app.py

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, AutoModel
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 TextModelWithClassifier(nn.Module):
    def __init__(self, base_model):
        super(TextModelWithClassifier, self).__init__()
        self.bert = base_model  # Use 'bert' to match saved state_dict keys
        self.classifier = nn.Linear(base_model.config.hidden_size, 1)
    
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs.pooler_output if hasattr(outputs, 'pooler_output') else outputs.last_hidden_state[:, 0]
        logits = self.classifier(pooled_output)
        return type('Output', (), {'logits': logits})()

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
        # MLP fusion layer (matching saved model structure)
        # Structure: Linear(2, hidden) -> ReLU -> Dropout -> Linear(hidden, 1)
        hidden_dim = 16  # Matching saved model: [16, 2] -> [16] -> [1, 16]
        self.fusion_mlp = nn.Sequential(
            nn.Linear(2, hidden_dim),  # layer 0: [16, 2]
            nn.ReLU(),                 # layer 1 (no params)
            nn.Dropout(0.1),           # layer 2 (no params)
            nn.Linear(hidden_dim, 1)   # layer 3: [1, 16]
        )

    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)

        # Stack logits and pass through MLP
        stacked_logits = torch.stack([image_logits, text_logits], dim=1)
        fused_logits = self.fusion_mlp(stacked_logits).squeeze(1)
        
        # For compatibility, create dummy weights
        weights = torch.tensor([0.5, 0.5], device=fused_logits.device)

        return fused_logits, image_logits, text_logits, weights

# Load Fusion Model
# Create model architecture first
image_model_for_fusion = models.efficientnet_b3(weights=models.EfficientNet_B3_Weights.DEFAULT)
num_features = image_model_for_fusion.classifier[1].in_features
# Match saved model structure: classifier.1 instead of classifier
image_model_for_fusion.classifier = nn.Sequential(
    nn.Dropout(p=0.3, inplace=True),
    nn.Linear(num_features, 1)
)

text_base_model = AutoModel.from_pretrained('indobenchmark/indobert-base-p1')
text_model = TextModelWithClassifier(text_base_model)

fusion_model = LateFusionModel(image_model_for_fusion, text_model)

# Load state_dict
model_path = "models/best_mlp_fusion_model_state_dict.pt"
if os.path.exists(model_path):
    state_dict = torch.load(model_path, map_location=device)
    try:
        fusion_model.load_state_dict(state_dict, strict=True)
        print("Fusion model loaded from local state_dict successfully!")
    except RuntimeError as e:
        print(f"Warning: Some keys didn't match. Trying with strict=False...")
        print(f"Error details: {str(e)[:500]}")
        fusion_model.load_state_dict(state_dict, strict=False)
        print("Fusion model loaded with strict=False (some keys may be missing)")
else:
    print("Fusion model not found locally. Downloading from Hugging Face Hub...")
    model_path = hf_hub_download(repo_id="azzandr/gambling-fusion-model", filename="best_mlp_fusion_model_state_dict.pt")
    state_dict = torch.load(model_path, map_location=device)
    try:
        fusion_model.load_state_dict(state_dict, strict=True)
        print("Fusion model downloaded and loaded successfully!")
    except RuntimeError as e:
        print(f"Warning: Some keys didn't match. Trying with strict=False...")
        print(f"Error details: {str(e)[:500]}")
        fusion_model.load_state_dict(state_dict, strict=False)
        print("Fusion model loaded with strict=False (some keys may be missing)")

fusion_model.to(device)
fusion_model.eval()
print("Fusion model ready!")

