AI Analysis and Content Strategy fixes. Enhanced Strategy Routes refactoring.
This commit is contained in:
ajaysi
@@ -31,7 +31,7 @@ logger = get_service_logger("api.images")
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class ImageGenerateRequest(BaseModel):
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prompt: str
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negative_prompt: Optional[str] = None
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provider: Optional[str] = Field(None, pattern="^(gemini|huggingface|stability)$")
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provider: Optional[str] = Field(None, pattern="^(gemini|huggingface|stability|wavespeed)$")
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model: Optional[str] = None
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width: Optional[int] = Field(default=1024, ge=64, le=2048)
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height: Optional[int] = Field(default=1024, ge=64, le=2048)
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@@ -246,7 +246,10 @@ def generate(
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# Non-blocking: log error but don't fail the request
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logger.error(f"[images.generate] ❌ Failed to track usage: {usage_error}", exc_info=True)
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return ImageGenerateResponse(
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# Create response with explicit success field
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# Note: Asset saving and usage tracking are non-blocking and won't affect this response
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response = ImageGenerateResponse(
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success=True,
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image_base64=image_b64,
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image_url=image_url,
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width=result.width,
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@@ -255,6 +258,11 @@ def generate(
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model=result.model,
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seed=result.seed,
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)
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logger.info(f"[images.generate] ✅ Returning successful response: provider={result.provider}, model={result.model}, size={len(image_b64)} chars")
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# Return response immediately - any post-processing errors won't affect the response
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return response
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except Exception as inner:
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last_error = inner
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logger.error(f"Image generation attempt {attempt+1} failed: {inner}")
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@@ -282,7 +290,9 @@ class PromptSuggestion(BaseModel):
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class ImagePromptSuggestRequest(BaseModel):
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provider: Optional[str] = Field(None, pattern="^(gemini|huggingface|stability)$")
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provider: Optional[str] = Field(None, pattern="^(gemini|huggingface|stability|wavespeed)$")
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model: Optional[str] = None # Specific model (e.g., "qwen-image", "ideogram-v3-turbo")
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image_type: Optional[str] = Field(None, pattern="^(realistic|chart|conceptual|diagram|illustration|background)$")
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title: Optional[str] = None
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section: Optional[Dict[str, Any]] = None
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research: Optional[Dict[str, Any]] = None
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@@ -315,6 +325,218 @@ class ImageEditResponse(BaseModel):
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seed: Optional[int] = None
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# Model-specific guidance for prompt optimization
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MODEL_SPECIFIC_GUIDANCE = {
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"ideogram-v3-turbo": {
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"text_overlay": {
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"guidance": "Ideogram V3 excels at rendering readable text. Use simple, bold text (max 3-5 words). Avoid complex infographics - instead create clean backgrounds with designated text areas.",
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"best_practices": [
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"Use high contrast areas (top 20% or bottom 20%) for text placement",
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"Keep text simple: headlines, statistics, or short phrases only",
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"Avoid rendering text as part of complex graphics",
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"Design with 'text overlay zones' in mind, not embedded text"
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],
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"negative_prompt_additions": "complex infographics, detailed charts with text, busy data visualizations"
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},
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"realistic": {
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"guidance": "Photorealistic generation with professional quality. Include camera settings and lighting cues.",
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"best_practices": [
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"Include camera settings: '50mm lens, f/2.8, professional photography'",
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"Specify lighting: 'natural lighting, soft shadows, rim light'",
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"Add quality descriptors: 'high quality, detailed, sharp focus'"
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]
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},
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"chart": {
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"guidance": "Simple bar charts or pie charts with minimal text. Use high contrast areas for labels.",
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"best_practices": [
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"Avoid complex infographics - use simple visual representations",
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"Design with text overlay zones, not embedded text",
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"Use abstract data visualization elements"
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],
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"warnings": ["Complex infographics are too difficult - use simple charts or conceptual representations"]
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},
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"conceptual": {
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"guidance": "Conceptual imagery with photorealistic elements. Clean compositions with text overlay areas.",
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"best_practices": [
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"Focus on visual metaphors and abstract concepts",
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"Design with text overlay zones in mind (top/bottom 30%)",
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"Use simple, clear compositions"
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]
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}
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},
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"flux-kontext-pro": {
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"text_overlay": {
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"guidance": "FLUX Kontext Pro excels at typography and text rendering with improved prompt adherence. Best for professional designs with text elements.",
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"best_practices": [
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"Excellent for images requiring clear, readable text",
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"Superior typography rendering compared to other models",
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"Improved prompt adherence for consistent results",
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"Can handle text in various styles and sizes",
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"Best for professional blog images with embedded text or typography"
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],
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"negative_prompt_additions": ""
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},
