app.py

import streamlit as st
import warnings
import os
import tempfile
# First load unsloth
from unsloth import FastVisionModel
# Completely disable dynamic compilation due to compatibility issues
import torch
# Disable TorchDynamo completely to avoid optimization errors
torch._dynamo.config.disable = True
# Disable fallback warnings to reduce noise
torch._dynamo.config.suppress_errors = True
# Then transformers
from transformers import BlipProcessor, BlipForConditionalGeneration
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms
from PIL import Image
import numpy as np
import io
import base64
import cv2
import matplotlib.pyplot as plt
from peft import PeftModel
from gradcam_xception import generate_smoothgrad_visualizations_xception
warnings.filterwarnings("ignore", category=UserWarning)

# Xception transform is now defined directly in preprocess_image_xception

# App title and description
st.set_page_config(
    page_title="Deepfake Analyzer", 
    layout="wide",
    page_icon="🔍"
)

# Debug logging
debug_mode = False
if "debug" not in st.session_state:
    st.session_state.debug = debug_mode

# Add debug toggle in sidebar
with st.sidebar:
    st.session_state.debug = st.toggle("Enable Debug Mode", value=debug_mode, key="debug_toggle_sidebar")

# Add after existing debug mode toggle in sidebar
with st.sidebar:
    if st.session_state.debug:
        st.write("### Connection Diagnostics")
        if st.button("Test File Upload Connection"):
            try:
                # Create a simple test file in memory
                import io
                test_file = io.BytesIO(b"test content")
                test_file.name = "test.txt"
                
                # Test the Streamlit file uploader connection
                st.write("Checking file upload capability...")
                st.write("Status: Testing... If this freezes, there may be connectivity issues.")
                
                # Check basic file operations
                test_path = "test_upload_capability.txt"
                try:
                    with open(test_path, "w") as f:
                        f.write("test")
                    st.write("✅ File write test: Success")
                    import os
                    os.remove(test_path)
                    st.write("✅ File delete test: Success")
                except Exception as e:
                    st.write(f"❌ File operation test: Failed - {str(e)}")
                
                # Check Streamlit session state
                try:
                    st.session_state.test_value = "test"
                    if st.session_state.test_value == "test":
                        st.write("✅ Session state test: Success")
                except Exception as e:
                    st.write(f"❌ Session state test: Failed - {str(e)}")
                
                # Environment variables check
                import os
                st.write("### Environment Variables")
                for key in ["STREAMLIT_SERVER_ENABLE_CORS", "STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION", 
                            "TEMP", "TMP", "TMPDIR"]:
                    st.write(f"{key}: {os.environ.get(key, 'Not set')}")
                
                # Check for specific Hugging Face Spaces environment variables
                hf_vars = [k for k in os.environ if k.startswith("HF_")]
                if hf_vars:
                    st.write("### Hugging Face Environment Variables")
                    for key in hf_vars:
                        st.write(f"{key}: {os.environ.get(key, 'Not set')}")
                
                st.success("Diagnostics completed!")
            except Exception as e:
                st.error(f"Diagnostics error: {str(e)}")
                import traceback
                st.error(traceback.format_exc())

def log_debug(message):
    """Helper function to log debug messages only when debug mode is enabled"""
    if st.session_state.debug:
        st.sidebar.write(f"DEBUG: {message}")

# Function to check environment
def check_environment():
    import sys
    import platform
    
    if st.session_state.debug:
        st.sidebar.write("### Environment Info")
        st.sidebar.write(f"Python version: {sys.version}")
        st.sidebar.write(f"Platform: {platform.platform()}")
        try:
            import torch
            st.sidebar.write(f"Torch version: {torch.__version__}")
            st.sidebar.write(f"CUDA available: {torch.cuda.is_available()}")
            if torch.cuda.is_available():
                st.sidebar.write(f"CUDA version: {torch.version.cuda}")
                st.sidebar.write(f"GPU: {torch.cuda.get_device_name(0)}")
        except:
            st.sidebar.write("Torch not available or error checking")

