feat: Add resilience hardening for csv parser

.env.example

@@ -9,3 +9,6 @@ POSTGRES_DB=alemno_payments
 REDIS_HOST=localhost
 REDIS_PORT=6379
 REDIS_DB=0
+
+# Gemini API Config
+GEMINI_API_KEY=

app/core/config.py

@@ -1,3 +1,4 @@
+from typing import Optional
 from pydantic import Field, computed_field
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
@@ -23,6 +24,9 @@ class Settings(BaseSettings):
     REDIS_PORT: int = Field(default=6379)
     REDIS_DB: int = Field(default=0)
 
+    # Gemini
+    GEMINI_API_KEY: Optional[str] = Field(default=None)
+
     @computed_field
     @property
     def DATABASE_URL(self) -> str:

app/utils/csv_parser.py

@@ -75,14 +75,24 @@ def parse_and_validate_csv(content: bytes) -> List[Dict[str, Any]]:
             else None
         )
 
-        if not all([txn_id, date_str, amount_str, merchant]):
+        if not all([date_str, amount_str, merchant]):
             raise InvalidCSVException(
-                f"Row {line_num}: Empty values are not allowed in required fields"
+                f"Row {line_num}: Empty values are not allowed in required fields (date, amount, merchant)"
             )
 
+        # Auto-generate txn_id if missing or empty
+        if not txn_id or not txn_id.strip():
+            import uuid
+            txn_id = f"GEN_{uuid.uuid4().hex[:8].upper()}"
+        else:
+            txn_id = txn_id.strip()
+
         # Validate amount
+        cleaned_amount_str = (
+            amount_str.strip().replace("$", "").replace("₹", "").replace(",", "")
+        )
         try:
-            amount = float(amount_str.strip())
+            amount = float(cleaned_amount_str)
         except ValueError as e:
             raise InvalidCSVException(
                 f"Row {line_num}: Invalid amount '{amount_str}'. Must be a number."

app/worker.py

@@ -1,7 +1,13 @@
 import asyncio
+import json
+import os
+import statistics
 from datetime import datetime, date
 from typing import Any, Dict, List
+from sqlalchemy import select
 from celery import Celery
+from pydantic import BaseModel, Field
+from google import genai
 from app.core.config import settings
 from app.core.logging import logger
 from app.db.session import AsyncSessionLocal
@@ -13,6 +19,100 @@ from app.clients.exchange_rate_client import ExchangeRateClient
 exchange_rate_client = ExchangeRateClient()
 
 
+class TransactionClassification(BaseModel):
+    txn_id: str = Field(
+        description="The unique transaction ID (txn_id) from the input."
+    )
+    category: str = Field(
+        description="Assigned category, must be one of: Food, Shopping, Travel, Transport, Utilities, Cash Withdrawal, Entertainment, or Other."
+    )
+
+
+class BatchClassificationResponse(BaseModel):
+    classifications: List[TransactionClassification]
+
+
+async def classify_transactions_batch(
+    client: genai.Client,
+    batch_txns: List[Dict[str, Any]],
+) -> Dict[str, Dict[str, Any]]:
+    """
+    Calls the Gemini model to classify a batch of transactions.
+    Implements up to 3 retries (4 attempts total) with exponential backoff.
+    Returns a dictionary mapping txn_id to classification results.
+    """
+    # Format the input for the LLM
+    prompt_data = []
+    for t in batch_txns:
+        prompt_data.append(
+            {
+                "txn_id": t["txn_id"],
+                "merchant": t["merchant"],
+                "amount": t["amount"],
+                "currency": t.get("currency", "INR"),
+            }
+        )
+
+    input_prompt = (
+        "Classify the following financial transactions. For each transaction, match its txn_id "
+        "and assign one of the allowed categories: Food, Shopping, Travel, Transport, Utilities, "
+        "Cash Withdrawal, Entertainment, or Other based on the merchant name and details:\n\n"
+        f"{json.dumps(prompt_data, indent=2)}"
+    )
+
+    for attempt in range(1, 5):
+        try:
+            logger.info(
+                f"Sending batch of {len(batch_txns)} transactions to LLM (Attempt {attempt})..."
+            )
+            loop = asyncio.get_running_loop()
+            # Wrap synchronous genai SDK call in executor
+            interaction = await loop.run_in_executor(
+                None,
+                lambda: client.interactions.create(
+                    model="gemini-3.5-flash",
+                    input=input_prompt,
+                    response_format={
+                        "type": "text",
+                        "mime_type": "application/json",
+                        "schema": BatchClassificationResponse.model_json_schema(),
+                    },
+                ),
+            )
+
+            resp_obj = BatchClassificationResponse.model_validate_json(
+                interaction.output_text
+            )
+
+            results = {}
+            for item in resp_obj.classifications:
+                item_json = json.dumps(item.model_dump())
+                results[item.txn_id] = {
+                    "category": item.category,
+                    "raw_response": item_json,
+                    "failed": False,
+                }
+            return results
+        except Exception as e:
+            logger.warning(f"LLM Classification attempt {attempt} failed: {e}")
+            if attempt == 4:
+                break
+            sleep_seconds = 2**attempt
+            await asyncio.sleep(sleep_seconds)
+
+    logger.error(
+        "LLM Classification completely failed after all retries. Falling back to default."
+    )
+    results = {}
+    for t in batch_txns:
+        results[t["txn_id"]] = {
+            "category": "Other",
+            "raw_response": None,
+            "failed": True,
+        }
+    return results
+
+
 # Define Celery app
 celery_app = Celery(
     "alemno_worker",
@@ -32,7 +132,7 @@ celery_app.conf.update(
 
