backend/modules/question_answering.py

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."