# Test Hugging Face Hub connectivity
def test_huggingface_hub_access():
    """Test connectivity to the Hugging Face Hub"""
    try:
        from huggingface_hub import HfApi
        api = HfApi()
        
        # Try to get info for a public model
        model_info = api.model_info("openai/clip-vit-base-patch32")
        
        # If we get here, access worked
        st.sidebar.success("✅ Hugging Face Hub connectivity: Good")
        return True
    except Exception as e:
        st.sidebar.error(f"⚠️ Hugging Face Hub connectivity issue: {str(e)}")
        if st.session_state.debug:
            import traceback
            st.sidebar.error(traceback.format_exc())
        return False

# Run environment check first
check_environment()

# Run Hugging Face Hub connectivity test if debug is enabled
if st.session_state.debug:
    try:
        test_huggingface_hub_access()
    except Exception as e:
        st.sidebar.error(f"Error testing HuggingFace Hub: {str(e)}")
        log_debug(f"HF Hub test error: {str(e)}")

# Main title and description
st.title("Deepfake Image Analyser")
st.markdown("Analyse images for deepfake manipulation")

# Check for GPU availability
def check_gpu():
    if torch.cuda.is_available():
        gpu_info = torch.cuda.get_device_properties(0)
        st.sidebar.success(f"✅ GPU available: {gpu_info.name} ({gpu_info.total_memory / (1024**3):.2f} GB)")
        return True
    else:
        st.sidebar.warning("⚠️ No GPU detected. Analysis will be slower.")
        return False

# Sidebar components
st.sidebar.title("Model Controls")

# Model loading buttons in sidebar
with st.sidebar:
    st.write("### Load Models")
    
    # Xception model loading
    if 'xception_model_loaded' not in st.session_state:
        st.session_state.xception_model_loaded = False
        st.session_state.xception_model = None
    
    if not st.session_state.xception_model_loaded:
        if st.button("📥 Load Xception Model", type="primary"):
            # Load Xception model
            try:
                from gradcam_xception import load_xception_model
                model = load_xception_model()
                device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
                
                # Explicitly move model to device
                model = model.to(device)
                
                if model is not None:
                    st.session_state.xception_model = model
                    st.session_state.device = device
                    st.session_state.xception_model_loaded = True
                    st.success("✅ Xception model loaded!")
                else:
                    st.error("❌ Failed to load Xception model.")
            except Exception as e:
                st.error(f"Error loading model: {str(e)}")
    else:
        st.success("✅ Xception model loaded")
    
    # BLIP model loading
    if 'blip_model_loaded' not in st.session_state:
        st.session_state.blip_model_loaded = False
        st.session_state.original_processor = None
        st.session_state.original_model = None
        st.session_state.finetuned_processor = None
        st.session_state.finetuned_model = None
    
    if not st.session_state.blip_model_loaded:
        if st.button("📥 Load BLIP Models", type="primary"):
            # Load BLIP models
            try:
                with st.spinner("Loading BLIP captioning models..."):
                    # Load original BLIP model for general image captioning
                    original_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
                    original_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
                    
                    # Load fine-tuned BLIP model for GradCAM analysis
                    finetuned_processor = BlipProcessor.from_pretrained("saakshigupta/gradcam-xception-finetuned")
                    finetuned_model = BlipForConditionalGeneration.from_pretrained("saakshigupta/gradcam-xception-finetuned")
                    
                    if all([original_processor, original_model, finetuned_processor, finetuned_model]):
                        st.session_state.original_processor = original_processor
                        st.session_state.original_model = original_model
                        st.session_state.finetuned_processor = finetuned_processor
                        st.session_state.finetuned_model = finetuned_model
                        st.session_state.blip_model_loaded = True
                        st.success("✅ BLIP models loaded!")
                    else:
                        st.error("❌ Failed to load BLIP models.")
            except Exception as e:
                st.error(f"Error loading BLIP models: {str(e)}")
    else:
        st.success("✅ BLIP models loaded")
    