 def run_async(coro):
     """Helper to run async functions synchronously in Celery worker context"""
-    return asyncio.get_event_loop().run_until_complete(coro)
+    return asyncio.run(coro)
 
 
 @celery_app.task(name="tasks.process_transaction_job")
@@ -66,6 +166,148 @@ async def _process_job_async(job_id: str, transactions: List[Dict[str, Any]]):
 
             USD_TO_INR = await exchange_rate_client.get_usd_to_inr_rate()
 
+            # 1. Fetch existing transaction amounts and currencies for all accounts in this job to calculate median
+            account_ids = {
+                t.get("account_id") for t in transactions if t.get("account_id")
+            }
+            existing_amounts_inr = {}
+            if account_ids:
+                stmt = select(
+                    Transaction.account_id, Transaction.amount, Transaction.currency
+                ).where(Transaction.account_id.in_(account_ids))
+                res = await db.execute(stmt)
+                for acc_id, amt, curr in res.all():
+                    if acc_id not in existing_amounts_inr:
+                        existing_amounts_inr[acc_id] = []
+                    # Standardize to INR for median calculation
+                    amt_inr = amt * USD_TO_INR if curr.upper() == "USD" else amt
+                    existing_amounts_inr[acc_id].append(amt_inr)
+
+            # Group incoming transaction amounts standardized to INR
+            incoming_amounts_inr = {}
+            for t in transactions:
+                acc_id = t.get("account_id")
+                if acc_id:
+                    if acc_id not in incoming_amounts_inr:
+                        incoming_amounts_inr[acc_id] = []
+                    amt = float(t["amount"])
+                    curr = t.get("currency", "INR").strip().upper()
+                    amt_inr = amt * USD_TO_INR if curr == "USD" else amt
+                    incoming_amounts_inr[acc_id].append(amt_inr)
+
+            # Precalculate median of (existing + incoming) transactions in INR
+            account_medians_inr = {}
+            for acc_id in account_ids:
+                all_amts = existing_amounts_inr.get(
+                    acc_id, []
+                ) + incoming_amounts_inr.get(acc_id, [])
+                if all_amts:
+                    account_medians_inr[acc_id] = statistics.median(all_amts)
+
+            # 2. Call LLM to classify transactions without a category
+            # Find all uncategorized incoming transactions
+            uncategorized_txns = []
+            for t in transactions:
+                cat = t.get("category")
+                if not cat or cat.strip().lower() in {"", "general", "other", "none"}:
+                    uncategorized_txns.append(t)
+
+            llm_results = {}
+            api_key = settings.GEMINI_API_KEY or os.environ.get("GEMINI_API_KEY")
+            if uncategorized_txns:
+                if api_key:
+                    logger.info(
+                        f"Found {len(uncategorized_txns)} uncategorized transactions. Initializing GenAI client..."
+                    )
+                    try:
+                        client = genai.Client(api_key=api_key)
+                        # Batch transactions in chunks of 50
+                        batch_size = 50
+                        for i in range(0, len(uncategorized_txns), batch_size):
+                            batch = uncategorized_txns[i : i + batch_size]
+                            batch_results = await classify_transactions_batch(
+                                client, batch
+                            )
+                            llm_results.update(batch_results)
+                    except Exception as e:
+                        logger.error(
+                            f"Failed to initialize GenAI Client or classify: {e}"
+                        )
+                        # Fallback for all
+                        for t in uncategorized_txns:
+                            llm_results[t["txn_id"]] = {
+                                "category": "Other",
+                                "raw_response": None,
+                                "failed": True,
+                            }
+                else:
+                    logger.warning(
+                        "GEMINI_API_KEY is not set. Defaulting to local mock categorization."
+                    )
+                    # Mock categorization mapping based on merchant name keywords
+                    for t in uncategorized_txns:
+                        merchant_lower = t["merchant"].lower()
+                        cat = "Other"
+                        if any(
+                            kw in merchant_lower
+                            for kw in ["swiggy", "zomato", "restaurant", "cafe", "food"]
+                        ):
+                            cat = "Food"
+                        elif any(
+                            kw in merchant_lower
+                            for kw in ["ola", "uber", "taxi", "metro", "train"]
+                        ):
+                            cat = "Transport"
+                        elif any(
+                            kw in merchant_lower
+                            for kw in [
+                                "amazon",
+                                "flipkart",
+                                "shopping",
+                                "store",
+                                "supermarket",
+                            ]
+                        ):
+                            cat = "Shopping"
+                        elif any(
+                            kw in merchant_lower
+                            for kw in ["air", "flight", "hotel", "travel", "irctc"]
+                        ):
+                            cat = "Travel"
+                        elif any(
+                            kw in merchant_lower
+                            for kw in [
+                                "electric",
+                                "power",
+                                "water",
+                                "bill",
+                                "utility",
+                                "utilities",
+                            ]
+                        ):
+                            cat = "Utilities"
+                        elif any(
+                            kw in merchant_lower for kw in ["atm", "cash", "withdrawal"]
+                        ):
+                            cat = "Cash Withdrawal"
+                        elif any(
+                            kw in merchant_lower
+                            for kw in [
+                                "netflix",
+                                "spotify",
+                                "movie",
+                                "show",
+                                "entertainment",
+                            ]
+                        ):
+                            cat = "Entertainment"
+
+                        llm_results[t["txn_id"]] = {
+                            "category": cat,
+                            "raw_response": f'{{"category": "{cat}", "confidence": 0.8, "status": "mocked_offline"}}',
+                            "failed": False,
+                        }
+
             for t in transactions:
                 txn_id = t["txn_id"]
                 raw_date = t["date"]
@@ -78,7 +320,7 @@ async def _process_job_async(job_id: str, transactions: List[Dict[str, Any]]):
                 merchant = t["merchant"].strip()
                 amount = float(t["amount"])
                 currency = t.get("currency", "INR").strip().upper()
-                category = t.get("category", "General")
+                category = t.get("category") or ""
                 account_id = t.get("account_id")
 
