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| #!/usr/bin/env python3 | |
| """ | |
| Improved ASCII to High-Resolution Image Converter | |
| Optimized for academic conferences (NeurIPS) with fallback font support | |
| """ | |
| from PIL import Image, ImageDraw, ImageFont | |
| import os | |
| from pathlib import Path | |
| def create_ascii_diagram(ascii_text: str, output_path: str = "oncall_ai_flowchart.png") -> bool: | |
| """ | |
| Convert ASCII diagram to high-resolution image with academic quality | |
| Args: | |
| ascii_text: ASCII art text content | |
| output_path: Output PNG file path | |
| Returns: | |
| Boolean indicating success | |
| """ | |
| # Font selection with fallback options | |
| font_paths = [ | |
| "/System/Library/Fonts/SFNSMono.ttf", # macOS Big Sur+ | |
| "/System/Library/Fonts/Monaco.ttf", # macOS fallback | |
| "/System/Library/Fonts/Menlo.ttf", # macOS alternative | |
| "/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf", # Linux | |
| "C:/Windows/Fonts/consola.ttf", # Windows | |
| None # PIL default font fallback | |
| ] | |
| font = None | |
| font_size = 14 # Slightly smaller for better readability | |
| # Try fonts in order of preference | |
| for font_path in font_paths: | |
| try: | |
| if font_path is None: | |
| font = ImageFont.load_default() | |
| print("🔤 Using PIL default font") | |
| break | |
| elif os.path.exists(font_path): | |
| font = ImageFont.truetype(font_path, font_size) | |
| print(f"✅ Using font: {font_path}") | |
| break | |
| except Exception as e: | |
| print(f"⚠️ Font loading failed: {font_path} - {e}") | |
| continue | |
| if font is None: | |
| print("❌ No suitable font found") | |
| return False | |
| # Process text lines | |
| lines = ascii_text.strip().split("\n") | |
| lines = [line.rstrip() for line in lines] # Remove trailing whitespace | |
| # Calculate dimensions using modern PIL methods | |
| try: | |
| # Modern Pillow 10.0+ method | |
| line_metrics = [font.getbbox(line) for line in lines] | |
| max_width = max([metrics[2] - metrics[0] for metrics in line_metrics]) | |
| line_height = max([metrics[3] - metrics[1] for metrics in line_metrics]) | |
| except AttributeError: | |
| # Fallback for older Pillow versions | |
| try: | |
| line_sizes = [font.getsize(line) for line in lines] | |
| max_width = max([size[0] for size in line_sizes]) | |
| line_height = max([size[1] for size in line_sizes]) | |
| except AttributeError: | |
| # Ultimate fallback | |
| max_width = len(max(lines, key=len)) * font_size * 0.6 | |
| line_height = font_size * 1.2 | |
| # Image dimensions with padding | |
| padding = 40 | |
| img_width = int(max_width + padding * 2) | |
| img_height = int(line_height * len(lines) + padding * 2) | |
| print(f"📐 Image dimensions: {img_width} x {img_height}") | |
| print(f"📏 Max line width: {max_width}, Line height: {line_height}") | |
| # Create high-resolution image | |
| img = Image.new("RGB", (img_width, img_height), "white") | |
| draw = ImageDraw.Draw(img) | |
| # Draw text lines | |
| for i, line in enumerate(lines): | |
| y_pos = padding + i * line_height | |
| draw.text((padding, y_pos), line, font=font, fill="black") | |
| # Save with high DPI for academic use | |
| try: | |
| img.save(output_path, dpi=(300, 300), optimize=True) | |
| print(f"✅ High-resolution diagram saved: {output_path}") | |
| print(f"📊 Image size: {img_width}x{img_height} at 300 DPI") | |
| return True | |
| except Exception as e: | |
| print(f"❌ Failed to save image: {e}") | |
| return False | |
| # Example usage with your OnCall.ai flowchart | |
| if __name__ == "__main__": | |
| # Your OnCall.ai ASCII flowchart | |
| oncall_ascii = """ | |
| +---------------------------------------------------+-------------------------------------------------------------+ | |
| | User Input | 1. STEP 1: Condition Extraction | | |
| | ↓ | - Processes user input through 5-level fallback | | |
| | STEP 1: Condition Extraction (5-level fallback) | - Extracts medical conditions and keywords | | |
| | ↓ | - Handles complex symptom descriptions & terminology | | |
| | STEP 2: System Understanding Display (Transparent)|-------------------------------------------------------------| | |
| | ↓ | 2. STEP 2: System Understanding Display | | |
| | STEP 3: Medical Guidelines Retrieval | - Shows transparent interpretation of user query | | |
| | ↓ | - No user interaction required | | |
| | STEP 4: Evidence-based Advice Generation | - Builds confidence in system understanding | | |
| | ↓ |-------------------------------------------------------------| | |
| | STEP 5: Performance Summary & Technical Details | 3. STEP 3: Medical Guidelines Retrieval | | |
| | ↓ | - Searches dual-index system (emergency + treatment) | | |
| | Multi-format Output | - Returns 8-9 relevant guidelines per query | | |
| | (Advice + Guidelines + Metrics) | - Maintains emergency/treatment balance | | |
| | |-------------------------------------------------------------| | |
| | | 4. STEP 4: Evidence-based Advice Generation | | |
| | | - Uses RAG-based prompt construction | | |
| | | - Integrates specialized medical LLM (Med42-70B) | | |
| | | - Generates clinically appropriate guidance | | |
| | |-------------------------------------------------------------| | |
| | | 5. STEP 5: Performance Summary | | |
| | | - Aggregates timing and confidence metrics | | |
| | | - Provides technical metadata for transparency | | |
| | | - Enables system performance monitoring | | |
| +---------------------------------------------------+-------------------------------------------------------------+ | |
| | General Pipeline 5 steps Mechanism Overview | | |
| """ | |
| # Execute conversion | |
| success = create_ascii_diagram(oncall_ascii, "5level_general_pipeline.png") | |
| if success: | |
| print("\n🎉 Ready for NeurIPS presentation!") | |
| print("💡 You can now insert this high-quality diagram into your paper or poster") | |
| else: | |
| print("\n❌ Conversion failed - check font availability") |