# 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:
        error_label = {"Error": 1.0, "Non-Gambling": 0.0, "Gambling": 0.0}
        error_msg = f"**Error:** Unable to capture screenshot for `{url}`\n\n**Possible reasons:**\n• Too many redirects\n• Website blocking automated access\n• Network connectivity issues\n• Invalid URL"
        return error_label, error_msg, None, "", "", "**Model:** Screenshot capture failed"

    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

        gambling_prob = image_probs[0].item()
        non_gambling_prob = 1 - gambling_prob
        
        label_dict = {
            "Gambling": gambling_prob,
            "Non-Gambling": non_gambling_prob
        }
        
        confidence = gambling_prob if is_gambling else non_gambling_prob
        confidence_md = f"**Confidence:** {confidence:.1%}\n\n**Model Used:** Image-Only Model (EfficientNet-B3)\n\n**Prediction:** {'Gambling' if is_gambling else 'Non-Gambling'}"
        
        model_info = f"**Model Type:** Image-Only\n**Architecture:** EfficientNet-B3\n**Gambling Probability:** {gambling_prob:.1%}\n**Non-Gambling Probability:** {non_gambling_prob:.1%}"
        
        print(f"[Image-Only] URL: {url}")
        print(f"Prediction: {'Gambling' if is_gambling else 'Non-Gambling'} | Confidence: {confidence:.2f}\n")
        return label_dict, confidence_md, screenshot_path, raw_text, "", model_info

    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

        gambling_prob = fused_probs[0].item()
        non_gambling_prob = 1 - gambling_prob
        
        label_dict = {
            "Gambling": gambling_prob,
            "Non-Gambling": non_gambling_prob
        }
        
        confidence = gambling_prob if is_gambling else non_gambling_prob
        
        # Calculate relative contribution (approximation for MLP fusion)
        image_contrib = abs(image_probs[0].item() - 0.5)
        text_contrib = abs(text_probs[0].item() - 0.5)
        total_contrib = image_contrib + text_contrib
        if total_contrib > 0:
            image_weight = image_contrib / total_contrib
            text_weight = text_contrib / total_contrib
        else:
            image_weight = 0.5
            text_weight = 0.5
        
        confidence_md = f"**Confidence:** {confidence:.1%}\n\n**Model Used:** Fusion Model (Image + Text)\n\n**Prediction:** {'Gambling' if is_gambling else 'Non-Gambling'}"
        
        model_info = f"""**Model Type:** Fusion Model (MLP)
**Image Model:** EfficientNet-B3
**Text Model:** IndoBERT

**Individual Predictions:**
- Image Model: {image_probs[0].item():.1%}
- Text Model: {text_probs[0].item():.1%}
- Fusion Result: {gambling_prob:.1%}"""

        # ✨ 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: {'Gambling' if is_gambling else 'Non-Gambling'} | Confidence: {confidence:.2f}\n")

        return label_dict, confidence_md, screenshot_path, raw_text, clean_text_data, model_info

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 ---

# Custom CSS for professional styling
custom_css = """
    .main-header {
        text-align: center;
        padding: 2rem 0;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        border-radius: 10px;
        margin-bottom: 2rem;
    }
    .main-header h1 {
        margin: 0;
        font-size: 2.5rem;
        font-weight: 700;
    }
    .main-header p {
        margin: 0.5rem 0 0 0;
        font-size: 1.1rem;
        opacity: 0.9;
    }
    .result-card {
        background: #f8f9fa;
        padding: 1.5rem;
        border-radius: 10px;
        border: 2px solid #e9ecef;
        margin: 1rem 0;
    }
    .info-box {
        background: #e7f3ff;
        padding: 1rem;
        border-radius: 8px;
        border-left: 4px solid #2196F3;
        margin: 1rem 0;
    }
    .success-box {
        background: #d4edda;
        border-left-color: #28a745;
    }
    .warning-box {
        background: #fff3cd;
        border-left-color: #ffc107;
    }
    .gradio-container {
        max-width: 1200px;
        margin: 0 auto;
    }
"""

with gr.Blocks(theme=gr.themes.Soft(), css=custom_css, title="Gambling Website Detector") as app:
    # Header Section
    with gr.Row():
        gr.HTML("""
            <div class="main-header">
                <h1>Gambling Website Detection System</h1>
                <p>AI-Powered URL Analysis using Deep Learning Fusion Model</p>
            </div>
        """)
    
