286 lines
13 KiB
Python
286 lines
13 KiB
Python
"""
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Image renderer for report generation using AI image generation.
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"""
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from .rendererBaseTemplate import BaseRenderer
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from typing import Dict, Any, Tuple, List
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import logging
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logger = logging.getLogger(__name__)
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class RendererImage(BaseRenderer):
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"""Renders content to image format using AI image generation."""
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@classmethod
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def get_supported_formats(cls) -> List[str]:
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"""Return supported image formats."""
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return ['png', 'jpg', 'jpeg', 'image']
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@classmethod
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def get_format_aliases(cls) -> List[str]:
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"""Return format aliases."""
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return ['img', 'picture', 'photo', 'graphic']
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@classmethod
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def get_priority(cls) -> int:
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"""Return priority for image renderer."""
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return 90
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async def render(self, extracted_content: Dict[str, Any], title: str, user_prompt: str = None, ai_service=None) -> Tuple[str, str]:
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"""Render extracted JSON content to image format using AI image generation."""
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try:
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# Generate AI image from content
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image_content = await self._generate_ai_image(extracted_content, title, user_prompt, ai_service)
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return image_content, "image/png"
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except Exception as e:
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self.logger.error(f"Error rendering image: {str(e)}")
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# Re-raise the exception instead of using fallback
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raise Exception(f"Image rendering failed: {str(e)}")
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async def _generate_ai_image(self, extracted_content: Dict[str, Any], title: str, user_prompt: str = None, ai_service=None) -> str:
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"""Generate AI image from extracted content."""
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try:
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if not ai_service:
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raise ValueError("AI service is required for image generation")
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# Validate JSON structure
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if not isinstance(extracted_content, dict):
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raise ValueError("Extracted content must be a dictionary")
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if "sections" not in extracted_content:
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raise ValueError("Extracted content must contain 'sections' field")
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# Use title from JSON metadata if available, otherwise use provided title
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document_title = extracted_content.get("metadata", {}).get("title", title)
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# Create AI prompt for image generation
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image_prompt = await self._create_imageGenerate_prompt(extracted_content, document_title, user_prompt, ai_service)
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# Save image generation prompt to debug
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ai_service.services.utils.writeDebugFile(image_prompt, "image_generation_prompt")
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# Generate image using AI
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image_result = await ai_service.aiObjects.generateImage(
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prompt=image_prompt,
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size="1024x1024",
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quality="standard",
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style="vivid"
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)
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# Save image generation response to debug
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ai_service.services.utils.writeDebugFile(str(image_result), "image_generation_response")
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# Extract base64 image data from result
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if image_result and image_result.get("success", False):
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image_data = image_result.get("image_data", "")
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if image_data:
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return image_data
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else:
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raise ValueError("No image data returned from AI")
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else:
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error_msg = image_result.get("error", "Unknown error") if image_result else "No result"
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raise ValueError(f"AI image generation failed: {error_msg}")
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except Exception as e:
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self.logger.error(f"Error generating AI image: {str(e)}")
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raise Exception(f"AI image generation failed: {str(e)}")
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async def _create_imageGenerate_prompt(self, extracted_content: Dict[str, Any], title: str, user_prompt: str = None, ai_service=None) -> str:
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"""Create a detailed prompt for AI image generation based on the content."""
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try:
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# Start with base prompt
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prompt_parts = []
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# Add user's original intent if available
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if user_prompt:
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prompt_parts.append(f"User Request: {ai_service.sanitizePromptContent(user_prompt, 'userinput')}")
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# Add document title
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prompt_parts.append(f"Document Title: {title}")
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# Analyze content and create visual description
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sections = extracted_content.get("sections", [])
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content_description = self._analyze_content_for_visual_description(sections)
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if content_description:
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prompt_parts.append(f"Content to Visualize: {content_description}")
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# Add style guidance
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style_guidance = self._get_style_guidance_from_content(extracted_content, user_prompt)
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if style_guidance:
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prompt_parts.append(f"Visual Style: {style_guidance}")
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# Combine all parts
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full_prompt = "Create a professional, informative image that visualizes the following content:\n\n" + "\n\n".join(prompt_parts)
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# Add technical requirements
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full_prompt += "\n\nTechnical Requirements:"
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full_prompt += "\n- High quality, professional appearance"
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full_prompt += "\n- Clear, readable text if any text is included"
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full_prompt += "\n- Appropriate colors and layout"
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full_prompt += "\n- Suitable for business/professional use"
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# Truncate prompt if it exceeds DALL-E's 4000 character limit
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if len(full_prompt) > 4000:
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# Use AI to compress the prompt intelligently
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compressed_prompt = await self._compress_prompt_with_ai(full_prompt, ai_service)
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if compressed_prompt and len(compressed_prompt) <= 4000:
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return compressed_prompt
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# Fallback to minimal prompt if AI compression fails or is still too long
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minimal_prompt = f"Create a professional image representing: {title}"
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if user_prompt:
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minimal_prompt += f" - {ai_service.sanitizePromptContent(user_prompt, 'userinput')}"
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# If even the minimal prompt is too long, truncate it
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if len(minimal_prompt) > 4000:
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minimal_prompt = minimal_prompt[:3997] + "..."
