Delete main.py

main.py

@@ -1,73 +0,0 @@
-from __future__ import annotations
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import FileResponse
-from fastapi.staticfiles import StaticFiles
-from fastapi import FastAPI, File, UploadFile, Form
-from fastapi.responses import FileResponse
-import torch
-import shutil
-import cv2
-import numpy as np
-import dlib
-from torchvision import transforms
-import torch.nn.functional as F
-from vtoonify_model import Model  # Importing the Model class from vtoonify_model.py
-
-import gradio as gr
-import pathlib
-import sys
-sys.path.insert(0, 'vtoonify')
-
-from util import load_psp_standalone, get_video_crop_parameter, tensor2cv2
-import torch
-import torch.nn as nn
-import numpy as np
-import dlib
-import cv2
-from model.vtoonify import VToonify
-from model.bisenet.model import BiSeNet
-import torch.nn.functional as F
-from torchvision import transforms
-from model.encoder.align_all_parallel import align_face
-import gc
-import huggingface_hub
-import os
-
-app = FastAPI()
-model = Model(device='cuda' if torch.cuda.is_available() else 'cpu')
-
-  
-    
-@app.on_event("startup")
-async def load_model():
-    global model
-    model = Model(device='cuda' if torch.cuda.is_available() else 'cpu')
-
-from fastapi.responses import StreamingResponse
-from io import BytesIO
-
-@app.post("/upload/")
-async def process_image(file: UploadFile = File(...), top: int = Form(...), bottom: int = Form(...), left: int = Form(...), right: int = Form(...)):
-    if model is None:
-        return {"error": "Model not loaded."}
-
-    # Save the uploaded image locally
-    with open("uploaded_image.jpg", "wb") as buffer:
-        shutil.copyfileobj(file.file, buffer)
-
-    # Process the uploaded image
-    aligned_face, instyle, message = model.detect_and_align_image("uploaded_image.jpg", top, bottom, left, right)
-    processed_image, message = model.image_toonify(aligned_face, instyle, model.exstyle, style_degree=0.5, style_type='cartoon1')
-
-    # Convert processed image to bytes
-    image_bytes = cv2.imencode('.jpg', processed_image)[1].tobytes()
-
-    # Return the processed image as a streaming response
-    return StreamingResponse(BytesIO(image_bytes), media_type="image/jpeg")
-
-
-app.mount("/", StaticFiles(directory="AB", html=True), name="static")
-
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="/app/AB/index.html", media_type="text/html")