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"realistic": {
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"guidance": "Photorealistic generation with professional typography support. Include text elements naturally in the composition.",
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"best_practices": [
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"Can render text elements within realistic scenes",
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"Include typography naturally in the design",
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"Specify text style, size, and placement in prompts",
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"Use for professional designs requiring text integration"
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]
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},
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"chart": {
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"guidance": "Excellent for data visualizations with text labels. Can render simple charts with clear typography.",
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"best_practices": [
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"Can render charts with text labels effectively",
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"Use for data visualizations requiring clear typography",
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"Specify chart type and label requirements clearly",
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"Design with text integration in mind"
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],
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"warnings": ["Complex infographics may still be challenging - start with simple charts"]
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},
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"diagram": {
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"guidance": "Technical diagrams with clear text labels. Excellent typography for professional diagrams.",
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"best_practices": [
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"Can render diagrams with embedded text labels",
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"Specify text requirements clearly in prompts",
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"Use for technical illustrations requiring typography",
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"Design with text integration as a core element"
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]
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},
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"illustration": {
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"guidance": "Stylized illustrations with typography support. Professional designs with text elements.",
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"best_practices": [
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"Can integrate text naturally into illustrations",
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"Specify typography style and placement",
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"Use for professional blog illustrations with text",
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"Design with text as a design element"
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]
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},
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"conceptual": {
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"guidance": "Conceptual imagery with typography capabilities. Can include text elements naturally.",
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"best_practices": [
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"Can integrate text into conceptual designs",
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"Use for abstract concepts with text support",
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"Specify text requirements in prompts",
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"Design with typography as a visual element"
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]
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}
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},
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"qwen-image": {
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"text_overlay": {
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"guidance": "Qwen Image does NOT render readable text well. Design for text overlay areas only - never ask for text in the image itself.",
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"best_practices": [
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"Create clean backgrounds with high-contrast safe zones",
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"Design simple compositions with space for text (top/bottom 30%)",
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"Use abstract or conceptual imagery that supports text",
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"NEVER request text, words, or labels in the image"
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],
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"negative_prompt_additions": "text, words, letters, numbers, labels, captions, infographics with text"
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},
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"conceptual": {
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"guidance": "Best for abstract concepts, simple diagrams, and background imagery.",
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"best_practices": [
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"Focus on visual metaphors and abstract representations",
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"Use simple compositions with clear focal points",
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"Avoid complex details or fine textures"
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]
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},
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"chart": {
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"guidance": "Abstract representation of data - avoid actual charts. Use shapes, colors, and patterns to represent data concepts.",
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"best_practices": [
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"Create visual metaphors for data, not actual charts",
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"Use abstract patterns and shapes",
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"Design with text overlay zones for data labels"
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],
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"warnings": ["Do not request actual charts with text - use abstract representations instead"]
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},
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"background": {
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"guidance": "Perfect for background images with text overlay areas. Clean, simple compositions.",
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"best_practices": [
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"Focus on clean backgrounds with designated text zones",
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"Use simple, uncluttered compositions",
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"High contrast areas for text placement"
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]
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}
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}
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}
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def get_model_specific_guidance(model: Optional[str], image_type: Optional[str]) -> Dict[str, Any]:
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"""Get model-specific guidance based on model and image type."""
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if not model:
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return {}
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model_lower = model.lower()
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image_type_lower = (image_type or "conceptual").lower()
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# Get model guidance
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model_guidance = MODEL_SPECIFIC_GUIDANCE.get(model_lower, {})
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# Get image type specific guidance
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type_guidance = model_guidance.get(image_type_lower, model_guidance.get("text_overlay", {}))
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return type_guidance
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def extract_visual_data(section: Dict[str, Any], research: Optional[Dict[str, Any]]) -> Dict[str, Any]:
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"""Intelligently extract visual-relevant data from section and research."""