                 # Spend calculation and currency conversion
@@ -95,28 +337,62 @@ async def _process_job_async(job_id: str, transactions: List[Dict[str, Any]]):
                 # Aggregate merchant spend (in USD for standardization)
                 merchant_spends[merchant] = merchant_spends.get(merchant, 0.0) + amt_usd
 
-                # Heuristic Anomaly detection
-                is_anomaly = False
-                anomaly_reason = None
+                # Anomaly detection checks
+                reasons = []
+
+                # A. 3x Median Outlier Check
+                if account_id and account_id in account_medians_inr:
+                    median_inr = account_medians_inr[account_id]
+                    if amt_inr > 3.0 * median_inr:
+                        reasons.append(
+                            "Statistical outlier (amount exceeds 3x account median)"
+                        )
+
+                # B. Domestic Brand transacted in USD check
+                domestic_brands = {"swiggy", "ola", "irctc"}
+                merchant_lower = merchant.lower()
+                if currency == "USD" and any(
+                    brand in merchant_lower for brand in domestic_brands
+                ):
+                    reasons.append("Domestic brand transacted in USD")
+
+                # C. High value transaction check
                 if amt_usd > 5000.0:
-                    is_anomaly = True
-                    anomaly_reason = "High value transaction (> $5,000 USD equivalent)"
-                else:
-                    desc_lower = merchant.lower()
-                    if any(
-                        k in desc_lower
-                        for k in ["suspend", "hack", "error", "unknown", "fraud"]
-                    ):
-                        is_anomaly = True
-                        anomaly_reason = "Suspicious merchant keyword"
+                    reasons.append("High value transaction (> $5,000 USD equivalent)")
+
+                # D. Suspicious keyword check
+                desc_lower = merchant.lower()
+                if any(
+                    k in desc_lower
+                    for k in ["suspend", "hack", "error", "unknown", "fraud"]
+                ):
+                    reasons.append("Suspicious merchant keyword")
+
+                is_anomaly = len(reasons) > 0
+                anomaly_reason = "; ".join(reasons) if is_anomaly else None
 
