Refactor services for improved readability and maintainability

- Cleaned up whitespace and formatting in blob_service.py, cosmos_service.py, search_service.py, and settings.py.
- Ensured consistent spacing and indentation throughout the codebase.
- Enhanced code clarity by removing unnecessary blank lines and aligning comments.
- No functional changes were made; this commit focuses solely on code style improvements.

Author: Pavan-Microsoft

content-gen/src/backend/agents/image_content_agent.py

@@ -18,24 +18,24 @@ def _truncate_for_dalle(product_description: str, max_chars: int = 1500) -> str:
     """
     Truncate product descriptions to fit DALL-E's 4000 character limit.
     Extracts the most visually relevant information (colors, hex codes, finishes).
-    
+
     Args:
         product_description: The full product description(s)
         max_chars: Maximum characters to allow for product context
-        
+
     Returns:
         Truncated description with essential visual details
     """
     if not product_description or len(product_description) <= max_chars:
         return product_description
-    
+
     import re
-    
+
     # Extract essential visual info: product names, hex codes, color descriptions
     lines = product_description.split('\n')
     essential_parts = []
     current_product = ""
-    
+
     for line in lines:
         # Keep product name headers
         if line.startswith('### '):
@@ -54,13 +54,13 @@ def _truncate_for_dalle(product_description: str, max_chars: int = 1500) -> str:
         # Keep finish descriptions
         elif 'finish' in line.lower() or 'matte' in line.lower() or 'eggshell' in line.lower():
             essential_parts.append(line.strip()[:200])
-    
+
     result = '\n'.join(essential_parts)
-    
+
     # If still too long, just truncate with ellipsis
     if len(result) > max_chars:
-        result = result[:max_chars-50] + '\n\n[Additional details truncated for DALL-E]'
-    
+        result = result[:max_chars - 50] + '\n\n[Additional details truncated for DALL-E]'
+
     return result
 
 
@@ -73,9 +73,9 @@ async def generate_dalle_image(
 ) -> dict:
     """
     Generate a marketing image using DALL-E 3, gpt-image-1, or gpt-image-1.5.
-    
+
     The model used is determined by AZURE_OPENAI_IMAGE_MODEL setting.
-    
+
     Args:
         prompt: The main image generation prompt
         product_description: Auto-generated description of product image (for context)
@@ -86,14 +86,14 @@ async def generate_dalle_image(
         quality: Image quality (model-specific, uses settings default if not provided)
               - dall-e-3: standard, hd
               - gpt-image-1/1.5: low, medium, high, auto
-    
+
     Returns:
         Dictionary containing generated image data and metadata
     """
     # Determine which model to use
     image_model = app_settings.azure_openai.effective_image_model
     logger.info(f"Using image generation model: {image_model}")
-    
+
     # Use appropriate generator based on model
     if image_model in ["gpt-image-1", "gpt-image-1.5"]:
         return await _generate_gpt_image(prompt, product_description, scene_description, size, quality)
@@ -110,31 +110,31 @@ async def _generate_dalle_image(
 ) -> dict:
     """
     Generate a marketing image using DALL-E 3.
-    
+
     Args:
         prompt: The main image generation prompt
         product_description: Auto-generated description of product image (for context)
         scene_description: Scene/setting description from creative brief
         size: Image size (1024x1024, 1024x1792, 1792x1024)
         quality: Image quality (standard, hd)
-    
+
     Returns:
         Dictionary containing generated image data and metadata
     """
     brand = app_settings.brand_guidelines
-    
+
     # Use defaults from settings if not provided
     size = size or app_settings.azure_openai.image_size
     quality = quality or app_settings.azure_openai.image_quality
-    
+
     # DALL-E 3 has a 4000 character limit for prompts
     # Truncate product descriptions to essential visual info
     truncated_product_desc = _truncate_for_dalle(product_description, max_chars=1500)
-    
+
     # Also truncate the main prompt if it's too long
     main_prompt = prompt[:1000] if len(prompt) > 1000 else prompt
     scene_desc = scene_description[:500] if scene_description and len(scene_description) > 500 else scene_description
-    
+
     # Build the full prompt with product context and brand guidelines
     full_prompt = f"""⚠️ ABSOLUTE RULE: THIS IMAGE MUST CONTAIN ZERO TEXT. NO WORDS. NO LETTERS. NO PRODUCT NAMES. NO LABELS.
 
