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)}")
    logging.debug(f"Starting to process paper: {pdf_file}")
    os.makedirs(paper_dir, exist_ok=True)

    if isinstance(pdf_file, str):
        pdf_path = pdf_file
    elif hasattr(pdf_file, 'name') and hasattr(pdf_file, 'read'):
        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 [], []

    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)

    models = ['gpt', 'claude', 'gemini', 'commandr']
    selected_models = random.sample(models, 2)

    reviews = []
    for model in selected_models:
        processor = PaperProcessor(prompt_dir, model, **api_keys)
        review_text = processor.process_paper(paper)
        reviews.append(review_text)
    logging.debug(f"Reviews generated: {reviews}")
    return reviews, selected_models