from fastapi import FastAPI, File, UploadFile, Form, HTTPException from fastapi.responses import FileResponse, HTMLResponse from fastapi.middleware.cors import CORSMiddleware from fastapi import BackgroundTasks import os import tempfile import re import json from pathlib import Path # Import your conversion function from meta.py from meta import process_excel_to_word app = FastAPI(title="QCM Converter API - META") # Enable CORS for all origins (you can restrict this in production) app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) def validate_hex_color(color: str) -> bool: """Validate hex color format""" pattern = r'^[0-9A-Fa-f]{6}$' return bool(re.match(pattern, color)) @app.get("/", response_class=HTMLResponse) async def root(): """Serve the HTML interface""" html_path = Path(__file__).parent / "index.html" if html_path.exists(): return html_path.read_text() return """

QCM Converter API - META

META Version: Answer tables only at the end of each module

Upload your Excel files at /docs

""" @app.post("/convert") async def convert_file( background_tasks: BackgroundTasks, file: UploadFile = File(...), images: UploadFile = File(None), # Optional ZIP file with images use_two_columns: bool = Form(True), add_separator_line: bool = Form(True), theme_color: str = Form("5FFFDF"), highlight_words: str = Form(None) # JSON string of words to highlight ): """ Convert Excel QCM file to Word document (META version) META Version Features: - NO empty tables after each course - ONLY answer tables at the end of each module Parameters: - file: Excel file (.xlsx) - images: Optional ZIP file containing images - use_two_columns: Use two-column layout - add_separator_line: Add separator line between columns - theme_color: Hex color code (without #) e.g., "5FFFDF" - highlight_words: JSON array of words to highlight (e.g., '["word1", "word2"]') """ # Validate file extension if not file.filename.endswith('.xlsx'): raise HTTPException(status_code=400, detail="Only .xlsx files are supported") # Validate color if not validate_hex_color(theme_color): raise HTTPException( status_code=400, detail="Invalid color format. Use 6-character hex code (e.g., '5FFFDF')" ) original_name = Path(file.filename).stem temp_dir = tempfile.mkdtemp() temp_input_path = os.path.join(temp_dir, f"{original_name}.xlsx") # Save the Excel file with open(temp_input_path, "wb") as f: f.write(await file.read()) # Handle optional image ZIP file temp_images_path = None if images and images.filename: if not images.filename.endswith('.zip'): cleanup_files(temp_input_path) raise HTTPException(status_code=400, detail="Images must be in a ZIP file") temp_images_path = os.path.join(temp_dir, "images.zip") with open(temp_images_path, "wb") as f: f.write(await images.read()) output_filename = file.filename.replace('.xlsx', '_converted.docx') temp_output_path = tempfile.mktemp(suffix='.docx') # Parse highlight words from JSON string highlight_words_list = [] if highlight_words: try: highlight_words_list = json.loads(highlight_words) if not isinstance(highlight_words_list, list): highlight_words_list = [] except json.JSONDecodeError: # If it's not valid JSON, treat it as empty list highlight_words_list = [] try: process_excel_to_word( excel_file_path=temp_input_path, output_word_path=temp_output_path, image_folder=temp_images_path, # Can be None display_name=None, use_two_columns=use_two_columns, add_separator_line=add_separator_line, balance_method="dynamic", theme_hex=theme_color, highlight_words=highlight_words_list ) # Schedule cleanup as a background task files_to_cleanup = [temp_input_path, temp_output_path] if temp_images_path: files_to_cleanup.append(temp_images_path) background_tasks.add_task(cleanup_files, *files_to_cleanup) return FileResponse( temp_output_path, media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document", filename=output_filename, background=None ) except Exception as e: files_to_cleanup = [temp_input_path, temp_output_path] if temp_images_path: files_to_cleanup.append(temp_images_path) cleanup_files(*files_to_cleanup) raise HTTPException(status_code=500, detail=f"Conversion failed: {str(e)}") def cleanup_files(*file_paths): """Clean up temporary files""" for file_path in file_paths: try: if os.path.exists(file_path): os.unlink(file_path) except Exception as e: print(f"Error cleaning up {file_path}: {e}") @app.get("/health") async def health_check(): """Health check endpoint""" return {"status": "healthy", "message": "QCM Converter API - META is running"} if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=7860)