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from flask import Flask, request, render_template_string
from PIL import Image
import numpy as np
import io
import base64
import torch
import torchvision.transforms as transforms
import os
import sys
app = Flask(__name__)
HTML_TEMPLATE = """
<!DOCTYPE html>
<html>
<head>
<title>ZeroIG Enhancement - Debug</title>
<style>
body { font-family: Arial, sans-serif; max-width: 1000px; margin: 0 auto; padding: 20px; }
.debug { background: #f0f0f0; padding: 15px; margin: 10px 0; border-radius: 5px; font-family: monospace; }
.container { text-align: center; }
.upload-area { border: 2px dashed #ccc; padding: 40px; margin: 20px 0; border-radius: 10px; }
.result { margin-top: 20px; }
.comparison { display: flex; justify-content: space-around; flex-wrap: wrap; }
.image-container { margin: 10px; }
img { max-width: 400px; height: auto; border: 1px solid #ddd; border-radius: 5px; }
.status { color: green; font-weight: bold; margin: 10px 0; }
.error { color: red; }
</style>
</head>
<body>
<div class="container">
<h1>🔧 ZeroIG Debug Mode</h1>
<div class="debug">
<h3>📁 File Check:</h3>
{{ file_status }}
</div>
<div class="debug">
<h3>🐍 Import Status:</h3>
{{ import_status }}
</div>
<form method="post" enctype="multipart/form-data">
<div class="upload-area">
<input type="file" name="image" accept="image/*" required>
<br><br>
<button type="submit" style="padding: 10px 20px; font-size: 16px;">🧪 Test Enhancement</button>
</div>
</form>
{% if status %}
<div class="status">{{ status }}</div>
{% endif %}
{% if error %}
<div class="error">{{ error }}</div>
{% endif %}
{% if original_image and result_image %}
<div class="result">
<h3>Results:</h3>
<div class="comparison">
<div class="image-container">
<h4>Original</h4>
<img src="data:image/png;base64,{{ original_image }}" alt="Original">
</div>
<div class="image-container">
<h4>Enhanced</h4>
<img src="data:image/png;base64,{{ result_image }}" alt="Enhanced">
</div>
</div>
</div>
{% endif %}
</div>
</body>
</html>
"""
def check_files():
"""Check what files are available"""
status = []
# Check current directory files
files_in_dir = os.listdir('.')
status.append(f"Files in current directory: {files_in_dir}")
# Check specific ZeroIG files
required_files = ['model.py', 'loss.py', 'utils.py']
for file in required_files:
if os.path.exists(file):
status.append(f"✅ {file} - EXISTS")
# Check file size
size = os.path.getsize(file)
status.append(f" Size: {size} bytes")
else:
status.append(f"❌ {file} - MISSING")
# Check weights directory
if os.path.exists('weights'):
weights_files = os.listdir('weights')
status.append(f"📁 weights/ directory: {weights_files}")
else:
status.append("📁 weights/ directory: NOT FOUND")
return "<br>".join(status)
def check_imports():
"""Try importing ZeroIG modules and report results"""
status = []
try:
# Add current directory to path
import sys
sys.path.insert(0, '.')
status.append("✅ Added current directory to Python path")
# Try importing each module
try:
import model
status.append("✅ Successfully imported 'model' module")
# Check what's in the model module
model_contents = dir(model)
status.append(f" model module contents: {model_contents}")
# Try to access specific classes
if hasattr(model, 'Network'):
status.append("✅ Found Network class")
else:
status.append("❌ Network class not found")
if hasattr(model, 'Finetunemodel'):
status.append("✅ Found Finetunemodel class")
else:
status.append("❌ Finetunemodel class not found")
except Exception as e:
status.append(f"❌ Failed to import model: {e}")
try:
import loss
status.append("✅ Successfully imported 'loss' module")
except Exception as e:
status.append(f"❌ Failed to import loss: {e}")
try:
import utils
status.append("✅ Successfully imported 'utils' module")
except Exception as e:
status.append(f"❌ Failed to import utils: {e}")
except Exception as e:
status.append(f"❌ General import error: {e}")
return "<br>".join(status)
def try_create_model():
"""Try to create ZeroIG model and report what happens"""
try:
sys.path.insert(0, '.')
from model import Network, Finetunemodel
# Try creating Network
network = Network()
print("✅ Successfully created Network model")
# Try creating Finetunemodel (this will fail without weights, but should show the error)
try:
finetuned = Finetunemodel("./weights/model.pt")
print("✅ Successfully created Finetunemodel")
return finetuned, "Finetunemodel"
except Exception as e:
print(f"⚠️ Finetunemodel failed (expected without weights): {e}")
print("✅ Using Network model instead")
return network, "Network"
except Exception as e:
print(f"❌ Model creation failed: {e}")
import traceback
traceback.print_exc()
return None, f"Failed: {e}"
# Try to load model on startup
print("🚀 Starting ZeroIG debug app...")
print("📁 Checking files...")
file_check = check_files()
print("🐍 Checking imports...")
import_check = check_imports()
# Try to create model
print("🤖 Trying to create model...")
model, model_status = try_create_model()
def simple_enhance(image):
"""Fallback enhancement"""
arr = np.array(image).astype(np.float32)
enhanced = np.clip(arr * 1.5, 0, 255).astype(np.uint8)
return Image.fromarray(enhanced)
def image_to_base64(image):
"""Convert PIL image to base64"""
img_buffer = io.BytesIO()
image.save(img_buffer, format='PNG')
img_str = base64.b64encode(img_buffer.getvalue()).decode()
return img_str
@app.route('/', methods=['GET', 'POST'])
def index():
original_image = None
result_image = None
status = None
error = None
if request.method == 'POST':
try:
file = request.files['image']
if file:
print(f"Processing: {file.filename}")
image = Image.open(file.stream).convert('RGB')
original_image = image_to_base64(image)
if model is not None:
# Try using the actual model
try:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
model.eval()
transform = transforms.ToTensor()
input_tensor = transform(image).unsqueeze(0).to(device)
with torch.no_grad():
if hasattr(model, 'enhance') and hasattr(model, 'denoise_1'):
enhanced, denoised = model(input_tensor)
result_tensor = denoised
else:
outputs = model(input_tensor)
result_tensor = outputs[13]
result_tensor = result_tensor.squeeze(0).cpu().clamp(0, 1)
enhanced_image = transforms.ToPILImage()(result_tensor)
result_image = image_to_base64(enhanced_image)
status = f"✅ Used {model_status} model successfully!"
except Exception as e:
print(f"Model processing error: {e}")
enhanced_image = simple_enhance(image)
result_image = image_to_base64(enhanced_image)
status = f"⚠️ Model failed, used simple enhancement: {e}"
else:
enhanced_image = simple_enhance(image)
result_image = image_to_base64(enhanced_image)
status = "⚠️ No model available, used simple enhancement"
except Exception as e:
error = f"Error: {e}"
print(f"Error: {e}")
return render_template_string(HTML_TEMPLATE,
file_status=file_check,
import_status=import_check,
original_image=original_image,
result_image=result_image,
status=status,
error=error)
if __name__ == '__main__':
print("🚀 Debug app ready!")
app.run(host='0.0.0.0', port=7860) |