from fastapi import FastAPI, File, UploadFile, Form, Request
from fastapi.responses import HTMLResponse, FileResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
import cv2
import os
import torch
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan.utils import GFPGANer
from realesrgan.utils import RealESRGANer

app = FastAPI()
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")

# Download weights if not exists
def download_weights():
    weights = [
        ('realesr-general-x4v3.pth', 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'),
        ('GFPGANv1.2.pth', 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth'),
        ('GFPGANv1.3.pth', 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'),
        ('GFPGANv1.4.pth', 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth')
    ]
    for weight_file, weight_url in weights:
        if not os.path.exists(weight_file):
            os.system(f"wget {weight_url} -P .")

# Initialize model and weights
def initialize_models():
    model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
    half = True if torch.cuda.is_available() else False
    return model, half

# Perform image enhancement
def enhance_image(img_path, version, scale, model, half):
    try:
        input_img = cv2.imread(img_path)
        face_enhancer = None
        
        if version == 'v1.2':
            face_enhancer = GFPGANer(
                model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None)
        elif version == 'v1.3':
            face_enhancer = GFPGANer(
                model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None)
        elif version == 'v1.4':
            face_enhancer = GFPGANer(
                model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None)
        elif version == 'RealESR-General-x4v3':
            face_enhancer = RealESRGANer(
                scale=4, model_path='realesr-general-x4v3.pth', model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
        
        if face_enhancer:
            _, _, output = face_enhancer.enhance(input_img, has_aligned=False, only_center_face=False, paste_back=True)
            
            if scale != 2:
                interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
                h, w = input_img.shape[0:2]
                output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
                
            output_path = f'output/out.jpg'
            cv2.imwrite(output_path, output)
            
            return output_path
        else:
            return None
    except Exception as e:
        print(f"Error enhancing image: {e}")
        return None

# Download weights
download_weights()

# Initialize model
model, half = initialize_models()


@app.post("/process_image/")
async def process_image(file: UploadFile = File(...), version: str = Form(...), scale: int = Form(...)):
    try:
        contents = await file.read()
        img_path = "temp.jpg"
        with open(img_path, "wb") as f:
            f.write(contents)
        
        output_path = enhance_image(img_path, version, scale, model, half)
        
        if output_path:
            return FileResponse(output_path, media_type='image/jpeg')
        else:
            return {"error": "Failed to process the image."}
    except Exception as e:
        return {"error": f"An error occurred: {e}"}

app.mount("/", StaticFiles(directory="static", html=True), name="static")

@app.get("/")
def index() -> FileResponse:
    return FileResponse(path="/app/static/index.html", media_type="text/html")