    # Info Section
    with gr.Row():
        gr.Markdown("""
        ### About This Tool
        
        This advanced detection system uses a **fusion model** combining:
        - **Image Analysis**: EfficientNet-B3 for visual content detection
        - **Text Analysis**: IndoBERT for Indonesian text understanding
        - **Fusion Learning**: Intelligent combination of both modalities
        
        Simply enter a website URL to analyze whether it contains gambling-related content.
        """)
    
    with gr.Tabs():
        with gr.Tab("Single URL Analysis", id="single"):
            with gr.Row():
                with gr.Column(scale=2):
                    gr.Markdown("### Enter Website URL")
                    url_input = gr.Textbox(
                        label="Website URL",
                        placeholder="https://example.com",
                        info="Enter the full URL of the website you want to analyze",
                        lines=1
                    )
                    predict_button = gr.Button(
                        "Analyze Website",
                        variant="primary",
                        size="lg"
                    )
            
            gr.Markdown("---")
            
            # Results Section
            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown("### Detection Results")
                    label_output = gr.Label(
                        label="Prediction Result",
                        value={"Gambling": 0.0, "Non-Gambling": 0.0},
                        num_top_classes=2
                    )
                    confidence_output = gr.Markdown(
                        value="**Confidence:** Waiting for analysis...",
                        label="Confidence Score"
                    )
                    model_info_output = gr.Markdown(
                        value="",
                        label="Model Information"
                    )
                
                with gr.Column(scale=1):
                    gr.Markdown("### Website Screenshot")
                    screenshot_output = gr.Image(
                        label="Captured Screenshot",
                        type="filepath",
                        height=400
                    )
            
            gr.Markdown("---")
            
            # Text Analysis Section
            with gr.Accordion("Text Analysis Details", open=False):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("#### Raw OCR Text")
                        raw_text_output = gr.Textbox(
                            label="Extracted Text (Raw)",
                            lines=8,
                            interactive=False,
                            placeholder="Raw text extracted from the screenshot will appear here..."
                        )
                    with gr.Column():
                        gr.Markdown("#### Processed Text")
                        cleaned_text_output = gr.Textbox(
                            label="Cleaned Text (Processed)",
                            lines=8,
                            interactive=False,
                            placeholder="Processed and cleaned text will appear here..."
                        )

            predict_button.click(
                fn=predict_single_url, 
                inputs=url_input, 
                outputs=[
                    label_output, 
                    confidence_output, 
                    screenshot_output,
                    raw_text_output,
                    cleaned_text_output,
                    model_info_output
                ]
            )

        with gr.Tab("Batch URL Analysis", id="batch"):
            gr.Markdown("""
            ### Batch Processing
            
            Upload a text file containing multiple URLs (one per line) to analyze them all at once.
            The results will be displayed in a table format.
            """)
            
            with gr.Row():
                with gr.Column():
                    file_input = gr.File(
                        label="Upload URL File (.txt)",
                        file_types=[".txt"]
                    )
                    gr.Markdown("**Tip:** Upload a .txt file with one URL per line")
                    batch_predict_button = gr.Button(
                        "Process Batch",
                        variant="primary",
                        size="lg"
                    )
            
            gr.Markdown("---")
            
            with gr.Row():
                gr.Markdown("### Batch Results")
                batch_output = gr.DataFrame(
                    label="Analysis Results",
                    wrap=True,
                    interactive=False
                )
            
            batch_predict_button.click(
                fn=predict_batch_urls, 
                inputs=file_input, 
                outputs=batch_output
            )
    
    # Footer
    gr.Markdown("---")
    gr.Markdown("""
    <div style="text-align: center; color: #666; padding: 1rem;">
        <p>Powered by PyTorch • Gradio • EfficientNet • IndoBERT</p>
        <p style="font-size: 0.9rem;">This tool is for educational and research purposes only</p>
    </div>
    """)

app.launch()