import os
import requests
from io import BytesIO
from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredFileLoader
from dotenv import load_dotenv
from yolo_text_extraction import pdf_to_text

load_dotenv()

class CV:
    def __init__(self, file_url):
        self.file_url = file_url
        self.doc_loader = {
            ".pdf": PyPDFLoader,
            ".docx": Docx2txtLoader,
            ".txt": UnstructuredFileLoader
        }

    def get_cv_text(self):
        # Download the file from Supabase
        response = requests.get(self.file_url)
        file_content = BytesIO(response.content)
        
        # Determine file extension
        _, ext = os.path.splitext(self.file_url.split("?")[0])  # Remove query parameters
        
        if ext.lower() in self.doc_loader:
            if ext.lower() == '.pdf':
                loader = self.doc_loader[ext.lower()](file_content)
            else:
                # For non-PDF files, save temporarily and use the appropriate loader
                with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as temp_file:
                    temp_file.write(file_content.getvalue())
                    temp_file_path = temp_file.name
                
                loader = self.doc_loader[ext.lower()](temp_file_path)
            
            data = loader.load()
            
            if ext.lower() != '.pdf':
                os.unlink(temp_file_path)  # Delete the temporary file
            
            if data:
                text = "\n".join([page.page_content for page in data])
                return text if text.strip() else pdf_to_text(file_content)
            else:
                return pdf_to_text(file_content)
        else:
            return pdf_to_text(file_content)

    def analyse_cv_quality(self):
        from cv_analyzer import analyze_cv
        cv_text = self.get_cv_text()
        result = analyze_cv(cv_text)
        return result