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| import logging | |
| from pathlib import Path | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| def process_question(file_path: str, question: str) -> str: | |
| """ | |
| Process a question about a document or image using Hugging Face models. | |
| Args: | |
| file_path (str): Path to the document or image | |
| question (str): User's question | |
| Returns: | |
| str: Answer to the question | |
| """ | |
| logger.info(f"Processing question for {file_path}: {question}") | |
| # Mock implementation - would use actual Hugging Face models in production | |
| # Example models: deepseek-ai/DeepSeek-V2-Chat or meta-llama/Llama-2-70b-chat-hf | |
| file_ext = Path(file_path).suffix.lower() | |
| # Generate mock response based on file type | |
| if file_ext in ['.pdf', '.docx', '.pptx', '.xlsx', '.xls']: | |
| return f"Based on the document content, the answer to '{question}' is: This is a mock response that would be generated by an actual language model in production." | |
| elif file_ext in ['.jpg', '.jpeg', '.png']: | |
| return f"Looking at the image, in response to '{question}': This is a mock response that would be generated by a vision-language model in production." | |
| else: | |
| return "Unsupported file type for question answering." |