Create app.py

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+import pandas as pd
+import joblib
+import os
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.multioutput import MultiOutputClassifier
+from sklearn.pipeline import Pipeline
+
+# ========== Config ==========
+DATA_PATH = "data/synthetic_transactions_samples_5000.csv"
+MODEL_DIR = "models"
+MODEL_PATH = os.path.join(MODEL_DIR, "logreg_model.pkl")
+VECTORIZER_PATH = os.path.join(MODEL_DIR, "tfidf_vectorizer.pkl")
+
+# ========== FastAPI Init ==========
+app = FastAPI()
+
+# ========== Input Schema ==========
+class TransactionData(BaseModel):
+    Transaction_Id: str
+    Hit_Seq: int
+    Hit_Id_List: str
+    Origin: str
+    Designation: str
+    Keywords: str
+    Name: str
+    SWIFT_Tag: str
+    Currency: str
+    Entity: str
+    Message: str
+    City: str
+    Country: str
+    State: str
+    Hit_Type: str
+    Record_Matching_String: str
+    WatchList_Match_String: str
+    Payment_Sender_Name: Optional[str] = ""
+    Payment_Reciever_Name: Optional[str] = ""
+    Swift_Message_Type: str
+    Text_Sanction_Data: str
+    Matched_Sanctioned_Entity: str
+    Is_Match: int
+    Red_Flag_Reason: str
+    Risk_Level: str
+    Risk_Score: float
+    Risk_Score_Description: str
+    CDD_Level: str
+    PEP_Status: str
+    Value_Date: str
+    Last_Review_Date: str
+    Next_Review_Date: str
+    Sanction_Description: str
+    Checker_Notes: str
+    Sanction_Context: str
+    Maker_Action: str
+    Customer_ID: int
+    Customer_Type: str
+    Industry: str
+    Transaction_Date_Time: str
+    Transaction_Type: str
+    Transaction_Channel: str
+    Originating_Bank: str
+    Beneficiary_Bank: str
+    Geographic_Origin: str
+    Geographic_Destination: str
+    Match_Score: float
+    Match_Type: str
+    Sanctions_List_Version: str
+    Screening_Date_Time: str
+    Risk_Category: str
+    Risk_Drivers: str
+    Alert_Status: str
+    Investigation_Outcome: str
+    Case_Owner_Analyst: str
+    Escalation_Level: str
+    Escalation_Date: str
+    Regulatory_Reporting_Flags: bool
+    Audit_Trail_Timestamp: str
+    Source_Of_Funds: str
+    Purpose_Of_Transaction: str
+    Beneficial_Owner: str
+    Sanctions_Exposure_History: bool
+
+# ========== Utils ==========
+def create_text_input(row):
+    return f"""
+        Transaction ID: {row['Transaction_Id']}
+        Origin: {row['Origin']}
+        Designation: {row['Designation']}
+        Keywords: {row['Keywords']}
+        Name: {row['Name']}
+        SWIFT Tag: {row['SWIFT_Tag']}
+        Currency: {row['Currency']}
+        Entity: {row['Entity']}
+        Message: {row['Message']}
+        City: {row['City']}
+        Country: {row['Country']}
+        State: {row['State']}
+        Hit Type: {row['Hit_Type']}
+        Record Matching String: {row['Record_Matching_String']}
+        WatchList Match String: {row['WatchList_Match_String']}
+        Payment Sender: {row['Payment_Sender_Name']}
+        Payment Receiver: {row['Payment_Reciever_Name']}
+        Swift Message Type: {row['Swift_Message_Type']}
+        Text Sanction Data: {row['Text_Sanction_Data']}
+        Matched Sanctioned Entity: {row['Matched_Sanctioned_Entity']}
+        Red Flag Reason: {row['Red_Flag_Reason']}
+        Risk Level: {row['Risk_Level']}
+        Risk Score: {row['Risk_Score']}
+        CDD Level: {row['CDD_Level']}
+        PEP Status: {row['PEP_Status']}
+        Sanction Description: {row['Sanction_Description']}
+        Checker Notes: {row['Checker_Notes']}
+        Sanction Context: {row['Sanction_Context']}
+        Maker Action: {row['Maker_Action']}
+        Customer Type: {row['Customer_Type']}
+        Industry: {row['Industry']}
+        Transaction Type: {row['Transaction_Type']}
+        Transaction Channel: {row['Transaction_Channel']}
+        Geographic Origin: {row['Geographic_Origin']}
+        Geographic Destination: {row['Geographic_Destination']}
+        Risk Category: {row['Risk_Category']}
+        Risk Drivers: {row['Risk_Drivers']}
+        Alert Status: {row['Alert_Status']}
+        Investigation Outcome: {row['Investigation_Outcome']}
+        Source of Funds: {row['Source_Of_Funds']}
+        Purpose of Transaction: {row['Purpose_Of_Transaction']}
+        Beneficial Owner: {row['Beneficial_Owner']}
+    """
+
+# ========== API Routes ==========
+@app.post("/train")
+def train_model():
+    df = pd.read_csv(DATA_PATH)
+    df = df.fillna("")
+
+    df["text_input"] = df.apply(create_text_input, axis=1)
+    X = df["text_input"]
+    y = df[["Maker_Action", "Escalation_Level", "Risk_Category", "Risk_Drivers", "Investigation_Outcome"]]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    vectorizer = TfidfVectorizer()
+    classifier = MultiOutputClassifier(LogisticRegression(max_iter=1000))
+    pipeline = Pipeline([
+        ("vectorizer", vectorizer),
+        ("classifier", classifier)
+    ])
+
+    pipeline.fit(X_train, y_train)
+
+    os.makedirs(MODEL_DIR, exist_ok=True)
+    joblib.dump(pipeline, MODEL_PATH)
+
+    accuracy = pipeline.score(X_test, y_test)
+    return {"message": "Model trained and saved.", "accuracy": accuracy}
+
+@app.post("/predict")
+def predict(request: TransactionData):
+    try:
+        model = joblib.load(MODEL_PATH)
+        input_data = pd.DataFrame([request.dict()])
+        input_data = input_data.fillna("")
+        text_input = create_text_input(input_data.iloc[0])
+        prediction = model.predict([text_input])[0]
+        return {
+            "Maker_Action": prediction[0],
+            "Escalation_Level": prediction[1],
+            "Risk_Category": prediction[2],
+            "Risk_Drivers": prediction[3],
+            "Investigation_Outcome": prediction[4],
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/validate")
+def validate_input(request: TransactionData):
+    return {"message": "Input is valid."}
+
+@app.get("/test")
+def test_api():
+    return {"message": "Test successful."}