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visual_data = {
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"visual_keywords": [],
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"data_points": [],
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"concepts": [],
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"statistics": []
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}
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# Extract from section
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if section:
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# Key points that are visualizable
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key_points = section.get("key_points", []) or []
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for point in key_points[:5]:
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if isinstance(point, str):
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# Look for numbers, percentages, comparisons
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if any(char.isdigit() for char in point):
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visual_data["statistics"].append(point)
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# Look for visual concepts
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elif any(word in point.lower() for word in ["increase", "decrease", "growth", "trend", "pattern", "comparison"]):
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visual_data["data_points"].append(point)
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else:
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visual_data["concepts"].append(point)
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# Subheadings that suggest visuals
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subheadings = section.get("subheadings", []) or []
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for subhead in subheadings[:3]:
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if isinstance(subhead, str):
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visual_data["concepts"].append(subhead)
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# Keywords
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keywords = section.get("keywords", []) or []
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visual_data["visual_keywords"].extend([str(k) for k in keywords[:8] if k])
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# Extract from research
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if research:
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# Key facts that are visualizable
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key_facts = research.get("key_facts", []) or research.get("highlights", []) or []
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for fact in key_facts[:3]:
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if isinstance(fact, str):
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if any(char.isdigit() for char in fact):
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visual_data["statistics"].append(fact)
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else:
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visual_data["data_points"].append(fact)
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# Research insights
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insights = research.get("insights", []) or research.get("summary", "")
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if isinstance(insights, str) and insights:
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# Extract key phrases
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sentences = insights.split('.')[:3]
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visual_data["concepts"].extend([s.strip() for s in sentences if s.strip()])
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elif isinstance(insights, list):
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visual_data["concepts"].extend([str(i) for i in insights[:3]])
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return visual_data
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@router.post("/suggest-prompts", response_model=ImagePromptSuggestResponse)
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def suggest_prompts(
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req: ImagePromptSuggestRequest,
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@@ -322,6 +544,9 @@ def suggest_prompts(
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) -> ImagePromptSuggestResponse:
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try:
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provider = (req.provider or ("gemini" if (os.getenv("GPT_PROVIDER") or "").lower().startswith("gemini") else "huggingface")).lower()
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model = req.model or None
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image_type = req.image_type or "conceptual"
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section = req.section or {}
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title = (req.title or section.get("heading") or "").strip()
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subheads = section.get("subheadings", []) or []
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@@ -338,6 +563,9 @@ def suggest_prompts(
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audience = persona.get("audience", "content creators and digital marketers")
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industry = persona.get("industry", req.research.get("domain") if req.research else "your industry")
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tone = persona.get("tone", "professional, trustworthy")
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# Extract visual-relevant data intelligently
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visual_data = extract_visual_data(section, req.research)
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schema = {
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"type": "object",
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@@ -368,52 +596,129 @@ def suggest_prompts(
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"Return STRICT JSON matching the provided schema, no extra text."
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)
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provider_guidance = {
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# Get model-specific guidance
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model_guidance_data = get_model_specific_guidance(model, image_type)
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model_guidance_text = model_guidance_data.get("guidance", "")
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model_best_practices = model_guidance_data.get("best_practices", [])
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model_warnings = model_guidance_data.get("warnings", [])
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negative_prompt_additions = model_guidance_data.get("negative_prompt_additions", "")
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# Build provider guidance with model-specific details
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provider_guidance_base = {
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"huggingface": "Photorealistic Flux 1 Krea Dev; include camera/lighting cues (e.g., 50mm, f/2.8, rim light).",
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"gemini": "Editorial, brand-safe, crisp edges, balanced lighting; avoid artifacts.",
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"stability": "SDXL coherent details, sharp focus, cinematic contrast; readable text if present."
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"stability": "SDXL coherent details, sharp focus, cinematic contrast; readable text if present.",
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"wavespeed": "Blog-optimized imagery: focus on data visualization, infographics, clean layouts with text overlay areas, professional diagrams, charts, or conceptual illustrations. Avoid random people or poster-style images. Prefer clean backgrounds suitable for text overlays, data representations, or abstract concepts that support the blog content."
