main.py

from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import StreamingResponse, FileResponse
from fastapi.staticfiles import StaticFiles
import torch
import cv2
import numpy as np
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('cartoon4')

@app.post("/upload/")
async def process_image(file: UploadFile = File(...)):
    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)

    # Automatically detect and align the face
    aligned_face, instyle, message = model.detect_and_align_image(frame_rgb, 0, 0, 0, 0)
    
    if instyle is None:
        return {"error": "No face detected. Please try a different image."}

    # Process the uploaded image
    processed_image, message = model.image_toonify(aligned_face, instyle, model.exstyle, style_degree=0.5, style_type='cartoon4')

    # 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")