#!/usr/bin/env python3 """ RateRight Construction Photo Analyzer Analyze construction site photos to identify trade types, safety gear, equipment, and conditions. Generates marketing insights and post caption suggestions. Usage: python photo-analyzer.py /path/to/photo.jpg Example: python photo-analyzer.py ./site-photos/formwork-crew.jpg python photo-analyzer.py https://example.com/construction-photo.jpg Output: - Trade type identification - Safety equipment assessment - Equipment and tools identified - Site conditions evaluation - Marketing insights (e.g., "steel fixing crew needed") - Social media caption suggestions """ import sys import json import logging from pathlib import Path import base64 import requests try: import openai except ImportError: print("OpenAI library not found. Installing...") import subprocess subprocess.check_call([sys.executable, "-m", "pip", "install", "openai"]) import openai # Setup logging log_dir = Path("/home/ccuser/rateright-growth/rivet/memory") log_dir.mkdir(exist_ok=True) logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler(log_dir / "content-engine.log"), logging.StreamHandler() ] ) logger = logging.getLogger(__name__) def load_openai_key(): """Load OpenAI API key from secrets file.""" try: secrets_path = "/root/.clawdbot/secrets.json" with open(secrets_path, 'r') as f: secrets = json.load(f) return secrets.get("openai_api_key") or secrets.get("OPENAI_API_KEY") except Exception as e: logger.error(f"Failed to load OpenAI key: {e}") return None def encode_image(image_path): """Encode image to base64 for API processing.""" try: if image_path.startswith(('http://', 'https://')): # Download image from URL response = requests.get(image_path, timeout=30) response.raise_for_status() return base64.b64encode(response.content).decode('utf-8') else: # Read local image file with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') except Exception as e: logger.error(f"Failed to encode image: {e}") return None def analyze_construction_photo(image_path): """Analyze construction photo using OpenAI Vision API.""" api_key = load_openai_key() if not api_key: logger.error("OpenAI API key not found") return None client = openai.OpenAI(api_key=api_key) # Encode image base64_image = encode_image(image_path) if not base64_image: return None system_prompt = """You are a construction industry expert analyzing site photos for RateRight, an Australian construction hiring marketplace. Provide detailed analysis for marketing purposes. Focus on identifying: 1. Trade types and specializations visible 2. Safety equipment and compliance 3. Equipment, tools, and machinery 4. Site conditions and project phase 5. Workforce size and composition 6. Quality standards and workmanship level Generate marketable insights about hiring needs and workforce requirements.""" user_prompt = """Analyze this construction site photo and provide: 1. TRADE IDENTIFICATION - Primary trade(s) visible - Specialization level (apprentice, journeyman, expert) - Number of workers visible 2. SAFETY ASSESSMENT - PPE compliance (hard hats, hi-vis, steel caps, etc.) - Safety equipment present - Any safety concerns visible 3. EQUIPMENT & TOOLS - Major equipment visible - Hand tools and specialized gear - Equipment quality/condition 4. SITE CONDITIONS - Project type (residential, commercial, infrastructure) - Construction phase - Site organization and cleanliness - Weather conditions if apparent 5. MARKETABLE INSIGHTS - Hiring needs this photo suggests - Skill gaps or shortages evident - Equipment/supply needs - Project timeline implications 6. SOCIAL MEDIA CAPTIONS - 3 LinkedIn caption suggestions - 2 Instagram caption suggestions - 1 TikTok caption idea - Include relevant hashtags and emojis where appropriate Format response with clear sections and bullet points. Be specific and actionable for marketing purposes.""" try: response = client.chat.completions.create( model="gpt-4-vision-preview", messages=[ {"role": "system", "content": system_prompt}, { "role": "user", "content": [ {"type": "text", "text": user_prompt}, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{base64_image}" } } ] } ], max_tokens=1500 ) content = response.choices[0].message.content logger.info(f"Successfully analyzed construction photo: {image_path}") return content except Exception as e: logger.error(f"OpenAI Vision API error: {e}") return None def format_output(content, image_path): """Format the analysis for display.""" from datetime import datetime timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") output = f"""# RateRight Construction Photo Analyzer Analyzed on: {timestamp} Image: {image_path} {'='*60} {content} {'='*60} """ return output def main(): """Main function to handle command line execution.""" if len(sys.argv) < 2: print("Usage: python photo-analyzer.py /path/to/photo.jpg") print("Example: python photo-analyzer.py ./site-photos/formwork-crew.jpg") print(" python photo-analyzer.py https://example.com/photo.jpg") sys.exit(1) image_path = sys.argv[1] logger.info(f"Starting photo analysis for: {image_path}") # Validate image path if not image_path.startswith(('http://', 'https://')): if not Path(image_path).exists(): print(f"Error: Image file not found: {image_path}") logger.error(f"Image file not found: {image_path}") sys.exit(1) # Analyze photo content = analyze_construction_photo(image_path) if content: formatted_output = format_output(content, image_path) print(formatted_output) logger.info("Photo analysis completed successfully") else: print("Failed to analyze photo. Check logs for details.") logger.error("Photo analysis failed") sys.exit(1) if __name__ == "__main__": main()