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| import httpx | |
| import os | |
| from typing import List, Dict, Any, Optional | |
| from openai import OpenAI | |
| from app.schemas import AIContentRequest, AIContentResponse | |
| class AIService: | |
| def __init__(self): | |
| self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY", "")) | |
| self.model = os.getenv("OPENAI_MODEL", "gpt-4o") | |
| async def generate_content( | |
| self, | |
| request: AIContentRequest, | |
| assets_context: Optional[str] = None, | |
| asset_insights: Optional[List[Dict[str, Any]]] = None | |
| ) -> AIContentResponse: | |
| """Generate LinkedIn post content using GPT with agentic context from assets""" | |
| product_descriptions = { | |
| "ocr": "Intelligent Document Parsing (OCR) - AI-powered document processing and data extraction", | |
| "p2p": "Purchase To Pay (P2P) - End-to-end procurement and accounts payable automation", | |
| "o2c": "Order to Cash (O2C) - Complete order management and accounts receivable workflow" | |
| } | |
| post_type_descriptions = { | |
| "carousel": "A multi-slide carousel post with visual storytelling", | |
| "cover_content": "A post with a cover image and engaging text content", | |
| "content_only": "A text-only post focused on valuable insights", | |
| "webinar": "A webinar invitation post to promote an upcoming event" | |
| } | |
| # Build rich context from analyzed assets | |
| asset_context_text = "" | |
| if asset_insights: | |
| asset_context_text = "\n\nRelevant Asset Insights (use these to create authentic, specific content):\n" | |
| for asset in asset_insights: | |
| if asset.get("extracted_content"): | |
| from app.services.asset_analyzer import AssetAnalyzer | |
| analyzer = AssetAnalyzer() | |
| insight = analyzer.extract_key_insights(asset.get("extracted_content")) | |
| if insight: | |
| asset_context_text += f"- {asset.get('name', 'Asset')}: {insight}\n" | |
| elif assets_context: | |
| asset_context_text = f"\n\nAvailable assets: {assets_context}" | |
| system_prompt = f"""You are an expert LinkedIn content creator specializing in B2B SaaS marketing. | |
| Create engaging, professional LinkedIn posts that: | |
| - Are authentic and valuable to the audience | |
| - Use specific insights from uploaded assets when available | |
| - Include relevant hashtags (3-5 hashtags) | |
| - Use emojis sparingly and appropriately | |
| - Are optimized for engagement | |
| - Follow LinkedIn best practices | |
| Product: {product_descriptions.get(request.product_category, request.product_category)} | |
| Post Type: {post_type_descriptions.get(request.post_type, request.post_type)} | |
| """ | |
| user_prompt = f"""Create a LinkedIn post about {product_descriptions.get(request.product_category, request.product_category)}. | |
| Post type: {post_type_descriptions.get(request.post_type, request.post_type)} | |
| {f'Additional context: {request.context}' if request.context else ''} | |
| {asset_context_text} | |
| Make it engaging, professional, and include relevant hashtags at the end. | |
| If asset insights are provided, incorporate specific details from them to make the content more authentic and valuable.""" | |
| try: | |
| response = self.client.chat.completions.create( | |
| model=self.model, | |
| messages=[ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": user_prompt} | |
| ], | |
| temperature=0.7, | |
| max_tokens=1000 | |
| ) | |
| content = response.choices[0].message.content | |
| # Extract hashtags | |
| hashtags = [] | |
| lines = content.split('\n') | |
| for line in lines: | |
| if '#' in line: | |
| hashtags.extend([tag.strip() for tag in line.split() if tag.startswith('#')]) | |
| # If no hashtags found, generate some | |
| if not hashtags: | |
| hashtags = self._generate_hashtags(request.product_category) | |
| return AIContentResponse( | |
| content=content, | |
| suggested_hashtags=hashtags[:5] | |
| ) | |
| except Exception as e: | |
| # Fallback content if AI fails | |
| return AIContentResponse( | |
| content=f"🚀 Exciting news about {product_descriptions.get(request.product_category, 'our product')}!\n\nStay tuned for more updates.\n\n#Innovation #Technology", | |
| suggested_hashtags=["#Innovation", "#Technology", "#Business"] | |
| ) | |
| def _generate_hashtags(self, product_category: str) -> List[str]: | |
| """Generate relevant hashtags based on product category""" | |
| hashtag_map = { | |
| "ocr": ["#DocumentAutomation", "#OCR", "#AITechnology", "#DigitalTransformation", "#BusinessEfficiency"], | |
| "p2p": ["#Procurement", "#AccountsPayable", "#FinanceAutomation", "#BusinessProcess", "#Efficiency"], | |
| "o2c": ["#OrderManagement", "#AccountsReceivable", "#SalesAutomation", "#BusinessGrowth", "#CustomerExperience"] | |
| } | |
| return hashtag_map.get(product_category, ["#Innovation", "#Technology", "#Business"]) | |