app.py

from flask import Flask, render_template, request, send_file, redirect, url_for
import os
import uuid
from colorizers import *
import torch
from colorizers.util import load_img, preprocess_img, postprocess_tens
from PIL import Image
import numpy as np
import gc

# Disable CUDA to save memory on Render
torch.cuda.is_available = lambda: False

UPLOAD_FOLDER = 'uploads'
OUTPUT_FOLDER = 'outputs'
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'bmp'}

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config['OUTPUT_FOLDER'] = OUTPUT_FOLDER

os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(OUTPUT_FOLDER, exist_ok=True)

# Load models once at startup (CPU only)
print("Loading colorization models...")
colorizer_eccv16 = eccv16(pretrained=True).eval()
colorizer_siggraph17 = siggraph17(pretrained=True).eval()
print("Models loaded successfully!")

def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

@app.route('/', methods=['GET', 'POST'])
def index():
    if request.method == 'POST':
        files = request.files.getlist('files')
        
        if not files or len(files) == 0:
            return render_template('index.html', error='No files selected')
        
        results = []
        for file in files:
            if file.filename == '':
                continue
            if file and allowed_file(file.filename):
                filename = str(uuid.uuid4()) + os.path.splitext(file.filename)[1]
                filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
                file.save(filepath)
                
                try:
                    out_paths = colorize_and_save(filepath, filename)
                    results.append({
                        'orig_img': url_for('uploaded_file', filename=filename),
                        'eccv16_img': url_for('output_file', filename=os.path.basename(out_paths['eccv16'])),
                        'siggraph17_img': url_for('output_file', filename=os.path.basename(out_paths['siggraph17'])),
                        'filename': os.path.splitext(filename)[0]
                    })
                except Exception as e:
                    print(f"Error processing {filename}: {str(e)}")
                    continue
            else:
                continue
        
        if len(results) == 0:
            return render_template('index.html', error='No valid files to process')
        
        return render_template('result.html', images=results, total_count=len(results))
    
    return render_template('index.html')

@app.route('/uploads/<filename>')
def uploaded_file(filename):
    return send_file(os.path.join(app.config['UPLOAD_FOLDER'], filename))

@app.route('/outputs/<filename>')
def output_file(filename):
    return send_file(os.path.join(app.config['OUTPUT_FOLDER'], filename))

def colorize_and_save(img_path, filename):
    global colorizer_eccv16, colorizer_siggraph17
    img = load_img(img_path)
    (tens_l_orig, tens_l_rs) = preprocess_img(img, HW=(256,256))
    
    # Colorize with both models (tens_l_rs is already a PyTorch tensor)
    with torch.no_grad():
        out_ab_eccv16 = colorizer_eccv16(tens_l_rs)
        out_ab_siggraph17 = colorizer_siggraph17(tens_l_rs)
    
    out_img_eccv16 = postprocess_tens(tens_l_orig, out_ab_eccv16.cpu())
    out_img_siggraph17 = postprocess_tens(tens_l_orig, out_ab_siggraph17.cpu())
    
    # Convert to uint8 and save with PIL
    base_filename = os.path.splitext(filename)[0]
    
    out_img_eccv16_uint8 = (np.clip(out_img_eccv16, 0, 1) * 255).astype(np.uint8)
    eccv16_path = os.path.join(OUTPUT_FOLDER, f'{base_filename}_eccv16.png')
    Image.fromarray(out_img_eccv16_uint8).save(eccv16_path)
    
    out_img_siggraph17_uint8 = (np.clip(out_img_siggraph17, 0, 1) * 255).astype(np.uint8)
    siggraph17_path = os.path.join(OUTPUT_FOLDER, f'{base_filename}_siggraph17.png')
    Image.fromarray(out_img_siggraph17_uint8).save(siggraph17_path)
    
    # Clean up memory
    del tens_l_rs, out_ab_eccv16, out_ab_siggraph17, img, tens_l_orig
    del out_img_eccv16, out_img_siggraph17, out_img_eccv16_uint8, out_img_siggraph17_uint8
    gc.collect()
    
    return {'eccv16': eccv16_path, 'siggraph17': siggraph17_path}

if __name__ == '__main__':
    # Support both Render (PORT env var) and HuggingFace Spaces (default 7860)
    port = int(os.getenv('PORT', os.getenv('SERVER_PORT', 7860)))
    app.run(host='0.0.0.0', port=port, debug=False)