    # LLM model loading
    if 'llm_model_loaded' not in st.session_state:
        st.session_state.llm_model_loaded = False
        st.session_state.llm_model = None
        st.session_state.tokenizer = None
    
    if not st.session_state.llm_model_loaded:
        if st.button("📥 Load Vision LLM", type="primary"):
            # Load LLM model
            try:
                with st.spinner("Loading LLM vision model... This may take a few minutes. Please be patient..."):
                    # Check for GPU
                    has_gpu = check_gpu()
                    
                    # Load base model and tokenizer using Unsloth
                    base_model_id = "unsloth/llama-3.2-11b-vision-instruct"
                    model, tokenizer = FastVisionModel.from_pretrained(
                        base_model_id,
                        load_in_4bit=True,
                    )

                    # Load the adapter
                    adapter_id = "saakshigupta/deepfake-explainer-new"
                    model = PeftModel.from_pretrained(model, adapter_id)

                    # Set to inference mode
                    FastVisionModel.for_inference(model)
                    
                    if model is not None and tokenizer is not None:
                        st.session_state.llm_model = model
                        st.session_state.tokenizer = tokenizer
                        st.session_state.llm_model_loaded = True
                        st.success("✅ Vision LLM loaded!")
                    else:
                        st.error("❌ Failed to load Vision LLM.")
            except Exception as e:
                st.error(f"Error loading LLM model: {str(e)}")
    else:
        st.success("✅ Vision LLM loaded")
    
    # Display model info

# Fixed values for temperature and max tokens
temperature = 0.7
max_tokens = 500

# Define empty custom_instruction to maintain compatibility
custom_instruction = ""

# ----- GradCAM Implementation for Xception -----
class ImageDataset(torch.utils.data.Dataset):
    def __init__(self, image, transform=None, face_only=True, dataset_name=None):
        self.image = image
        self.transform = transform
        self.face_only = face_only
        self.dataset_name = dataset_name
        # Load face detector
        self.face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
        
    def __len__(self):
        return 1  # Only one image

    def detect_face(self, image_np):
        """Detect face in image and return the face region"""
        gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
        faces = self.face_detector.detectMultiScale(gray, 1.1, 5)
        
        # If no face is detected, use the whole image
        if len(faces) == 0:
            st.info("No face detected, using whole image for analysis")
            h, w = image_np.shape[:2]
            return (0, 0, w, h), image_np
            
        # Get the largest face
        if len(faces) > 1:
            # Choose the largest face by area
            areas = [w*h for (x, y, w, h) in faces]
            largest_idx = np.argmax(areas)
            x, y, w, h = faces[largest_idx]
        else:
            x, y, w, h = faces[0]
            
        # Add padding around the face (5% on each side)
        padding_x = int(w * 0.05)
        padding_y = int(h * 0.05)
        
        # Ensure padding doesn't go outside image bounds
        x1 = max(0, x - padding_x)
        y1 = max(0, y - padding_y)
        x2 = min(image_np.shape[1], x + w + padding_x)
        y2 = min(image_np.shape[0], y + h + padding_y)
        
        # Extract the face region
        face_img = image_np[y1:y2, x1:x2]
        
        return (x1, y1, x2-x1, y2-y1), face_img

    def __getitem__(self, idx):
        image_np = np.array(self.image)
        label = 0  # Default label; will be overridden by prediction
        
        # Store original image for visualization
        original_image = self.image.copy()
        
        # Detect face if required
        if self.face_only:
            face_box, face_img_np = self.detect_face(image_np)
            face_img = Image.fromarray(face_img_np)
            
            # Apply transform to face image
            IMAGE_SIZE = 299
            if self.transform:
                face_tensor = self.transform(face_img)
            else:
                # Use default transform if none provided
                transform = transforms.Compose([
                    transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
                ])
                face_tensor = transform(face_img)
            
            return face_tensor, label, "uploaded_image", original_image, face_box, self.dataset_name
        else:
            # Process the whole image
            IMAGE_SIZE = 299
            if self.transform:
                image_tensor = self.transform(self.image)
            else:
                # Use default transform if none provided
                transform = transforms.Compose([
                    transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
                    transforms.ToTensor(),
                    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
                ])
                image_tensor = transform(self.image)
                
            return image_tensor, label, "uploaded_image", original_image, None, self.dataset_name

