ALwrity Version 0.5.1 (Fastapi + React)
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ajaysi
317
ToBeMigrated/ai_writers/ai_outline_writer/get_blog_outline.py
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317
ToBeMigrated/ai_writers/ai_outline_writer/get_blog_outline.py
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"""
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Enhanced Blog Outline Generator
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This module provides a sophisticated outline generation system that creates detailed,
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well-structured outlines for blog posts based on user preferences and content requirements.
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"""
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import sys
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from typing import Dict, List, Optional
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from enum import Enum
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from dataclasses import dataclass
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from loguru import logger
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import json
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from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
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from lib.gpt_providers.text_to_image_generation.main_generate_image_from_prompt import generate_image
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logger.remove()
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logger.add(sys.stdout,
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colorize=True,
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format="<level>{level}</level>|<green>{file}:{line}:{function}</green>| {message}")
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class ContentType(Enum):
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"""Types of content that can be generated."""
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HOW_TO = "how-to"
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TUTORIAL = "tutorial"
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LISTICLE = "listicle"
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COMPARISON = "comparison"
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CASE_STUDY = "case-study"
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OPINION = "opinion"
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NEWS = "news"
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REVIEW = "review"
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GUIDE = "guide"
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class ContentDepth(Enum):
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"""Depth levels for content coverage."""
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BASIC = "basic"
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INTERMEDIATE = "intermediate"
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ADVANCED = "advanced"
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EXPERT = "expert"
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class OutlineStyle(Enum):
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"""Styles for outline structure."""
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TRADITIONAL = "traditional"
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MODERN = "modern"
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CONVERSATIONAL = "conversational"
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ACADEMIC = "academic"
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SEO_OPTIMIZED = "seo-optimized"
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@dataclass
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class OutlineConfig:
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"""Configuration for outline generation."""
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content_type: ContentType = ContentType.GUIDE
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content_depth: ContentDepth = ContentDepth.INTERMEDIATE
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outline_style: OutlineStyle = OutlineStyle.MODERN
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target_word_count: int = 2000
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num_main_sections: int = 5
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num_subsections_per_section: int = 3
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include_introduction: bool = True
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include_conclusion: bool = True
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include_faqs: bool = True
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include_resources: bool = True
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target_audience: str = "general"
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language: str = "English"
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keywords: List[str] = None
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exclude_topics: List[str] = None
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include_images: bool = True
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image_style: str = "realistic"
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image_engine: str = "Gemini-AI"
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@dataclass
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class SectionContent:
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"""Content for a section including text and image."""
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title: str
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content: str
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image_prompt: Optional[str] = None
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image_path: Optional[str] = None
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class BlogOutlineGenerator:
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"""Enhanced blog outline generator with comprehensive controls."""
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def __init__(self, config: Optional[OutlineConfig] = None):
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"""Initialize the outline generator with optional configuration."""
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self.config = config or OutlineConfig()
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self.outline = {}
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self.section_contents = {}
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def generate_outline(self, topic: str) -> Dict[str, List[str]]:
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"""Generate a blog outline based on the topic and configuration."""
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try:
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# Create a focused prompt for outline generation
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prompt = f"""Generate a blog outline for topic: {topic}
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Content Type: {self.config.content_type.value}
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Target Audience: {self.config.target_audience}
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Content Depth: {self.config.content_depth.value}
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Style: {self.config.outline_style.value}
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Word Count Target: {self.config.target_word_count}
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Main Sections: {self.config.num_main_sections}
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Subsections per Section: {self.config.num_subsections_per_section}
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Requirements:
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- Create exactly {self.config.num_main_sections} main sections
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- Each section should have exactly {self.config.num_subsections_per_section} subsections
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- Focus on {self.config.content_type.value} content style
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- Target {self.config.target_audience} audience
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- Maintain {self.config.content_depth.value} depth
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- Follow {self.config.outline_style.value} style
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- Optimize for {self.config.target_word_count} words total
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IMPORTANT: You must return a valid JSON object with main sections as keys and lists of subsections as values.
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Example format: {{"Section 1": ["Subsection 1.1", "Subsection 1.2"], "Section 2": ["Subsection 2.1", "Subsection 2.2"]}}
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Do not include any additional text or explanations, only the JSON object."""
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# Get outline from LLM
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outline_json = llm_text_gen(prompt)
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# Clean the response to ensure it's valid JSON
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outline_json = outline_json.strip()
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if not outline_json.startswith('{'):
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outline_json = outline_json[outline_json.find('{'):]
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if not outline_json.endswith('}'):
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outline_json = outline_json[:outline_json.rfind('}')+1]
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# Parse the outline
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try:
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outline = json.loads(outline_json)
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except json.JSONDecodeError as e:
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logger.error(f"JSON parsing error: {str(e)}")
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logger.error(f"Raw response: {outline_json}")
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# Fallback to a basic outline structure
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outline = {
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f"Section {i+1}": [f"Subsection {i+1}.{j+1}" for j in range(self.config.num_subsections_per_section)]
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for i in range(self.config.num_main_sections)
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}
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# Add introduction and conclusion if configured
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if self.config.include_introduction:
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outline = {"Introduction": ["Overview", "Importance", "What to Expect"]} | outline
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if self.config.include_conclusion:
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outline["Conclusion"] = ["Summary", "Key Takeaways", "Next Steps"]
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# Add FAQs if configured
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if self.config.include_faqs:
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# Generate topic-specific FAQs
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faq_prompt = f"""Generate 3 specific and relevant FAQ questions for a blog post about: {topic}
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Content Type: {self.config.content_type.value}
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Target Audience: {self.config.target_audience}
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Content Depth: {self.config.content_depth.value}
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Requirements:
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- Questions should be specific to the topic
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- Cover common concerns and important aspects
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- Be relevant to the target audience
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- Include both basic and advanced questions
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Format: Return only a JSON array of 3 questions.
