added error recognition in spreadsheet

backend/app/routers/spreadsheet.py

@@ -4,40 +4,47 @@ import logging
 from app.services.spreadsheet_service import SampleSpreadsheetImporter, SpreadsheetImportError
 from fastapi.responses import FileResponse
 import os
+from pydantic import ValidationError  # Import ValidationError here
+
 
 router = APIRouter()
 logger = logging.getLogger(__name__)
 
+
 @router.get("/download-template", response_class=FileResponse)
 async def download_template():
-    # No changes here; just serves a static file
+    """Serve a template file for spreadsheet upload."""
     current_dir = os.path.dirname(__file__)
     template_path = os.path.join(current_dir, "../../downloads/V7_TELLSamplesSpreadsheetTemplate.xlsx")
+
     if not os.path.exists(template_path):
         raise HTTPException(status_code=404, detail="Template file not found.")
+
     return FileResponse(template_path, filename="template.xlsx",
                         media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
 
+
 @router.post("/upload", response_model=SpreadsheetResponse)
 async def upload_file(file: UploadFile = File(...)):
+    """Process the uploaded spreadsheet and return validation results."""
     try:
         logger.info(f"Received file: {file.filename}")
 
-        # Validate file type
+        # Validate file format
         if not file.filename.endswith('.xlsx'):
             logger.error("Invalid file format")
             raise HTTPException(status_code=400, detail="Invalid file format. Please upload an .xlsx file.")
 
-        # Process spreadsheet
+        # Initialize the importer and process the spreadsheet
         importer = SampleSpreadsheetImporter()
-        validated_model, errors, raw_data = importer.import_spreadsheet_with_errors(file)
+        validated_model, errors, raw_data, headers = importer.import_spreadsheet_with_errors(file)
 
-        # Collect dewar, puck, and sample names
+        # Extract unique values for dewars, pucks, and samples
         dewars = {sample.dewarname for sample in validated_model if sample.dewarname}
         pucks = {sample.puckname for sample in validated_model if sample.puckname}
         samples = {sample.crystalname for sample in validated_model if sample.crystalname}
 
-        # Construct response data
+        # Construct the response model with the processed data
         response_data = SpreadsheetResponse(
             data=validated_model,
             errors=errors,
@@ -47,14 +54,42 @@ async def upload_file(file: UploadFile = File(...)):
             pucks_count=len(pucks),
             pucks=list(pucks),
             samples_count=len(samples),
-            samples=list(samples)
+            samples=list(samples),
+            headers=headers  # Include headers in the response
         )
 
-        logger.info(f"Returning response: {response_data.dict()}")
+        logger.info(f"Returning response with {len(validated_model)} records and {len(errors)} errors.")
         return response_data
+
     except SpreadsheetImportError as e:
         logger.error(f"Spreadsheet import error: {str(e)}")
-        raise HTTPException(status_code=400, detail=str(e))
+        raise HTTPException(status_code=400, detail=f"Error processing spreadsheet: {str(e)}")
+
     except Exception as e:
-        logger.error(f"Failed to process file: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Failed to upload file. Please try again. {str(e)}")
+        logger.error(f"Unexpected error occurred: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Failed to upload file. Please try again. Error: {str(e)}")
+
+
+@router.post("/validate-cell")
+async def validate_cell(data: dict):
+    """Validate a single cell value based on expected column type."""
+    row_num = data.get("row")
+    col_name = data.get("column")
+    value = data.get("value")
+
+    importer = SampleSpreadsheetImporter()
+
+    # Determine the expected type based on column name
+    expected_type = importer.get_expected_type(col_name)
+
+    # Clean and validate the cell value
+    cleaned_value = importer._clean_value(value, expected_type)
+
+    try:
+        # Validate the cleaned value using the SpreadsheetModel (Pydantic validation)
+        SpreadsheetModel(**{col_name: cleaned_value})
+        return {"is_valid": True, "message": ""}
+    except ValidationError as e:
+        # If validation fails, return the first error message
+        message = e.errors()[0]['msg']
+        return {"is_valid": False, "message": message}