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| 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." |