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}.get(provider, "")
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# Combine provider and model-specific guidance
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provider_guidance = provider_guidance_base
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if model_guidance_text:
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provider_guidance = f"{provider_guidance_base}\n\nMODEL-SPECIFIC GUIDANCE ({model}): {model_guidance_text}"
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if model_best_practices:
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provider_guidance += f"\nBest Practices:\n" + "\n".join([f"- {bp}" for bp in model_best_practices])
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if model_warnings:
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provider_guidance += f"\n⚠️ WARNINGS:\n" + "\n".join([f"- {w}" for w in model_warnings])
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# Build visual data summary from extracted data
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visual_summary_parts = []
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if visual_data["statistics"]:
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visual_summary_parts.append(f"Key Statistics: {', '.join(visual_data['statistics'][:3])}")
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if visual_data["data_points"]:
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visual_summary_parts.append(f"Data Points: {', '.join(visual_data['data_points'][:3])}")
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if visual_data["concepts"]:
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visual_summary_parts.append(f"Visual Concepts: {', '.join(visual_data['concepts'][:5])}")
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if visual_data["visual_keywords"]:
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visual_summary_parts.append(f"Keywords: {', '.join(visual_data['visual_keywords'][:8])}")
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visual_summary = "\n".join(visual_summary_parts) if visual_summary_parts else ""
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best_practices = (
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"Best Practices: one clear focal subject; clean, uncluttered background; rule-of-thirds or center-weighted composition; "
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"text-safe margins if overlay text is included; neutral lighting if unsure; realistic skin tones; avoid busy patterns; "
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"no brand logos or watermarks; no copyrighted characters; avoid low-res, blur, noise, banding, oversaturation, over-sharpening; "
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"ensure hands and text are coherent if present; prefer 1024px+ on shortest side for quality."
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"BLOG IMAGE BEST PRACTICES: Create images optimized for blog content, not social media posters. "
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"Focus on: data visualization elements (charts, graphs, infographics), clean layouts with designated text overlay areas, "
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"professional diagrams, conceptual illustrations, or abstract representations of the topic. "
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"Avoid: random people posing, poster-style compositions, busy social media graphics, or trying to recreate text/words as images. "
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"Instead: use clean backgrounds, simple compositions, areas reserved for text overlays, data-driven visuals, or conceptual imagery. "
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"Technical: one clear focal subject; clean, uncluttered background; text-safe margins (20% padding on all sides for overlays); "
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"neutral or professional lighting; avoid busy patterns; no brand logos or watermarks; no copyrighted characters; "
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"avoid low-res, blur, noise, banding, oversaturation, over-sharpening; prefer 1024px+ on shortest side for quality."
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)
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# Harvest a few concise facts from research if available
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facts: list[str] = []
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try:
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if req.research:
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# try common shapes used in research service
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top_stats = req.research.get("key_facts") or req.research.get("highlights") or []
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if isinstance(top_stats, list):
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facts = [str(x) for x in top_stats[:3]]
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elif isinstance(top_stats, dict):
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facts = [f"{k}: {v}" for k, v in list(top_stats.items())[:3]]
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except Exception:
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facts = []
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facts_line = ", ".join(facts) if facts else ""
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overlay_hint = "Include an on-image short title or fact if it improves communication; ensure clean, high-contrast safe area for text." if (req.include_overlay is None or req.include_overlay) else "Do not include on-image text."
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overlay_hint = (
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"IMPORTANT FOR BLOG IMAGES: Design images with text overlay areas in mind. "
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"Include space for headlines, captions, or data labels. "
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"Suggest overlay_text (short title or key statistic, <= 8 words) that would work well as a text overlay. "
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"Ensure clean, high-contrast safe areas (top 20% or bottom 20% of image) for text placement. "
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"The image should complement text, not replace it - think data visualization, infographics, or clean conceptual imagery."
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if (req.include_overlay is None or req.include_overlay)
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else "Do not include on-image text, but still design with text overlay areas in mind for blog use."