# Function to process image with Xception GradCAM
def process_image_with_xception_gradcam(image, model, device, pred_class):
    """Process an image with Xception GradCAM"""
    cam_results = generate_smoothgrad_visualizations_xception(
        model=model,
        image=image,
        target_class=pred_class,
        face_only=True,
        num_samples=5  # Can be adjusted
    )
    
    if cam_results and len(cam_results) == 4:
        raw_cam, cam_img, overlay, comparison = cam_results
        
        # Extract the face box from the dataset if needed
        IMAGE_SIZE = 299
        transform = transforms.Compose([
            transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
        ])
        dataset = ImageDataset(image, transform=transform, face_only=True)
        _, _, _, _, face_box, _ = dataset[0]
        
        return raw_cam, overlay, comparison, face_box
    else:
        st.error("Failed to generate GradCAM visualization")
        return None, None, None, None

# ----- Xception Model Loading -----
@st.cache_resource
def load_detection_model_xception():
    """Loads the Xception model from HF Hub."""
    with st.spinner("Loading Xception model for deepfake detection..."):
        try:
            log_debug("Beginning Xception model loading")
            from gradcam_xception import load_xception_model
            log_debug("Loading Xception model (this may take a moment)...")
            model = load_xception_model()
            
            # Get the device
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            log_debug(f"Using device: {device}")
            
            model.to(device)
            model.eval()
            log_debug(f"Xception model loaded to {device}.")
            return model, device
        except ImportError as e:
            st.error(f"Import Error: {str(e)}. Make sure gradcam_xception.py is present.")
            log_debug("Import error with gradcam_xception.py module")
            return None, None
        except Exception as e:
            st.error(f"Error loading Xception model: {str(e)}")
            import traceback
            error_details = traceback.format_exc()
            if st.session_state.debug:
                st.error(error_details)
            log_debug(f"Error details: {error_details}")
            return None, None

# ----- BLIP Image Captioning -----

# Function to generate image caption using BLIP's VQA approach for GradCAM
def generate_gradcam_caption(image, processor, model, max_length=60):
    """
    Generate a detailed analysis of GradCAM visualization using the fine-tuned BLIP model
    """
    try:
        # Process image first
        inputs = processor(image, return_tensors="pt")
        
        # Check for available GPU and move model and inputs
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model = model.to(device)
        inputs = {k: v.to(device) if hasattr(v, 'to') else v for k, v in inputs.items()}
        
        # Generate caption
        with torch.no_grad():
            output = model.generate(**inputs, max_length=max_length, num_beams=5)
            
        # Decode the output
        caption = processor.decode(output[0], skip_special_tokens=True)
        
        # Try to parse the caption based on different possible formats
        try:
            # Original format with "high activation:" etc.
            formatted_text = ""
            if "high activation :" in caption:
                high_match = caption.split("high activation :")[1].split("moderate")[0]
                formatted_text += f"**High activation**:\n{high_match.strip()}\n\n"
            
            if "moderate activation :" in caption:
                moderate_match = caption.split("moderate activation :")[1].split("low")[0]
                formatted_text += f"**Moderate activation**:\n{moderate_match.strip()}\n\n"
            
            if "low activation :" in caption:
                low_match = caption.split("low activation :")[1]
                formatted_text += f"**Low activation**:\n{low_match.strip()}"
            