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Example format: ["Question 1?", "Question 2?", "Question 3?"]"""
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try:
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faq_json = llm_text_gen(faq_prompt)
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faq_json = faq_json.strip()
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if not faq_json.startswith('['):
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faq_json = faq_json[faq_json.find('['):]
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if not faq_json.endswith(']'):
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faq_json = faq_json[:faq_json.rfind(']')+1]
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faqs = json.loads(faq_json)
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outline["Frequently Asked Questions"] = faqs
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except Exception as e:
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logger.error(f"Error generating FAQs: {str(e)}")
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outline["Frequently Asked Questions"] = [
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f"Common Question about {topic} 1",
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f"Common Question about {topic} 2",
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f"Common Question about {topic} 3"
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]
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# Add resources if configured
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if self.config.include_resources:
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outline["Additional Resources"] = [
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"Further Reading",
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"Tools and References",
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"Related Topics"
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]
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return outline
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except Exception as e:
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logger.error(f"Error generating outline: {str(e)}")
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return {}
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def generate_section_content(self, section: str, subsections: List[str]) -> Optional[SectionContent]:
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"""Generate content for a section."""
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try:
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# Create a focused prompt for content generation
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prompt = f"""Generate content for section: {section}
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Subsections: {', '.join(subsections)}
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Content Type: {self.config.content_type.value}
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Target Audience: {self.config.target_audience}
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Content Depth: {self.config.content_depth.value}
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Style: {self.config.outline_style.value}
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Word Count Target: {self.config.target_word_count // self.config.num_main_sections}
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Requirements:
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- Write content for each subsection
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- Maintain {self.config.content_depth.value} depth
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- Target {self.config.target_audience} audience
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- Follow {self.config.outline_style.value} style
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- Optimize for {self.config.target_word_count // self.config.num_main_sections} words
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- Include relevant examples and data points
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- Use clear, engaging language
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Format: Return only a JSON object with 'content' and 'image_prompt' fields.
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Example format: {{"content": "Section content here...", "image_prompt": "Image description here..."}}"""
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# Get content from LLM
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content_json = llm_text_gen(prompt)
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content_data = json.loads(content_json)
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# Generate image if configured
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image_path = None
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if self.config.include_images:
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image_path = self.generate_section_image(section)
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return SectionContent(
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title=section,
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content=content_data["content"],
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image_prompt=content_data.get("image_prompt"),
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image_path=image_path
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)
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except Exception as e:
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logger.error(f"Error generating content for section {section}: {str(e)}")
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return None
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def generate_section_image(self, section: str) -> Optional[str]:
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"""Generate an image for a section."""
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try:
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# Create a focused prompt for image generation
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prompt = f"""Generate an image prompt for section: {section}
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Style: {self.config.image_style}
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Engine: {self.config.image_engine}
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Content Type: {self.config.content_type.value}
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Target Audience: {self.config.target_audience}
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Requirements:
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- Create a {self.config.image_style} style image
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- Optimize for {self.config.image_engine} engine
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- Match {self.config.content_type.value} content type
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- Appeal to {self.config.target_audience} audience
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- Be visually engaging and relevant
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Format: Return only a JSON object with an 'image_prompt' field.
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Example format: {{"image_prompt": "Detailed image description here..."}}"""
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# Get image prompt from LLM
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prompt_json = llm_text_gen(prompt)
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prompt_data = json.loads(prompt_json)
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# Generate image using the specified engine
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if self.config.image_engine == "Gemini-AI":
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image_path = generate_gemini_image(prompt_data["image_prompt"])
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elif self.config.image_engine == "Dalle3":
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image_path = generate_dalle_image(prompt_data["image_prompt"])
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else: # Stability-AI
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image_path = generate_stability_image(prompt_data["image_prompt"])
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return image_path
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except Exception as e:
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logger.error(f"Error generating image for section {section}: {str(e)}")
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return None
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def to_markdown(self) -> str:
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"""Convert outline to markdown format with content and images."""
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markdown = f"# {self.outline.get('Introduction', [''])[0]}\n\n"
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for section, subsections in self.outline.items():
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if section not in ["Introduction", "Conclusion", "FAQs", "Additional Resources"]:
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markdown += f"## {section}\n\n"
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# Add section content if available
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if section in self.section_contents:
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content = self.section_contents[section]
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markdown += f"{content.content}\n\n"
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# Add image if available
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if content.image_path:
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markdown += f"\n\n"
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# Add subsections
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for subsection in subsections:
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markdown += f"- {subsection}\n"
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markdown += "\n"
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if "Conclusion" in self.outline:
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markdown += "## Conclusion\n\n"
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for subsection in self.outline["Conclusion"]:
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markdown += f"- {subsection}\n"
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markdown += "\n"
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if "FAQs" in self.outline:
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markdown += "## Frequently Asked Questions\n\n"
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for faq in self.outline["FAQs"]:
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markdown += f"- {faq}\n"
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markdown += "\n"
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if "Additional Resources" in self.outline:
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markdown += "## Additional Resources\n\n"
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for resource in self.outline["Additional Resources"]:
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markdown += f"- {resource}\n"
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return markdown
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