import cv2
import numpy as np
import torch
import io
import os
import asyncio
from fastapi.responses import StreamingResponse
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from huggingface_hub import hf_hub_download
from concurrent.futures import ThreadPoolExecutor

cache_dir = '/tmp/huggingface_cache'
os.environ['HF_HOME'] = cache_dir
os.makedirs(cache_dir, exist_ok=True)

class VideoEnhancer:
    def __init__(self, model_name="RealESRGAN_x4plus"):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = self.load_model(model_name)
        self.executor = ThreadPoolExecutor(max_workers=4)

    def load_model(self, model_name):
        if model_name == "RealESRGAN_x4plus":
            model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
            model_path = hf_hub_download("schwgHao/RealESRGAN_x4plus", "RealESRGAN_x4plus.pth")
            return RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=True)
        else:
            raise ValueError(f"Unsupported model: {model_name}")

    async def enhance_frame(self, frame):
        loop = asyncio.get_running_loop()
      
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
       
        enhanced, _ = await loop.run_in_executor(self.executor, self.model.enhance, frame_rgb)
        
        return cv2.cvtColor(enhanced, cv2.COLOR_RGB2BGR)

    async def process_video(self, input_bytes, output_bytes):
        cap = cv2.VideoCapture(input_bytes)
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        fps = cap.get(cv2.CAP_PROP_FPS)

        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_bytes, fourcc, fps, (width * 4, height * 4))

        while cap.isOpened():
            ret, frame = cap.read()
            if not ret:
                break
            enhanced_frame = await self.enhance_frame(frame)
            out.write(enhanced_frame)

        cap.release()
        out.release()

    async def stream_enhanced_video(self, video_file):
        video_bytes = await video_file.read()
        cap = cv2.VideoCapture(io.BytesIO(video_bytes).getvalue())
        
        async def generate():
            while cap.isOpened():
                ret, frame = cap.read()
                if not ret:
                    break
                enhanced_frame = await self.enhance_frame(frame)
                _, buffer = cv2.imencode('.jpg', enhanced_frame)
                yield (b'--frame\r\n'
                       b'Content-Type: image/jpeg\r\n\r\n' + buffer.tobytes() + b'\r\n')
            cap.release()

        return StreamingResponse(generate(), media_type="multipart/x-mixed-replace; boundary=frame")