diff --git a/salary_analytics/__pycache__/api.cpython-311.pyc b/salary_analytics/__pycache__/api.cpython-311.pyc
index 374ad32..af60713 100644
Binary files a/salary_analytics/__pycache__/api.cpython-311.pyc and b/salary_analytics/__pycache__/api.cpython-311.pyc differ
diff --git a/salary_analytics/api.py b/salary_analytics/api.py
index 8172a9b..ed4e7e5 100644
--- a/salary_analytics/api.py
+++ b/salary_analytics/api.py
@@ -2,7 +2,7 @@
 FastAPI application for salary analytics.
 """
 
-from fastapi import FastAPI, HTTPException, BackgroundTasks, UploadFile, File
+from fastapi import FastAPI, HTTPException, BackgroundTasks, UploadFile, File, Depends
 from fastapi.responses import FileResponse
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
@@ -242,13 +242,13 @@ async def run_full_pipeline():
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.post("/load-data")
-async def load_data(source: str = "db", file: UploadFile = None):
+async def load_data(source: str = "db", file: Optional[UploadFile] = File(None)):
     """
     Load data from either database or CSV file.
     
     Args:
         source (str): Source of data ('db' or 'csv')
-        file (UploadFile): CSV file to load (required if source is 'csv')
+        file (UploadFile, optional): CSV file to load (required if source is 'csv')
     
     Returns:
         dict: Status of data loading
@@ -288,15 +288,25 @@ async def load_data(source: str = "db", file: UploadFile = None):
         logger.error(f"Error loading data: {str(e)}")
         raise HTTPException(status_code=500, detail=str(e))
 
+async def get_file_if_csv(source: str, file: Optional[UploadFile] = File(None)):
+    """Dependency to handle file upload only when source is csv."""
+    if source == 'csv' and not file:
+        raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
+    return file
+
 @app.post("/run/streaming-pipeline", response_model=List[BatchResponse])
-async def run_streaming_pipeline(source: str = "db", file: UploadFile = None, batch_size: int = 10000):
+async def run_streaming_pipeline(
+    source: str = "db",
+    batch_size: int = 10000,
+    file: Optional[UploadFile] = Depends(get_file_if_csv)
+):
     """
     Run the complete salary analytics pipeline in batches.
     
     Args:
         source (str): Source of data ('db' or 'csv')
-        file (UploadFile): CSV file to load (required if source is 'csv')
         batch_size (int): Number of rows to process in each batch
+        file (UploadFile, optional): CSV file to load (required if source is 'csv')
     
     Returns:
         List[BatchResponse]: List of responses for each batch processed
@@ -305,9 +315,6 @@ async def run_streaming_pipeline(source: str = "db", file: UploadFile = None, ba
         if source not in ['db', 'csv']:
             raise HTTPException(status_code=400, detail="Source must be either 'db' or 'csv'")
         
-        if source == 'csv' and not file:
-            raise HTTPException(status_code=400, detail="File must be provided when loading from CSV")
-        
         # Initialize data loader
         data_loader = DataLoader()
         data_loader.chunk_size = batch_size
@@ -326,12 +333,14 @@ async def run_streaming_pipeline(source: str = "db", file: UploadFile = None, ba
             chunk['trx_start_date'] = pd.to_datetime(chunk['trx_start_date'])
             chunk['trx_end_date'] = pd.to_datetime(chunk['trx_end_date'])
             
+            # Rename columns
             chunk = chunk.rename(columns={
-                    'd1': 'trx_type',
-                    'd2': 'trx_subtype',
-                    'd3': 'initiated_by',
-                    'd4': 'customer_id'
-                    })
+                'd1': 'trx_type',
+                'd2': 'trx_subtype',
+                'd3': 'initiated_by',
+                'd4': 'customer_id'
+            })
+            
             chunk = chunk.dropna()
             
             return chunk