Create app.py

app.py

@@ -0,0 +1,196 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import torch
+import pandas as pd
+import numpy as np
+import os
+from typing import Optional
+from torch.utils.data import DataLoader
+
+from transformers import DebertaTokenizer
+from config import (
+    TEXT_COLUMN, LABEL_COLUMNS, MAX_LEN, DEVICE,
+    DEBERTA_MODEL_NAME, METADATA_COLUMNS
+)
+from dataset_utils import load_label_encoders, get_tokenizer, ComplianceDataset
+from train_utils import predict_probabilities
+from models.deberta_model import DebertaMultiOutputModel
+
+app = FastAPI()
+
+# ✅ Load model and tokenizer
+model_path = "saved_models/DEBERTA_model.pth"
+tokenizer = get_tokenizer(DEBERTA_MODEL_NAME)
+label_encoders = load_label_encoders()
+num_classes = [len(label_encoders[col].classes_) for col in LABEL_COLUMNS]
+
+model = DebertaMultiOutputModel(num_classes).to(DEVICE)
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"❌ Model not found at {model_path}")
+model.load_state_dict(torch.load(model_path, map_location=DEVICE))
+model.eval()
+
+# ✅ Request 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
+
+class PredictionRequest(BaseModel):
+    transaction_data: TransactionData
+
+# ✅ Health check routes
+@app.get("/")
+async def root():
+    return {"status": "healthy", "message": "DeBERTa API is running"}
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+# ✅ Inference endpoint
+@app.post("/predict")
+async def predict(request: PredictionRequest):
+    try:
+        input_data = pd.DataFrame([request.transaction_data.dict()])
+
+        # 🧠 Construct input text
+        text_input = f"""
+        Transaction ID: {input_data['Transaction_Id'].iloc[0]}
+        Origin: {input_data['Origin'].iloc[0]}
+        Designation: {input_data['Designation'].iloc[0]}
+        Keywords: {input_data['Keywords'].iloc[0]}
+        Name: {input_data['Name'].iloc[0]}
+        SWIFT Tag: {input_data['SWIFT_Tag'].iloc[0]}
+        Currency: {input_data['Currency'].iloc[0]}
+        Entity: {input_data['Entity'].iloc[0]}
+        Message: {input_data['Message'].iloc[0]}
+        City: {input_data['City'].iloc[0]}
+        Country: {input_data['Country'].iloc[0]}
+        State: {input_data['State'].iloc[0]}
+        Hit Type: {input_data['Hit_Type'].iloc[0]}
+        Record Matching String: {input_data['Record_Matching_String'].iloc[0]}
+        WatchList Match String: {input_data['WatchList_Match_String'].iloc[0]}
+        Payment Sender: {input_data['Payment_Sender_Name'].iloc[0]}
+        Payment Receiver: {input_data['Payment_Reciever_Name'].iloc[0]}
+        Swift Message Type: {input_data['Swift_Message_Type'].iloc[0]}
+        Text Sanction Data: {input_data['Text_Sanction_Data'].iloc[0]}
+        Matched Sanctioned Entity: {input_data['Matched_Sanctioned_Entity'].iloc[0]}
+        Red Flag Reason: {input_data['Red_Flag_Reason'].iloc[0]}
+        Risk Level: {input_data['Risk_Level'].iloc[0]}
+        Risk Score: {input_data['Risk_Score'].iloc[0]}
+        CDD Level: {input_data['CDD_Level'].iloc[0]}
+        PEP Status: {input_data['PEP_Status'].iloc[0]}
+        Sanction Description: {input_data['Sanction_Description'].iloc[0]}
+        Checker Notes: {input_data['Checker_Notes'].iloc[0]}
+        Sanction Context: {input_data['Sanction_Context'].iloc[0]}
+        Maker Action: {input_data['Maker_Action'].iloc[0]}
+        Customer Type: {input_data['Customer_Type'].iloc[0]}
+        Industry: {input_data['Industry'].iloc[0]}
+        Transaction Type: {input_data['Transaction_Type'].iloc[0]}
+        Transaction Channel: {input_data['Transaction_Channel'].iloc[0]}
+        Geographic Origin: {input_data['Geographic_Origin'].iloc[0]}
+        Geographic Destination: {input_data['Geographic_Destination'].iloc[0]}
+        Risk Category: {input_data['Risk_Category'].iloc[0]}
+        Risk Drivers: {input_data['Risk_Drivers'].iloc[0]}
+        Alert Status: {input_data['Alert_Status'].iloc[0]}
+        Investigation Outcome: {input_data['Investigation_Outcome'].iloc[0]}
+        Source of Funds: {input_data['Source_Of_Funds'].iloc[0]}
+        Purpose of Transaction: {input_data['Purpose_Of_Transaction'].iloc[0]}
+        Beneficial Owner: {input_data['Beneficial_Owner'].iloc[0]}
+        """
+
+        # ⚙️ Create dataset
+        dataset = ComplianceDataset(
+            texts=[text_input],
+            labels=[[0] * len(LABEL_COLUMNS)],
+            tokenizer=tokenizer,
+            max_len=MAX_LEN
+        )
+        loader = DataLoader(dataset, batch_size=1)
+
+        all_probabilities = predict_probabilities(model, loader)
+
+        # 📊 Format response
+        response = {}
+        for i, (col, probs) in enumerate(zip(LABEL_COLUMNS, all_probabilities)):
+            pred = np.argmax(probs[0])
+            decoded_pred = label_encoders[col].inverse_transform([pred])[0]
+            response[col] = {
+                "prediction": decoded_pred,
+                "probabilities": {
+                    label: float(probs[0][j])
+                    for j, label in enumerate(label_encoders[col].classes_)
+                }
+            }
+
+        return response
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 🖥️ Entry point for Spaces
+if __name__ == "__main__":
+    import uvicorn
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)