from fastapi import FastAPI, APIRouter, File, UploadFile, Form
from fastapi.middleware.cors import CORSMiddleware
from typing import Optional
from PIL import Image
import urllib.request
from io import BytesIO
import json
from config import settings  
import utils
from routers import inference, training
from routers.donut_inference import process_document_donut
from huggingface_hub import login
import os

# Login using Hugging Face token from environment
login(os.getenv("HUGGINGFACE_KEY"))

app = FastAPI(openapi_url="/api/v1/sparrow-ml/openapi.json", docs_url="/api/v1/sparrow-ml/docs")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
    allow_credentials=True,
)

app.include_router(inference.router, prefix="/api-inference/v1/sparrow-ml", tags=["Inference"])
app.include_router(training.router, prefix="/api-training/v1/sparrow-ml", tags=["Training"])

router = APIRouter()

def count_values(obj):
    if isinstance(obj, dict):
        return sum(count_values(v) for v in obj.values())
    elif isinstance(obj, list):
        return sum(count_values(i) for i in obj)
    else:
        return 1

@router.post("/inference")
async def run_inference(
    file: Optional[UploadFile] = File(None), 
    image_url: Optional[str] = Form(None),
    shipper_id: int = Form(...), 
    model_in_use: str = Form('donut')
):
    result = []

    # Dynamically select model
    model_url = settings.get_model_url(shipper_id)
    model_name = model_url.replace("https://huggingface.co/spaces/", "")
    print(f"[DEBUG] Using model: {model_name}")

    if file:
        if file.content_type not in ["image/jpeg", "image/jpg"]:
            return {"error": "Invalid file type. Only JPG images are allowed."}
        
        image = Image.open(BytesIO(await file.read()))
        result, processing_time = process_document_donut(image, model_url)
        utils.log_stats(settings.inference_stats_file, [processing_time, count_values(result), file.filename, model_name])
        print(f"Processing time: {processing_time:.2f} seconds")
    
    elif image_url:
        with urllib.request.urlopen(image_url) as url:
            image = Image.open(BytesIO(url.read()))
        
        result, processing_time = process_document_donut(image, model_url)
        file_name = image_url.split("/")[-1]
        utils.log_stats(settings.inference_stats_file, [processing_time, count_values(result), file_name, model_name])
        print(f"Processing time inference: {processing_time:.2f} seconds")
    
    else:
        result = {"info": "No input provided"}

    return result

@router.get("/statistics")
async def get_statistics():
    file_path = settings.inference_stats_file
    if os.path.exists(file_path):
        with open(file_path, 'r') as file:
            try:
                content = json.load(file)
            except json.JSONDecodeError:
                content = []
    else:
        content = []
    return content

@app.get("/")
async def root():
    return {"message": "Senga delivery notes inferencing"}