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return minimal_prompt
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return full_prompt
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except Exception as e:
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self.logger.warning(f"Error creating image prompt: {str(e)}")
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# Fallback to simple prompt
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return f"Create a professional image representing: {title}"
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async def _compress_prompt_with_ai(self, long_prompt: str, ai_service=None) -> str:
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"""Use AI to intelligently compress a long prompt while preserving key information."""
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try:
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if not ai_service:
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return None
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compression_prompt = f"""
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You are an expert at creating concise, effective prompts for AI image generation.
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The following prompt is too long for DALL-E (4000 character limit) and needs to be compressed to under 4000 characters while preserving the most important visual information.
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Original prompt ({len(long_prompt)} characters):
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{long_prompt}
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Please create a compressed version that:
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1. Keeps the most important visual elements and requirements
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2. Maintains the core intent and style guidance
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3. Preserves technical requirements
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4. Stays under 4000 characters
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5. Is optimized for DALL-E image generation
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Return only the compressed prompt, no explanations.
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"""
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# Use AI to compress the prompt - call the AI service correctly
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# The ai_service has an aiObjects attribute that contains the actual AI interface
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from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationTypeEnum
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request = AiCallRequest(
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prompt=compression_prompt,
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options=AiCallOptions(
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operationType=OperationTypeEnum.DATA_GENERATE,
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maxTokens=None, # Let the model use its full context length
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temperature=0.3 # Lower temperature for more consistent compression
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)
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)
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response = await ai_service.aiObjects.call(request)
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compressed = response.content.strip()
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# Validate the compressed prompt
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if compressed and len(compressed) <= 4000 and len(compressed) > 50:
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self.logger.info(f"Successfully compressed prompt from {len(long_prompt)} to {len(compressed)} characters")
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return compressed
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else:
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self.logger.warning(f"AI compression failed or produced invalid result: {len(compressed) if compressed else 0} chars")
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return None
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except Exception as e:
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self.logger.warning(f"Error compressing prompt with AI: {str(e)}")
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return None
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def _analyze_content_for_visual_description(self, sections: List[Dict[str, Any]]) -> str:
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"""Analyze content sections and create a visual description for AI."""
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try:
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descriptions = []
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for section in sections:
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section_type = self._get_section_type(section)
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section_data = self._get_section_data(section)
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if section_type == "table":
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headers = section_data.get("headers", [])
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rows = section_data.get("rows", [])
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if headers and rows:
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descriptions.append(f"Data table with {len(headers)} columns and {len(rows)} rows: {', '.join(headers)}")
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elif section_type == "bullet_list":
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items = section_data.get("items", [])
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if items:
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descriptions.append(f"List with {len(items)} items")
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elif section_type == "heading":
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text = section_data.get("text", "")
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level = section_data.get("level", 1)
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if text:
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descriptions.append(f"Heading {level}: {text}")
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elif section_type == "paragraph":
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text = section_data.get("text", "")
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if text and len(text) > 10: # Only include substantial paragraphs
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# Truncate long text
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truncated = text[:100] + "..." if len(text) > 100 else text
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descriptions.append(f"Text content: {truncated}")
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elif section_type == "code_block":
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code = section_data.get("code", "")
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language = section_data.get("language", "")
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if code:
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descriptions.append(f"Code block ({language}): {code[:50]}...")
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return "; ".join(descriptions) if descriptions else "General document content"
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except Exception as e:
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self.logger.warning(f"Error analyzing content: {str(e)}")
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return "Document content"
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def _get_style_guidance_from_content(self, extracted_content: Dict[str, Any], user_prompt: str = None) -> str:
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"""Determine visual style guidance based on content and user prompt."""
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try:
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style_elements = []
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# Analyze user prompt for style hints
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if user_prompt:
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prompt_lower = user_prompt.lower()
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if any(word in prompt_lower for word in ["modern", "contemporary", "sleek"]):
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style_elements.append("modern, clean design")
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elif any(word in prompt_lower for word in ["classic", "traditional", "formal"]):
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style_elements.append("classic, formal design")
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elif any(word in prompt_lower for word in ["creative", "artistic", "colorful"]):
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style_elements.append("creative, artistic design")
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elif any(word in prompt_lower for word in ["corporate", "business", "professional"]):
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style_elements.append("corporate, professional design")
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# Analyze content type for additional style hints
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sections = extracted_content.get("sections", [])
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has_tables = any(self._get_section_type(s) == "table" for s in sections)
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has_lists = any(self._get_section_type(s) == "bullet_list" for s in sections)
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has_code = any(self._get_section_type(s) == "code_block" for s in sections)
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if has_tables:
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style_elements.append("data-focused layout")
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if has_lists:
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style_elements.append("organized, structured presentation")
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if has_code:
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style_elements.append("technical, developer-friendly")
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# Default style if no specific guidance
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if not style_elements:
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style_elements.append("professional, clean design")
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return ", ".join(style_elements)
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except Exception as e:
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self.logger.warning(f"Error determining style guidance: {str(e)}")
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return "professional design"
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