            # If nothing was extracted using the original format, try alternative formats
            if not formatted_text.strip():
                # Check for newer format that might be in the Xception model
                if ":" in caption:
                    parts = caption.split(":")
                    if len(parts) > 1:
                        formatted_text = f"**GradCAM Analysis**:\n{parts[1].strip()}"
                else:
                    # As a fallback, just use the entire caption
                    formatted_text = f"**GradCAM Analysis**:\n{caption.strip()}"
        except Exception as parsing_error:
            # Use the entire caption as is
            formatted_text = f"**GradCAM Analysis**:\n{caption.strip()}"
            
        return formatted_text.strip()
        
    except Exception as e:
        st.error(f"Error analyzing GradCAM: {str(e)}")
        import traceback
        st.error(traceback.format_exc())
        return "Error analyzing GradCAM visualization"

# Function to generate caption for original image
def generate_image_caption(image, processor, model, max_length=75, num_beams=5):
    """Generate a caption for the original image using the original BLIP model"""
    try:
        # Check for available GPU
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model = model.to(device)
        
        # For original image, use unconditional captioning
        inputs = processor(image, return_tensors="pt").to(device)
        
        # Generate caption
        with torch.no_grad():
            output = model.generate(**inputs, max_length=max_length, num_beams=num_beams)
            
        # Decode the output
        caption = processor.decode(output[0], skip_special_tokens=True)
        
        # Format into structured description
        structured_caption = f"""
**Subject**: The image shows a person in a photograph.

**Appearance**: {caption}

**Background**: The background appears to be a controlled environment.

**Lighting**: The lighting appears to be professional with even illumination.

**Colors**: The image contains natural skin tones and colors typical of photography.

**Notable Elements**: The facial features and expression are the central focus of the image.
"""
        return structured_caption.strip()
            
    except Exception as e:
        st.error(f"Error generating caption: {str(e)}")
        return "Error generating caption"

# ----- Fine-tuned Vision LLM -----

# Function to fix cross-attention masks
def fix_cross_attention_mask(inputs):
    if 'cross_attention_mask' in inputs and 0 in inputs['cross_attention_mask'].shape:
        batch_size, seq_len, _, num_tiles = inputs['cross_attention_mask'].shape
        visual_features = 6404  # Critical dimension
        new_mask = torch.ones((batch_size, seq_len, visual_features, num_tiles),
                            device=inputs['cross_attention_mask'].device)
        inputs['cross_attention_mask'] = new_mask
    return inputs

# Analyze image function
def analyze_image_with_llm(image, gradcam_overlay, face_box, pred_label, confidence, question, model, tokenizer, temperature=0.7, max_tokens=500, custom_instruction=""):
    # Create a prompt that includes GradCAM information
    if custom_instruction.strip():
        full_prompt = f"{question}\n\nThe image has been processed with GradCAM and classified as {pred_label} with confidence {confidence:.2f}. Focus on the highlighted regions in red/yellow which show the areas the detection model found suspicious.\n\n{custom_instruction}"
    else:
        full_prompt = f"{question}\n\nThe image has been processed with GradCAM and classified as {pred_label} with confidence {confidence:.2f}. Focus on the highlighted regions in red/yellow which show the areas the detection model found suspicious."
    
    try:
        # Format the message to include all available images
        message_content = [{"type": "text", "text": full_prompt}]

        # Add original image
        message_content.insert(0, {"type": "image", "image": image})

        # Add GradCAM overlay
        message_content.insert(1, {"type": "image", "image": gradcam_overlay})

        # Add comparison image if available
        if hasattr(st.session_state, 'comparison_image'):
            message_content.insert(2, {"type": "image", "image": st.session_state.comparison_image})

        messages = [{"role": "user", "content": message_content}]

        # Apply chat template
        input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

        # Create list of images to process
        image_list = [image, gradcam_overlay]
        if hasattr(st.session_state, 'comparison_image'):
            image_list.append(st.session_state.comparison_image)

        try:
            # Try with multiple images first
            inputs = tokenizer(
                image_list,
                input_text,
                add_special_tokens=False,
                return_tensors="pt",
            ).to(model.device)
        except Exception as e:
            st.warning(f"Multiple image analysis encountered an issue: {str(e)}")
            st.info("Falling back to single image analysis")
            # Fallback to single image
            inputs = tokenizer(
                image,
                input_text,
                add_special_tokens=False,
                return_tensors="pt",
            ).to(model.device)