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)
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# Image type specific guidance
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image_type_guidance = {
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"realistic": "Photorealistic style with professional photography quality. Include camera settings and lighting details.",
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"chart": "⚠️ IMPORTANT: Complex infographics are too difficult for current AI models. Create simple visual representations with designated text overlay areas instead. Use abstract data visualization elements, not actual charts with embedded text.",
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"conceptual": "Abstract or conceptual imagery that represents the topic visually. Clean compositions with text overlay zones.",
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"diagram": "Technical diagrams with simple, clear visual elements. Design for text overlay areas, not embedded labels.",
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"illustration": "Stylized illustrations that support the content. Professional, clean aesthetic suitable for blog use.",
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"background": "Background images optimized for text overlays. Clean, uncluttered compositions with high-contrast text zones."
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}.get(image_type, "General blog image guidance.")
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# Build comprehensive prompt with visual data and model-specific guidance
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prompt = f"""
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Provider: {provider}
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Model: {model or 'auto-selected'}
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Image Type: {image_type}
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Title: {title}
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Subheadings: {', '.join(subheads[:5])}
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Key Points: {', '.join(key_points[:5])}
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Keywords: {', '.join([str(k) for k in keywords[:8]])}
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Research Facts: {facts_line}
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VISUAL DATA EXTRACTED FROM CONTENT:
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{visual_summary if visual_summary else f"Subheadings: {', '.join(subheads[:5])}\nKey Points: {', '.join(key_points[:5])}\nKeywords: {', '.join([str(k) for k in keywords[:8]])}"}
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CONTEXT:
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Audience: {audience}
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Industry: {industry}
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Tone: {tone}
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Craft prompts that visually reflect this exact section (not generic blog topic). {provider_guidance}
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BLOG IMAGE GENERATION TASK: Create image prompts optimized for blog content, NOT social media posters.
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PROVIDER & MODEL GUIDANCE:
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{provider_guidance}
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IMAGE TYPE GUIDANCE:
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||||
{image_type_guidance}
|
||||
|
||||
BEST PRACTICES:
|
||||
{best_practices}
|
||||
|
||||
TEXT OVERLAY GUIDANCE:
|
||||
{overlay_hint}
|
||||
Include a suitable negative_prompt where helpful. Suggest width/height when relevant (e.g., 1024x1024 or 1920x1080).
|
||||
If including on-image text, return it in overlay_text (short: <= 8 words).
|
||||
|
||||
PROMPT GENERATION INSTRUCTIONS:
|
||||
Generate 3-5 diverse, well-formed prompt variations that:
|
||||
1. Intelligently use the visual data provided above (statistics, data points, concepts, keywords)
|
||||
2. Focus on the most visually-relevant elements from the section subheadings, key points, and research
|
||||
3. Create prompts that are optimized for the selected image type ({image_type})
|
||||
4. Follow model-specific best practices and avoid model limitations
|
||||
5. Include clean backgrounds suitable for text overlays
|
||||
6. Avoid random people, poster compositions, or trying to render text as images
|
||||
7. Support the blog section's content with relevant visual metaphors or data representations
|
||||
8. Are optimized for blog article use (not social media)
|
||||
|
||||
PROMPT QUALITY REQUIREMENTS:
|
||||
- Each prompt should be specific and detailed (50-100 words)
|
||||
- Use the visual data intelligently - prioritize statistics and data points for charts, concepts for conceptual images
|
||||
- Include visual composition guidance (layout, colors, style)
|
||||
- Specify lighting and quality descriptors when appropriate
|
||||
- Make prompts actionable and clear for the AI model
|
||||
|
||||
NEGATIVE PROMPT:
|
||||
Include a suitable negative_prompt that excludes: people posing, social media graphics, posters, text rendered as images, busy compositions, watermarks, logos{f", {negative_prompt_additions}" if negative_prompt_additions else ""}.
|
||||
|
||||
DIMENSIONS:
|
||||
Suggest width/height when relevant (e.g., 1024x1024 for square, 1920x1080 for landscape blog headers).
|
||||
|
||||
OVERLAY TEXT:
|
||||
If including overlay text suggestion, return it in overlay_text (short: <= 8 words, typically a key statistic or section title). Use statistics from the visual data when available.
|
||||
"""
|
||||
|
||||
# Get user_id for llm_text_gen subscription check (required)
|
||||
|
||||
Reference in New Issue
Block a user