from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import StreamingResponse
from fastapi.staticfiles import StaticFiles
import torch
import shutil
import cv2
import numpy as np
import io
from io import BytesIO

app = FastAPI()

# Load model and necessary components
model = None

def load_model():
    global model
    from vtoonify_model import Model
    model = Model(device='cuda' if torch.cuda.is_available() else 'cpu')
    model.load_model('cartoon1')

@app.post("/upload/")
async def process_image(file: UploadFile = File(...), top: int = Form(...), bottom: int = Form(...), left: int = Form(...), right: int = Form(...)):
    global model
    if model is None:
        load_model()

    # Read the uploaded image file
    contents = await file.read()

    # Convert the uploaded image to numpy array
    nparr = np.frombuffer(contents, np.uint8)
    frame_rgb = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

    # Process the uploaded image
    aligned_face, instyle, message = model.detect_and_align_image(frame_rgb, top, bottom, left, right)
    processed_image, message = model.image_toonify(aligned_face, instyle, model.exstyle, style_degree=0.5, style_type='cartoon1')

    # Convert BGR to RGB
    processed_image_rgb = cv2.cvtColor(processed_image, cv2.COLOR_BGR2RGB)

    # Convert processed image to bytes
    _, encoded_image = cv2.imencode('.jpg', processed_image_rgb)

    # Return the processed image as a streaming response
    return StreamingResponse(BytesIO(encoded_image.tobytes()), media_type="image/jpeg")


# Mount static files directory
app.mount("/", StaticFiles(directory="AB", html=True), name="static")

# Define index route
@app.get("/")
def index():
    return FileResponse(path="/app/AB/index.html", media_type="text/html")