
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Customer_Type: str |
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Industry: str |
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Transaction_Date_Time: str |
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Transaction_Type: str |
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Transaction_Channel: str |
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Originating_Bank: str |
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Beneficiary_Bank: str |
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Geographic_Origin: str |
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Geographic_Destination: str |
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Match_Score: float |
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Match_Type: str |
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Sanctions_List_Version: str |
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Screening_Date_Time: str |
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Risk_Category: str |
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Risk_Drivers: str |
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Alert_Status: str |
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Investigation_Outcome: str |
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Case_Owner_Analyst: str |
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Escalation_Level: str |
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Escalation_Date: str |
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Regulatory_Reporting_Flags: bool |
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Audit_Trail_Timestamp: str |
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Source_Of_Funds: str |
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Purpose_Of_Transaction: str |
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Beneficial_Owner: str |
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Sanctions_Exposure_History: bool |
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class PredictionRequest(BaseModel): |
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transaction_data: TransactionData |
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@app.get("/") |
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async def root(): |
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return {"status": "healthy", "message": "XGBoost TF-IDF API is running"} |
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@app.post("/predict") |
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async def predict(request: PredictionRequest): |
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try: |
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input_data = pd.DataFrame([request.transaction_data.dict()]) |
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text_input = f""" |
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Transaction ID: {input_data['Transaction_Id'].iloc[0]} |
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Origin: {input_data['Origin'].iloc[0]} |
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Designation: {input_data['Designation'].iloc[0]} |
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Keywords: {input_data['Keywords'].iloc[0]} |
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Name: {input_data['Name'].iloc[0]} |
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SWIFT Tag: {input_data['SWIFT_Tag'].iloc[0]} |
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Currency: {input_data['Currency'].iloc[0]} |
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Entity: {input_data['Entity'].iloc[0]} |
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Message: {input_data['Message'].iloc[0]} |
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City: {input_data['City'].iloc[0]} |
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Country: {input_data['Country'].iloc[0]} |
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State: {input_data['State'].iloc[0]} |
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Hit Type: {input_data['Hit_Type'].iloc[0]} |
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Record Matching String: {input_data['Record_Matching_String'].iloc[0]} |
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WatchList Match String: {input_data['WatchList_Match_String'].iloc[0]} |
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Payment Sender: {input_data['Payment_Sender_Name'].iloc[0]} |
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Payment Receiver: {input_data['Payment_Reciever_Name'].iloc[0]} |
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Swift Message Type: {input_data['Swift_Message_Type'].iloc[0]} |
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Text Sanction Data: {input_data['Text_Sanction_Data'].iloc[0]} |
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Matched Sanctioned Entity: {input_data['Matched_Sanctioned_Entity'].iloc[0]} |
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Red Flag Reason: {input_data['Red_Flag_Reason'].iloc[0]} |
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Risk Level: {input_data['Risk_Level'].iloc[0]} |
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Risk Score: {input_data['Risk_Score'].iloc[0]} |
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CDD Level: {input_data['CDD_Level'].iloc[0]} |
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PEP Status: {input_data['PEP_Status'].iloc[0]} |
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Sanction Description: {input_data['Sanction_Description'].iloc[0]} |
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Checker Notes: {input_data['Checker_Notes'].iloc[0]} |
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Sanction Context: {input_data['Sanction_Context'].iloc[0]} |
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Maker Action: {input_data['Maker_Action'].iloc[0]} |
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Customer Type: {input_data['Customer_Type'].iloc[0]} |
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Industry: {input_data['Industry'].iloc[0]} |
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Transaction Type: {input_data['Transaction_Type'].iloc[0]} |
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Transaction Channel: {input_data['Transaction_Channel'].iloc[0]} |
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Geographic Origin: {input_data['Geographic_Origin'].iloc[0]} |
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Geographic Destination: {input_data['Geographic_Destination'].iloc[0]} |
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Risk Category: {input_data['Risk_Category'].iloc[0]} |
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Risk Drivers: {input_data['Risk_Drivers'].iloc[0]} |
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Alert Status: {input_data['Alert_Status'].iloc[0]} |
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Investigation Outcome: {input_data['Investigation_Outcome'].iloc[0]} |
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Source of Funds: {input_data['Source_Of_Funds'].iloc[0]} |
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Purpose of Transaction: {input_data['Purpose_Of_Transaction'].iloc[0]} |
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Beneficial Owner: {input_data['Beneficial_Owner'].iloc[0]} |
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""" |
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X_tfidf = tfidf_vectorizer.transform([text_input]) |
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response = {} |
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for label, model in models.items(): |
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proba = model.predict_proba(X_tfidf)[0] |
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pred = proba.argmax() |
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decoded_label = label_encoders[label].inverse_transform([pred])[0] |
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class_probs = { |
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label_encoders[label].classes_[i]: float(prob) |
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for i, prob in enumerate(proba) |
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} |
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response[label] = { |
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"prediction": decoded_label, |
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"probabilities": class_probs |
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} |
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return response |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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if __name__ == "__main__": |
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import uvicorn |
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port = int(os.environ.get("PORT", 7860)) |
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uvicorn.run(app, host="0.0.0.0", port=port) |
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