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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)] |