import gradio as gr import os import re import time import torch import torch.nn as nn from PIL import Image import requests import easyocr from transformers import AutoTokenizer from torchvision import transforms from torchvision import models from torchvision.transforms import functional as F import pandas as pd from huggingface_hub import hf_hub_download import warnings warnings.filterwarnings("ignore") # --- Setup --- # Device setup device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") # Load tokenizer tokenizer = AutoTokenizer.from_pretrained('indobenchmark/indobert-base-p1') # Image transformation class ResizePadToSquare: def __init__(self, target_size=300): self.target_size = target_size def __call__(self, img): img = img.convert("RGB") img.thumbnail((self.target_size, self.target_size), Image.BILINEAR) delta_w = self.target_size - img.size[0] delta_h = self.target_size - img.size[1] padding = (delta_w // 2, delta_h // 2, delta_w - delta_w // 2, delta_h - delta_h // 2) img = F.pad(img, padding, fill=0, padding_mode='constant') return img transform = transforms.Compose([ ResizePadToSquare(300), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) # Screenshot folder SCREENSHOT_DIR = "screenshots" os.makedirs(SCREENSHOT_DIR, exist_ok=True) # Create OCR reader reader = easyocr.Reader(['id']) # Indonesia language print("OCR reader initialized.") # --- Model --- class LateFusionModel(nn.Module): def __init__(self, image_model, text_model): super(LateFusionModel, self).__init__() self.image_model = image_model self.text_model = text_model self.image_weight = nn.Parameter(torch.tensor(0.5)) self.text_weight = nn.Parameter(torch.tensor(0.5)) def forward(self, images, input_ids, attention_mask): with torch.no_grad(): image_logits = self.image_model(images).squeeze(1) text_logits = self.text_model(input_ids=input_ids, attention_mask=attention_mask).logits.squeeze(1) weights = torch.softmax(torch.stack([self.image_weight, self.text_weight]), dim=0) fused_logits = weights[0] * image_logits + weights[1] * text_logits return fused_logits, image_logits, text_logits, weights # Load model model_path = "models/best_fusion_model.pt" if os.path.exists(model_path): fusion_model = torch.load(model_path, map_location=device, weights_only=False) else: model_path = hf_hub_download(repo_id="azzandr/gambling-fusion-model", filename="best_fusion_model.pt") fusion_model = torch.load(model_path, map_location=device, weights_only=False) # fusion_model = unwrap_dataparallel(fusion_model) fusion_model.to(device) fusion_model.eval() print("Fusion model loaded successfully!") # Load Image-Only Model # Load image model from state_dict image_model_path = "models/best_image_model_Adam_lr0.0001_bs32_state_dict.pt" if os.path.exists(image_model_path): image_only_model = models.efficientnet_b3(weights=models.EfficientNet_B3_Weights.DEFAULT) num_features = image_only_model.classifier[1].in_features image_only_model.classifier = nn.Linear(num_features, 1) image_only_model.load_state_dict(torch.load(image_model_path, map_location=device)) image_only_model.to(device) image_only_model.eval() print("Image-only model loaded from state_dict successfully!") else: print("Image-only model not found locally. Downloading from Hugging Face Hub...") image_model_path = hf_hub_download(repo_id="azzandr/gambling-image-model", filename="best_image_model_Adam_lr0.0001_bs32_state_dict.pt") image_only_model = models.efficientnet_b3(weights=models.EfficientNet_B3_Weights.DEFAULT) num_features = image_only_model.classifier[1].in_features image_only_model.classifier = nn.Linear(num_features, 1) image_only_model.load_state_dict(torch.load(image_model_path, map_location=device)) image_only_model.to(device) image_only_model.eval() print("Image-only model downloaded and loaded successfully!") # --- Functions --- def clean_text(text): exceptions = { "di", "ke", "ya" } # ----- BASIC CLEANING ----- text = re.sub(r"http\S+", "", text) # Hapus URL text = re.sub(r"\n", " ", text) # Ganti newline dengan spasi text = re.sub(r"[^a-zA-Z']", " ", text) # Hanya sisakan huruf dan apostrof text = re.sub(r"\s{2,}", " ", text).strip().lower() # Hapus spasi ganda, ubah ke lowercase # ----- FILTERING ----- words = text.split() filtered_words = [ w for w in words if (len(w) > 2 or w in exceptions) # Simpan kata >2 huruf atau ada di exceptions ] text = ' '.join(filtered_words) # ----- REMOVE UNWANTED PATTERNS ----- text = re.sub(r'\b[aeiou]+\b', '', text) # Hapus kata semua vokal (panjang berapa pun) text = re.sub(r'\b[^aeiou\s]+\b', '', text) # Hapus kata semua konsonan (panjang berapa pun) text = re.sub(r'\b\w{20,}\b', '', text) # Hapus kata sangat panjang (≥20 huruf) text = re.sub(r'\s+', ' ', text).strip() # Bersihkan spasi ekstra # check words number if len(text.split()) < 5: print(f"Cleaned text too short ({len(text.split())} words). Ignoring text.") return "" # empty return to use image-only return text # Your API key SCREENSHOT_API_KEY = os.getenv("SCREENSHOT_API_KEY") # Ambil dari environment variable # Constants for screenshot configuration CLOUDFLARE_CHECK_KEYWORDS = ["Checking your browser", "Just a moment", "Cloudflare"] def ensure_http(url): if not url.startswith(('http://', 'https://')): return 'http://' + url return url def sanitize_filename(url): return re.sub(r'[^\w\-_\. ]', '_', url) def take_screenshot(url): url = ensure_http(url) filename = sanitize_filename(url) + '.png' filepath = os.path.join(SCREENSHOT_DIR, filename) try: if not SCREENSHOT_API_KEY: print("SCREENSHOT_API_KEY not found in environment.") return None api_url = "https://api.apiflash.com/v1/urltoimage" # Base parameters - only using supported parameters params = { "access_key": SCREENSHOT_API_KEY, "url": url, "format": "png", "wait_until": "network_idle", "delay": 2, "fail_on_status": "400,401,402,403,404,500,502,503,504", "fresh": "true", # Don't use cached version "response_type": "image", "wait_for": "body" # Wait for body to be present } print(f"Taking screenshot of: {url}") response = requests.get(api_url, params=params) if response.status_code == 200: # Check if response is actually an image if response.headers.get('content-type', '').startswith('image'): with open(filepath, 'wb') as f: f.write(response.content) print(f"Screenshot taken successfully for URL: {url}") return filepath else: print(f"API returned non-image content") return None else: error_msg = response.text print(f"Screenshot failed: {error_msg}") # Check for Cloudflare detection if any(keyword.lower() in error_msg.lower() for keyword in CLOUDFLARE_CHECK_KEYWORDS): print("Cloudflare challenge detected, retrying with different parameters...") # Retry with different parameters for Cloudflare params.update({ "wait_until": "load", "delay": 5 }) response = requests.get(api_url, params=params) if response.status_code == 200 and response.headers.get('content-type', '').startswith('image'): with open(filepath, 'wb') as f: f.write(response.content) print(f"Screenshot taken successfully after Cloudflare retry") return filepath return None except Exception as e: print(f"Error taking screenshot: {e}") return None def resize_if_needed(image_path, max_mb=1, target_height=720): file_size = os.path.getsize(image_path) / (1024 * 1024) # dalam MB if file_size > max_mb: try: with Image.open(image_path) as img: width, height = img.size if height > target_height: ratio = target_height / float(height) new_width = int(float(width) * ratio) img = img.resize((new_width, target_height), Image.Resampling.LANCZOS) img.save(image_path, optimize=True, quality=85) print(f"Image resized to {new_width}x{target_height}") except Exception as e: print(f"Resize error: {e}") def easyocr_extract(image_path): try: results = reader.readtext(image_path, detail=0) text = " ".join(results) print(f"OCR text extracted from EasyOCR: {len(text)} characters") return text.strip() except Exception as e: print(f"EasyOCR error: {e}") return "" # def extract_text_from_image(image_path): # print("Skipping OCR. Forcing Image-Only prediction.") # return "" def extract_text_from_image(image_path): try: resize_if_needed(image_path, max_mb=1, target_height=720) # Tambahkan ini di awal file_size = os.path.getsize(image_path) / (1024 * 1024) # ukuran MB if file_size < 1: print(f"Using OCR.Space API for image ({file_size:.2f} MB)") api_key = os.getenv("OCR_SPACE_API_KEY") if not api_key: print("OCR_SPACE_API_KEY not found in environment. Using EasyOCR as fallback.") return easyocr_extract(image_path) with open(image_path, 'rb') as f: payload = { 'isOverlayRequired': False, 'apikey': api_key, 'language': 'eng' } r = requests.post('https://api.ocr.space/parse/image', files={'filename': f}, data=payload) result = r.json() if result.get('IsErroredOnProcessing', False): print(f"OCR.Space API Error: {result.get('ErrorMessage')}") return easyocr_extract(image_path) text = result['ParsedResults'][0]['ParsedText'] print(f"OCR text extracted from OCR.Space: {len(text)} characters") return text.strip() else: print(f"Using EasyOCR for image ({file_size:.2f} MB)") return easyocr_extract(image_path) except Exception as e: print(f"OCR error: {e}") return "" def prepare_data_for_model(image_path, text): image = Image.open(image_path) image_tensor = transform(image).unsqueeze(0).to(device) clean_text_data = clean_text(text) encoding = tokenizer.encode_plus( clean_text_data, add_special_tokens=True, max_length=128, padding='max_length', truncation=True, return_tensors='pt' ) input_ids = encoding['input_ids'].to(device) attention_mask = encoding['attention_mask'].to(device) return image_tensor, input_ids, attention_mask def predict_single_url(url): print(f"Processing URL: {url}") screenshot_path = take_screenshot(url) if not screenshot_path: return f"❌ Error: Unable to capture screenshot for {url}. This may be due to:\n• Too many redirects\n• Website blocking automated access\n• Network connectivity issues\n• Invalid URL", "Screenshot capture failed", None, "", "" text = extract_text_from_image(screenshot_path) raw_text = text # Store raw text before cleaning if not text.strip(): # Jika text kosong print(f"No OCR text found for {url}. Using Image-Only Model.") image = Image.open(screenshot_path) image_tensor = transform(image).unsqueeze(0).to(device) with torch.no_grad(): image_logits = image_only_model(image_tensor).squeeze(1) image_probs = torch.sigmoid(image_logits) threshold = 0.6 is_gambling = image_probs[0] > threshold label = "Gambling" if is_gambling else "Non-Gambling" confidence = image_probs[0].item() if is_gambling else 1 - image_probs[0].item() print(f"[Image-Only] URL: {url}") print(f"Prediction: {label} | Confidence: {confidence:.2f}\n") return label, f"Confidence: {confidence:.2f} (Image-Only Model)", screenshot_path, raw_text, "" else: clean_text_data = clean_text(text) image_tensor, input_ids, attention_mask = prepare_data_for_model(screenshot_path, text) with torch.no_grad(): fused_logits, image_logits, text_logits, weights = fusion_model(image_tensor, input_ids, attention_mask) fused_probs = torch.sigmoid(fused_logits) image_probs = torch.sigmoid(image_logits) text_probs = torch.sigmoid(text_logits) threshold = 0.6 is_gambling = fused_probs[0] > threshold label = "Gambling" if is_gambling else "Non-Gambling" confidence = fused_probs[0].item() if is_gambling else 1 - fused_probs[0].item() # ✨ Log detail print(f"[Fusion Model] URL: {url}") print(f"Image Model Prediction Probability: {image_probs[0]:.2f}") print(f"Text Model Prediction Probability: {text_probs[0]:.2f}") print(f"Fusion Final Prediction: {label} | Confidence: {confidence:.2f}\n") return label, f"Confidence: {confidence:.2f} (Fusion Model)", screenshot_path, raw_text, clean_text_data def predict_batch_urls(file_obj): results = [] content = file_obj.read().decode('utf-8') urls = [line.strip() for line in content.splitlines() if line.strip()] for url in urls: label, confidence, screenshot_path, raw_text, cleaned_text = predict_single_url(url) results.append({ "url": url, "label": label, "confidence": confidence, "screenshot_path": screenshot_path, "raw_text": raw_text, "cleaned_text": cleaned_text }) df = pd.DataFrame(results) print(f"Batch prediction completed for {len(urls)} URLs.") return df # --- Gradio App --- with gr.Blocks() as app: gr.Markdown("# 🕵️ Gambling Website Detection (URL Based)") with gr.Tab("Single URL"): url_input = gr.Textbox(label="Enter Website URL") predict_button = gr.Button("Predict") with gr.Row(): with gr.Column(): label_output = gr.Label() confidence_output = gr.Textbox(label="Confidence", interactive=False) with gr.Column(): screenshot_output = gr.Image(label="Screenshot", type="filepath") with gr.Row(): with gr.Column(): raw_text_output = gr.Textbox(label="Raw OCR Text", lines=5) with gr.Column(): cleaned_text_output = gr.Textbox(label="Cleaned Text", lines=5) predict_button.click( fn=predict_single_url, inputs=url_input, outputs=[ label_output, confidence_output, screenshot_output, raw_text_output, cleaned_text_output ] ) with gr.Tab("Batch URLs"): file_input = gr.File(label="Upload .txt file with URLs (one per line)") batch_predict_button = gr.Button("Batch Predict") batch_output = gr.DataFrame() batch_predict_button.click(fn=predict_batch_urls, inputs=file_input, outputs=batch_output) app.launch()