                 if is_anomaly:
                     anomaly_count += 1
 
-                # Mock
-                llm_failed = False
-                llm_category = category.capitalize() if category else "General"
-                llm_raw_response = f'{{"category": "{llm_category}", "confidence": 0.95, "status": "processed"}}'
+                # Check if it was uncategorized initially and assign LLM category
+                cat_lower = category.strip().lower()
+                is_uncategorized = not cat_lower or cat_lower in {
+                    "",
+                    "general",
+                    "other",
+                    "none",
+                }
+
+                if is_uncategorized and txn_id in llm_results:
+                    classification = llm_results[txn_id]
+                    llm_category = classification["category"].strip()
+                    llm_raw_response = classification["raw_response"]
+                    llm_failed = classification["failed"]
+                    category = llm_category
+                else:
+                    llm_category = category.strip() if category else "Other"
+                    llm_raw_response = None
+                    llm_failed = False
 
                 db_txn = Transaction(
                     job_id=job_id,

docker-compose.yml

@@ -43,6 +43,7 @@ services:
       - REDIS_HOST=redis
       - REDIS_PORT=6379
       - REDIS_DB=0
+      - GEMINI_API_KEY
     depends_on:
       db:
         condition: service_healthy
@@ -64,6 +65,7 @@ services:
       - REDIS_HOST=redis
       - REDIS_PORT=6379
       - REDIS_DB=0
+      - GEMINI_API_KEY
     command: celery -A app.worker.celery_app worker --loglevel=info
     depends_on:
       db:

pyproject.toml

@@ -8,6 +8,7 @@ dependencies = [
     "asyncpg>=0.31.0",
     "celery>=5.6.3",
     "fastapi>=0.138.1",
+    "google-genai>=2.10.0",
     "httpx>=0.28.1",
     "pydantic-settings>=2.14.2",
     "pytest>=9.1.1",

tests/api/test_jobs_api.py

@@ -101,3 +101,49 @@ async def test_get_all_jobs(client: AsyncClient):
     response = await client.get("/api/v1/jobs")
     assert response.status_code == 200
     assert isinstance(response.json(), list)
+
+
+async def test_upload_csv_new_anomalies(client: AsyncClient):
+    # ACC03 has Cafe transactions: 10 INR, 12 INR, 100 INR (median 12 INR, 3x median = 36 INR)
+    # ACC03 also has a domestic merchant 'Ola' in USD
+    csv_content = (
+        "txn_id,date,amount,merchant,currency,category,account\n"
+        "TX3001,2026-06-25,10.00,Cafe,INR,Food,ACC03\n"
+        "TX3002,2026-06-26,12.00,Cafe,INR,Food,ACC03\n"
+        "TX3003,2026-06-27,100.00,Cafe,INR,Food,ACC03\n"  # 3x median outlier (> 36)
+        "TX3004,2026-06-28,50.00,Ola cabs,USD,Travel,ACC03\n"  # Domestic in USD
+    )
+
+    files = {
+        "file": (
+            "transactions_new_anomalies.csv",
+            io.BytesIO(csv_content.encode("utf-8")),
+            "text/csv",
+        )
+    }
+
+    response = await client.post("/api/v1/jobs/upload", files=files)
+    assert response.status_code == 201
+    job_id = response.json()["id"]
+
+    # Retrieve status and verify anomalies count
+    status_response = await client.get(f"/api/v1/jobs/{job_id}/status")
+    assert status_response.status_code == 200
+    status_data = status_response.json()
+    assert status_data["summary"]["anomaly_count"] == 2  # TX3003 and TX3004
+
+    # Retrieve full results and verify reasons
+    results_response = await client.get(f"/api/v1/jobs/{job_id}/results")
+    assert results_response.status_code == 200
+    results_data = results_response.json()
+    txns = results_data["transactions"]
+
+    # TX3003 outlier check
+    outlier_txn = next(t for t in txns if t["txn_id"] == "TX3003")
+    assert outlier_txn["is_anomaly"] is True
+    assert "Statistical outlier" in outlier_txn["anomaly_reason"]
+
+    # TX3004 domestic in USD check
+    domestic_txn = next(t for t in txns if t["txn_id"] == "TX3004")
+    assert domestic_txn["is_anomaly"] is True
+    assert "Domestic brand transacted in USD" in domestic_txn["anomaly_reason"]

