import os import logging from pathlib import Path # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # This is a placeholder for actual Hugging Face model integration # In a real implementation, you would import and use appropriate models def process_image(file_path, analysis_type): """ Process an image using Hugging Face models. Args: file_path (str): Path to the uploaded image analysis_type (str): Type of analysis to perform (caption, etc.) Returns: str: Result of image analysis """ logger.info(f"Processing image: {file_path} with analysis type: {analysis_type}") # For demonstration purposes, return a mock caption # In a real implementation, you would use a Hugging Face model here if analysis_type == "caption": # Mock image captioning result caption = generate_mock_caption(file_path) return caption else: return "Unsupported analysis type" def generate_mock_caption(image_path): """ Generate a mock image caption. In a real implementation, this would use a Hugging Face model like Qwen/Qwen2.5-VL-7B-Instruct or similar for image captioning. """ # Get file size as a simple way to generate different captions file_size = os.path.getsize(image_path) # List of mock captions captions = [ "A beautiful landscape with mountains and a lake under blue sky.", "A person working at a desk with a computer and various office supplies.", "A busy city street with pedestrians and vehicles during the day.", "A close-up photograph of a flower with vibrant colors and intricate details.", "A group of people gathered around a table for what appears to be a business meeting.", "A modern architectural building with glass and steel elements." ] # Select a caption based on file size mock_caption = captions[file_size % len(captions)] return f"{mock_caption}\n\nNote: This is a mock caption. In a real implementation, this would be generated by a vision-language model like Qwen/Qwen2.5-VL-7B-Instruct."