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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)] |