import os
from pathlib import Path
import tempfile
import logging

# 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_document(file_path, analysis_type):
    """
    Process a document using Hugging Face models.
    
    Args:
        file_path (str): Path to the uploaded document
        analysis_type (str): Type of analysis to perform (summarize, etc.)
        
    Returns:
        str: Result of document analysis
    """
    logger.info(f"Processing document: {file_path} with analysis type: {analysis_type}")
    
    # Get file extension
    file_ext = os.path.splitext(file_path)[1].lower()
    
    # Extract text from document based on file type
    extracted_text = extract_text_from_document(file_path, file_ext)
    
    # For demonstration purposes, return a mock summary
    # In a real implementation, you would use a Hugging Face model here
    if analysis_type == "summarize":
        # Mock summarization result
        summary = f"This is a mock summary of the document. In a real implementation, this would be generated by a Hugging Face model like Llama-2-70b-chat-hf or similar.\n\nThe document appears to contain information about {get_mock_topic(extracted_text)}."
        return summary
    else:
        return "Unsupported analysis type"

def extract_text_from_document(file_path, file_ext):
    """
    Extract text from different document types.
    
    This is a placeholder that would be replaced with actual document parsing
    libraries like Apache Tika, PyMuPDF, etc.
    """
    logger.info(f"Extracting text from {file_path}")
    
    # In a real implementation, you would use libraries like:
    # - PyMuPDF for PDFs
    # - python-docx for DOCX files
    # - python-pptx for PPTX files
    # - openpyxl for Excel files
    
    # Mock extraction based on file type
    if file_ext == ".pdf":
        return "This is mock extracted text from a PDF document."
    elif file_ext == ".docx":
        return "This is mock extracted text from a Word document."
    elif file_ext == ".pptx":
        return "This is mock extracted text from a PowerPoint presentation."
    elif file_ext in [".xlsx", ".xls"]:
        return "This is mock extracted tabular data from an Excel spreadsheet."
    else:
        return "Unknown document type"

def get_mock_topic(text):
    """Generate a mock topic based on some simple heuristics."""
    # In a real implementation, this might use keyword extraction or topic modeling
    topics = ["business analysis", "scientific research", "financial data", 
              "project planning", "educational material"]
    
    # Simple mock topic selection based on text length
    return topics[len(text) % len(topics)]