Refactor

Author: priyanshu6238

backend/app/api/routes/evaluation.py

@@ -14,6 +14,7 @@
 logger = logging.getLogger(__name__)
 router = APIRouter(tags=["evaluation"])
 
+
 @router.post("/evaluation/upload-dataset")
 async def upload_dataset(
     dataset_name: str,
@@ -26,7 +27,7 @@ async def upload_dataset(
     The CSV file should have two columns: input and expected_output.
     Only the first 30 rows will be processed.
     """
-    if not file.filename.endswith('.csv'):
+    if not file.filename.endswith(".csv"):
         return APIResponse.failure_response(error="Only CSV files are supported")
 
     # Get Langfuse credentials
@@ -57,21 +58,25 @@ async def upload_dataset(
         # Read and validate CSV file
         contents = await file.read()
         logger.info(f"Read {len(contents)} bytes from file")
-        
+
         # Decode contents and create CSV reader
-        csv_content = contents.decode('utf-8')
+        csv_content = contents.decode("utf-8")
         logger.info(f"CSV content preview: {csv_content[:200]}...")
-        
+
         csv_file = io.StringIO(csv_content)
         reader = csv.DictReader(csv_file)
-        
+
         # Validate headers
         if not reader.fieldnames:
-            return APIResponse.failure_response(error="CSV file is empty or has no headers")
-            
+            return APIResponse.failure_response(
+                error="CSV file is empty or has no headers"
+            )
+
         logger.info(f"CSV headers found: {reader.fieldnames}")
-        
-        if not all(header in reader.fieldnames for header in ['input', 'expected_output']):
+
+        if not all(
+            header in reader.fieldnames for header in ["input", "expected_output"]
+        ):
             return APIResponse.failure_response(
                 error="CSV must contain 'input' and 'expected_output' columns"
             )
@@ -82,36 +87,40 @@ async def upload_dataset(
             logger.info(f"Created dataset with ID: {dataset.id}")
         except Exception as e:
             logger.error(f"Error creating dataset: {str(e)}")
-            return APIResponse.failure_response(error=f"Failed to create dataset: {str(e)}")
+            return APIResponse.failure_response(
+                error=f"Failed to create dataset: {str(e)}"
+            )
 
         # Process rows (limited to 30)
         rows_processed = 0
         rows_data = []  # Store rows for logging
-        
+
         for row in reader:
             if rows_processed >= 30:
                 break
 
             try:
                 # Log the row data
                 logger.info(f"Processing row {rows_processed + 1}: {row}")
-                
+
                 # Create dataset item
                 item = langfuse.create_dataset_item(
                     dataset_name=dataset_name,
-                    input=row['input'],
-                    expected_output=row['expected_output']
+                    input=row["input"],
+                    expected_output=row["expected_output"],
                 )
                 logger.info(f"Created dataset item with ID: {item.id}")
-                
+
                 rows_processed += 1
                 rows_data.append(row)
             except Exception as e:
                 logger.error(f"Error processing row {rows_processed + 1}: {str(e)}")
                 continue
 
         if rows_processed == 0:
-            return APIResponse.failure_response(error="No rows were successfully processed")
+            return APIResponse.failure_response(
+                error="No rows were successfully processed"
+            )
 
         # Log summary
         logger.info(f"Successfully processed {rows_processed} rows")
@@ -121,12 +130,12 @@ async def upload_dataset(
             data={
                 "message": f"Successfully uploaded {rows_processed} rows to dataset '{dataset_name}'",
                 "rows_processed": rows_processed,
-                "dataset_id": dataset.id if hasattr(dataset, 'id') else None
+                "dataset_id": dataset.id if hasattr(dataset, "id") else None,
             }
         )
 
     except Exception as e:
         logger.error(f"Error uploading dataset: {str(e)}")
         return APIResponse.failure_response(error=str(e))
     finally:
-        await file.close() 
+        await file.close()