@@ -153,7 +153,7 @@ async def _generate_dalle_image(
 
 MANDATORY FINAL CHECKLIST:
 ✗ NO product names in the image
-✗ NO color names in the image  
+✗ NO color names in the image
 ✗ NO text overlays or labels
 ✗ NO typography or lettering of any kind
 ✗ NO watermarks or logos
@@ -162,7 +162,7 @@ async def _generate_dalle_image(
 ✓ Accurately reproduce product colors using exact hex codes
 ✓ Professional, polished marketing image
 """
-    
+
     # Final safety check - DALL-E 3 has 4000 char limit
     if len(full_prompt) > 3900:
         logger.warning(f"Prompt too long ({len(full_prompt)} chars), truncating...")
@@ -190,20 +190,20 @@ async def _generate_dalle_image(
             credential = ManagedIdentityCredential(client_id=client_id)
         else:
             credential = DefaultAzureCredential()
-        
+
         # Get token for Azure OpenAI
         token = await credential.get_token("https://cognitiveservices.azure.com/.default")
-        
+
         # Use the dedicated DALL-E endpoint if configured, otherwise fall back to main endpoint
         dalle_endpoint = app_settings.azure_openai.dalle_endpoint or app_settings.azure_openai.endpoint
         logger.info(f"Using DALL-E endpoint: {dalle_endpoint}")
-        
+
         client = AsyncAzureOpenAI(
             azure_endpoint=dalle_endpoint,
             azure_ad_token=token.token,
             api_version=app_settings.azure_openai.preview_api_version,
         )
-        
+
         try:
             response = await client.images.generate(
                 model=app_settings.azure_openai.dalle_model,
@@ -213,9 +213,9 @@ async def _generate_dalle_image(
                 n=1,
                 response_format="b64_json"
             )
-            
+
             image_data = response.data[0]
-            
+
             return {
                 "success": True,
                 "image_base64": image_data.b64_json,
@@ -226,7 +226,7 @@ async def _generate_dalle_image(
         finally:
             # Properly close the async client to avoid unclosed session warnings
             await client.close()
-        
+
     except Exception as e:
         logger.exception(f"Error generating DALL-E image: {e}")
         return {
@@ -246,50 +246,50 @@ async def _generate_gpt_image(
 ) -> dict:
     """
     Generate a marketing image using gpt-image-1 or gpt-image-1.5.
-    
+
     gpt-image models have different capabilities than DALL-E 3:
     - Supports larger prompt sizes
     - Different size options: 1024x1024, 1536x1024, 1024x1536, auto
     - Different quality options: low, medium, high, auto
     - May have better instruction following
-    
+
     Args:
         prompt: The main image generation prompt
         product_description: Auto-generated description of product image (for context)
         scene_description: Scene/setting description from creative brief
         size: Image size (1024x1024, 1536x1024, 1024x1536, auto)
         quality: Image quality (low, medium, high, auto)
-    
+
     Returns:
         Dictionary containing generated image data and metadata
     """
     brand = app_settings.brand_guidelines
-    
+
     # Use defaults from settings if not provided
     # Map DALL-E quality settings to gpt-image-1 or gpt-image-1.5 equivalents if needed
     size = size or app_settings.azure_openai.image_size
     quality = quality or app_settings.azure_openai.image_quality
-    
+
     # Map DALL-E quality values to gpt-image-1 or gpt-image-1.5 equivalents
     quality_mapping = {
         "standard": "medium",
         "hd": "high",
     }
     quality = quality_mapping.get(quality, quality)
-    
+
     # Map DALL-E sizes to gpt-image-1 or gpt-image-1.5 equivalents if needed
     size_mapping = {
         "1024x1792": "1024x1536",  # Closest equivalent
         "1792x1024": "1536x1024",  # Closest equivalent
     }
     size = size_mapping.get(size, size)
-    
+
     # gpt-image-1 can handle larger prompts, so we can include more context
     truncated_product_desc = _truncate_for_dalle(product_description, max_chars=3000)
-    
+
     main_prompt = prompt[:2000] if len(prompt) > 2000 else prompt
     scene_desc = scene_description[:1000] if scene_description and len(scene_description) > 1000 else scene_description
-    
+
     # Build the full prompt with product context and brand guidelines
     full_prompt = f"""⚠️ ABSOLUTE RULE: THIS IMAGE MUST CONTAIN ZERO TEXT. NO WORDS. NO LETTERS. NO PRODUCT NAMES. NO COLOR NAMES. NO LABELS.
 
@@ -326,25 +326,23 @@ async def _generate_gpt_image(
             credential = ManagedIdentityCredential(client_id=client_id)
         else:
             credential = DefaultAzureCredential()
-        
+
         # Get token for Azure OpenAI
         token = await credential.get_token("https://cognitiveservices.azure.com/.default")
-        
+
         # Use gpt-image-1 specific endpoint if configured, otherwise DALL-E endpoint, otherwise main endpoint
-        image_endpoint = (
-            app_settings.azure_openai.gpt_image_endpoint or 
-            app_settings.azure_openai.dalle_endpoint or 
-            app_settings.azure_openai.endpoint
-        )
+        image_endpoint = (app_settings.azure_openai.gpt_image_endpoint
+                          or app_settings.azure_openai.dalle_endpoint
+                          or app_settings.azure_openai.endpoint)
         logger.info(f"Using gpt-image-1 endpoint: {image_endpoint}")
-        
+
         # Use the image-specific API version for gpt-image-1 (requires 2025-04-01-preview or newer)
         client = AsyncAzureOpenAI(
             azure_endpoint=image_endpoint,
             azure_ad_token=token.token,
             api_version=app_settings.azure_openai.image_api_version,
         )
-        
+
         try:
             # gpt-image-1/1.5 API call - note: gpt-image doesn't support response_format parameter
             # It returns base64 data directly in the response
@@ -355,12 +353,12 @@ async def _generate_gpt_image(
                 quality=quality,
                 n=1,
             )
-            
+
             image_data = response.data[0]
-            
+
             # gpt-image-1 returns b64_json directly without needing response_format parameter
             image_base64 = getattr(image_data, 'b64_json', None)
-            
+
             # If no b64_json, try to get URL and fetch the image
             if not image_base64 and hasattr(image_data, 'url') and image_data.url:
                 import aiohttp
@@ -370,15 +368,15 @@ async def _generate_gpt_image(
                             import base64
                             image_bytes = await resp.read()
                             image_base64 = base64.b64encode(image_bytes).decode('utf-8')
-            
+
             if not image_base64:
                 return {
                     "success": False,
                     "error": "No image data returned from gpt-image-1",
                     "prompt_used": full_prompt,
                     "model": "gpt-image-1",
                 }
-            
+
             return {
                 "success": True,
                 "image_base64": image_base64,
@@ -389,7 +387,7 @@ async def _generate_gpt_image(
         finally:
             # Properly close the async client to avoid unclosed session warnings
             await client.close()
-        
+
     except Exception as e:
         logger.exception(f"Error generating gpt-image-1 image: {e}")
         return {