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b3a532e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | import os
import sys
import time
import asyncio
import torch
import torch.nn.functional as F
import timm
from torchvision import transforms
from PIL import Image
from playwright.async_api import async_playwright
class ProductionAnalyzer:
def __init__(self, model_path="models/production_resnet_ema.pth", model_name="resnet18", num_classes=2):
"""
Initializes the ResNet18 production model on available hardware.
"""
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"🖥️ Initializing backend hardware: {self.device}")
# 1. Initialize the ResNet18 architecture base
print(f"🏗️ Building network architecture: {model_name}...")
self.model = timm.create_model(model_name, pretrained=False, num_classes=num_classes)
# 2. Safety check for the weights file
if not os.path.exists(model_path):
raise FileNotFoundError(f"❌ Could not find weight file at: {model_path}\n"
f"Please ensure it is placed inside the 'models' folder.")
print(f"📥 Loading ResNet18 weights from {model_path}...")
state_dict = torch.load(model_path, map_location=self.device, weights_only=True)
# 3. 🛠️ FIXED: Strip wrapper prefixes and safely drop training metadata keys
clean_state_dict = {}
for key, value in state_dict.items():
if key == "n_averaged":
continue # Skip the training counter metadata so PyTorch doesn't throw an error
clean_key = key.replace('module.', '')
clean_state_dict[clean_key] = value
self.model.load_state_dict(clean_state_dict)
# 4. Lock the model for evaluation mode
self.model.eval()
self.model.to(self.device)
print("✅ ResNet18 Engine successfully loaded and locked for inference.")
# 5. Define standard normalizations expected by ResNet18
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Alphabetical class mapping (0: Legit, 1: Phishing)
self.class_names = ["Legit", "Phishing"]
def analyze_image(self, image_path):
"""
Feeds the captured screenshot into the ResNet18 neural network.
"""
try:
image = Image.open(image_path).convert('RGB')
input_tensor = self.transform(image).unsqueeze(0).to(self.device)
with torch.no_grad():
logits = self.model(input_tensor)
probabilities = F.softmax(logits[0], dim=0)
confidence, predicted_idx = torch.max(probabilities, 0)
return {
"prediction": self.class_names[predicted_idx.item()],
"confidence": f"{confidence.item() * 100:.2f}%"
}
except Exception as e:
return {"error": f"Model inference failed: {str(e)}"}
async def capture_screenshot(url, output_path="temp_inference.png"):
"""
Launches a headless browser to safely capture a screenshot of the live URL.
"""
if not url.startswith('http://') and not url.startswith('https://'):
url = 'https://' + url
print(f"🌐 Navigating to: {url} ...")
async with async_playwright() as p:
browser = await p.chromium.launch(headless=True)
context = await browser.new_context(
viewport={'width': 1280, 'height': 720},
ignore_https_errors=True, # Bypasses broken SSL certifications on malicious sites
accept_downloads=False
)
page = await context.new_page()
try:
# 12-second timeout to handle slow/malicious servers
await page.goto(url, timeout=12000, wait_until='domcontentloaded')
await asyncio.sleep(1) # Brief pause to let visual components load completely
await page.screenshot(path=output_path)
return output_path
except Exception as e:
print(f"❌ Failed to reach or capture the website: {e}")
return None
finally:
await page.close()
await browser.close()
async def main():
print("=" * 45)
print("🛡️ CHIMERA 2.0 LIVE URL DETECTOR ENGINE")
print("=" * 45)
try:
analyzer = ProductionAnalyzer()
except Exception as e:
print(e)
return
TEMP_IMG = "temp_inference.png"
try:
while True:
print("\n" + "-" * 45)
url_input = input("🔗 Enter URL to inspect (or type 'exit' to quit): ").strip()
if url_input.lower() == 'exit':
print("Shutting down engine...")
break
if not url_input:
continue
start_time = time.time()
# Step 1: Capture screenshot via Playwright
screenshot_file = await capture_screenshot(url_input, TEMP_IMG)
if screenshot_file and os.path.exists(screenshot_file):
# Step 2: Pass screenshot into ResNet18
print("🔍 Running Deep Learning Visual Inspection...")
result = analyzer.analyze_image(screenshot_file)
total_time = time.time() - start_time
# Step 3: Output results safely
print("\n" + "=" * 35)
print("📊 LIVE DETECTION REPORT")
print("=" * 35)
if "error" in result:
print(f"Result: {result['error']}")
else:
status_prefix = "🚨 ALERT!!" if result['prediction'] == "Phishing" else "✅ CLEAR:"
print(f"Verdict : {status_prefix} {result['prediction']}")
print(f"Confidence : {result['confidence']}")
print(f"Total Time : {total_time:.2f} seconds")
print("=" * 35)
if os.path.exists(TEMP_IMG):
os.remove(TEMP_IMG)
else:
print("❌ Inspection aborted. Visual fingerprint could not be gathered.")
except KeyboardInterrupt:
print("\nExiting execution gracefully...")
finally:
if os.path.exists(TEMP_IMG):
os.remove(TEMP_IMG)
if __name__ == "__main__":
if sys.platform == 'win32':
asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy())
asyncio.run(main()) |