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| import fitz | |
| import os | |
| import logging | |
| import random | |
| from models import Paper, PaperProcessor | |
| def extract_text_from_pdf(filename): | |
| with fitz.open(filename) as pdf_document: | |
| text = "" | |
| for page in pdf_document: | |
| text += page.get_text() | |
| return text.encode('latin-1', 'replace').decode('latin-1') | |
| def process_paper(pdf_file, paper_dir, prompt_dir, api_keys): | |
| logging.info(f"Processing file type in process_paper: {type(pdf_file)}") # Log the type of the file here as well | |
| logging.debug(f"Starting to process paper: {pdf_file}") | |
| # Ensure the directory exists | |
| os.makedirs(paper_dir, exist_ok=True) | |
| # Handle file based on its type | |
| if isinstance(pdf_file, str): | |
| # Assume pdf_file is a path to the PDF file | |
| pdf_path = pdf_file | |
| elif hasattr(pdf_file, 'name') and hasattr(pdf_file, 'read'): | |
| # It's a file-like object | |
| pdf_path = os.path.join(paper_dir, pdf_file.name) | |
| with open(pdf_path, "wb") as f: | |
| f.write(pdf_file.read()) | |
| else: | |
| logging.error("Received object is neither a path nor a file-like object.") | |
| return [] | |
| # Extract text from the PDF | |
| extracted_text = extract_text_from_pdf(pdf_path) | |
| paper = Paper(pdf_file.name if hasattr(pdf_file, 'name') else os.path.basename(pdf_path), extracted_text) | |
| # Randomly select two models | |
| models = ['gpt', 'claude', 'gemini', 'commandr'] | |
| selected_models = random.sample(models, 2) | |
| # Process the paper with each selected model | |
| reviews = [] | |
| for model in selected_models: | |
| processor = PaperProcessor(prompt_dir, model, **api_keys) | |
| review_text = processor.process_paper(paper) | |
| #review_dict = {section.split(':')[0]: section.split(':')[1].strip() for section in review_text} | |
| reviews.append(review_text) | |
| logging.debug(f"Reviews generated: {reviews}") | |
| return reviews |