import os
import requests
import torch
from io import BytesIO
from PIL import Image
from supabase import create_client, Client
from docling.document_converter import DocumentConverter
from transformers import pipeline

# --- CONFIGURATION ---
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_SERVICE_ROLE_KEY")
supabase = create_client(SUPABASE_URL, SUPABASE_KEY)

converter = DocumentConverter()

# LayoutLMv3 handles the 'Intelligence' (Question Answering on images)
# Note: This automatically uses GPU if available
device = 0 if torch.cuda.is_available() else -1
qa_predictor = pipeline(
    "document-question-answering", 
    model="impira/layoutlm-document-qa",
    device=device
)

def download_as_image(url):
    """Downloads a file and ensures it's a PIL Image for LayoutLM."""
    response = requests.get(url, timeout=10)
    return Image.open(BytesIO(response.content)).convert("RGB")

def clean_amount(text):
    """Converts string currency (e.g., '$1,200.50') to a float."""
    try:
        return float(''.join(c for c in text if c.isdigit() or c == '.'))
    except ValueError:
        return 0.0

def process_document(invoice_id, file_url):
    """The main extraction pipeline."""
    print(f"🔄 Processing Invoice ID: {invoice_id}")
    
    try:
        # STEP 1: Layout Analysis (Docling)
        # This converts the file into a structural JSON map
        conversion_result = converter.convert(file_url)
        doc_json = conversion_result.document.export_to_dict()

        # STEP 2: Spatial Extraction (Hugging Face / LayoutLM)
        # We ask questions directly to the document image
        img = download_as_image(file_url)
        
        questions = {
            "vendor_name": "What is the name of the company or vendor?",
            "invoice_number": "What is the invoice number or reference ID?",
            "total_amount": "What is the grand total or total amount due?",
            "date": "What is the date of the invoice?"
        }

        extracted = {}
        for key, question in questions.items():
            result = qa_predictor(image=img, question=question)
            extracted[key] = result[0]['answer'] if result else "Not Found"

        # STEP 3: Update Supabase
        # We push the normalized data back to the database
        supabase.table("invoices").update({
            "status": "completed",
            "vendor_name": extracted["vendor_name"],
            "invoice_number": extracted["invoice_number"],
            "total_amount": clean_amount(extracted["total_amount"]),
            "raw_json": doc_json, # Saves the full Docling structure for future use
            "metadata": {"extracted_date": extracted["date"]}
        }).eq("id", invoice_id).execute()

        print(f"✅ Success: Updated {invoice_id}")

    except Exception as e:
        print(f"❌ Critical Error: {str(e)}")
        supabase.table("invoices").update({"status": "error"}).eq("id", invoice_id).execute()

# --- 3. TRIGGER (Example for manual testing) ---
if __name__ == "__main__":
    # In production, this would be called by your n8n webhook or an API route
    print("🤖 Worker is online and waiting for tasks...")