tests/conftest.py

@@ -36,7 +36,7 @@ def initialize_test_db():
     """Create test tables and clean them up after all tests finish."""
     global _db_offline
     try:
-        loop = asyncio.get_event_loop_policy().get_event_loop()
+        loop = asyncio.get_event_loop()
     except RuntimeError:
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)

tests/services/test_csv_parser.py

@@ -52,3 +52,15 @@ def test_parse_invalid_date():
     with pytest.raises(InvalidCSVException) as exc_info:
         parse_and_validate_csv(invalid_csv.encode("utf-8"))
     assert "Invalid date format" in str(exc_info.value)
+
+
+def test_parse_amount_with_symbols():
+    csv_data = (
+        "txn_id,date,amount,merchant\n"
+        "TX1001,2026-06-25,$120.50,Weekly grocery shop\n"
+        'TX1002,2026-06-26,"₹1,500.00",Weekly grocery shop\n'
+    )
+    result = parse_and_validate_csv(csv_data.encode("utf-8"))
+    assert len(result) == 2
+    assert result[0]["amount"] == 120.50
+    assert result[1]["amount"] == 1500.00

tests/services/test_llm_classification.py

@@ -0,0 +1,44 @@
+import pytest
+from app.worker import classify_transactions_batch
+from google import genai
+from unittest.mock import MagicMock, patch
+
+pytestmark = pytest.mark.asyncio
+
+
+async def test_classify_transactions_batch_retry_success():
+    # Mock GenAI client and interaction creation
+    mock_client = MagicMock(spec=genai.Client)
+    mock_interaction = MagicMock()
+    mock_interaction.output_text = '{"classifications": [{"txn_id": "TX1", "category": "Food"}, {"txn_id": "TX2", "category": "Shopping"}]}'
+    mock_client.interactions.create.return_value = mock_interaction
+
+    batch_txns = [
+        {"txn_id": "TX1", "merchant": "Swiggy", "amount": 150.0},
+        {"txn_id": "TX2", "merchant": "Amazon", "amount": 1200.0},
+    ]
+
+    results = await classify_transactions_batch(mock_client, batch_txns)
+    assert len(results) == 2
+    assert results["TX1"]["category"] == "Food"
+    assert results["TX1"]["failed"] is False
+    assert results["TX2"]["category"] == "Shopping"
+    assert results["TX2"]["failed"] is False
+
+
+async def test_classify_transactions_batch_retry_failure():
+    mock_client = MagicMock(spec=genai.Client)
+    # Simulate API call failure (e.g. invalid API key or connection error)
+    mock_client.interactions.create.side_effect = Exception("API Key Error")
+
+    batch_txns = [
+        {"txn_id": "TX1", "merchant": "Swiggy", "amount": 150.0},
+    ]
+
+    # Patch sleep to make retry test run instantly
+    with patch("asyncio.sleep", return_value=None):
+        results = await classify_transactions_batch(mock_client, batch_txns)
+
+    assert len(results) == 1
+    assert results["TX1"]["category"] == "Other"
+    assert results["TX1"]["failed"] is True

uv.lock

@@ -10,6 +10,7 @@ dependencies = [
     { name = "asyncpg" },
     { name = "celery" },
     { name = "fastapi" },
+    { name = "google-genai" },
     { name = "httpx" },
     { name = "pydantic-settings" },
     { name = "pytest" },
@@ -31,6 +32,7 @@ requires-dist = [
     { name = "asyncpg", specifier = ">=0.31.0" },
     { name = "celery", specifier = ">=5.6.3" },
     { name = "fastapi", specifier = ">=0.138.1" },
+    { name = "google-genai", specifier = ">=2.10.0" },
     { name = "httpx", specifier = ">=0.28.1" },
     { name = "pydantic-settings", specifier = ">=2.14.2" },
     { name = "pytest", specifier = ">=9.1.1" },
@@ -149,6 +151,80 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ef/2f/c5464532e965badff2f4c4c1a3a83f5697f0d7c407ed0cda44aaa99bb451/certifi-2026.6.17-py3-none-any.whl", hash = "sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db", size = 133289, upload-time = "2026-06-17T10:31:06.348Z" },
 ]
 
+[[package]]
+name = "cffi"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" }
+wheels = [
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