        # Fix cross-attention mask if needed
        inputs = fix_cross_attention_mask(inputs)

        # Generate response with error handling
        with st.spinner("Generating detailed analysis... (this may take 15-30 seconds)"):
            with torch.no_grad():
                output_ids = model.generate(
                    **inputs,
                    max_new_tokens=max_tokens,
                    use_cache=True,
                    temperature=temperature,
                    top_p=0.9
                )

            # Decode the output
            response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
            
            # Try to extract just the model's response (after the prompt)
            if full_prompt in response:
                result = response.split(full_prompt)[-1].strip()
            else:
                result = response
                
            return result

    except Exception as e:
        st.error(f"Error during LLM analysis: {str(e)}")
        
        # Try one more time with simpler input
        try:
            st.info("Attempting fallback with simplified input...")
            
            # Prepare a simpler prompt
            simple_message = [{"role": "user", "content": [
                {"type": "text", "text": "Analyze this image and tell if it's a deepfake."},
                {"type": "image", "image": image}
            ]}]
            
            # Apply simpler template
            simple_text = tokenizer.apply_chat_template(simple_message, add_generation_prompt=True)
            
            # Generate with minimal settings
            with torch.no_grad():
                simple_inputs = tokenizer(
                    image,
                    simple_text,
                    add_special_tokens=False,
                    return_tensors="pt",
                ).to(model.device)
                
                simple_inputs = fix_cross_attention_mask(simple_inputs)
                
                output_ids = model.generate(
                    **simple_inputs,
                    max_new_tokens=200,
                    use_cache=True,
                    temperature=0.5,
                    top_p=0.9
                )
                
            # Decode
            fallback_response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
            return "Error with primary analysis. Fallback result: " + fallback_response.split("Analyze this image and tell if it's a deepfake.")[-1].strip()
        except Exception as fallback_error:
            return f"Error analyzing image: {str(fallback_error)}"

# Preprocess image for Xception
def preprocess_image_xception(image):
    """Preprocesses image for Xception model input and face detection."""
    try:
        log_debug("Starting image preprocessing for Xception model")
        face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
        
        # Ensure image is in correct format
        if image is None:
            log_debug("Image is None - this should never happen!")
            return None, None, None
        
        # Convert to numpy array for processing
        image_np = np.array(image.convert('RGB'))
        
        # Face detection
        gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
        faces = face_detector.detectMultiScale(gray, 1.1, 5)

        face_img_for_transform = image  # Default to whole image
        face_box_display = None  # For drawing on original image

        if len(faces) == 0:
            log_debug("No face detected in the image, using whole image")
            st.warning("No face detected, using whole image for prediction/CAM.")
        else:
            log_debug(f"Detected {len(faces)} faces in the image")
            areas = [w * h for (x, y, w, h) in faces]
            largest_idx = np.argmax(areas)
            x, y, w, h = faces[largest_idx]
            
            padding_x = int(w * 0.05)  # Use percentages as in gradcam_xception
            padding_y = int(h * 0.05)
            x1, y1 = max(0, x - padding_x), max(0, y - padding_y)
            x2, y2 = min(image_np.shape[1], x + w + padding_x), min(image_np.shape[0], y + h + padding_y)

            # Use the padded face region for the model transform
            face_img_for_transform = Image.fromarray(image_np[y1:y2, x1:x2])
            # Use the original detected box (without padding) for display rectangle
            face_box_display = (x, y, w, h)
            log_debug(f"Face detected: Box {face_box_display}")

        # Xception specific transform
        IMAGE_SIZE = 299
        transform = transforms.Compose([
            transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), # Standard Xception norm
        ])
        # Apply transform to the selected region (face or whole image)
        input_tensor = transform(face_img_for_transform).unsqueeze(0)

        # Return tensor, original full image, and the display face box
        return input_tensor, image, face_box_display
        
    except Exception as e:
        st.error(f"Error in preprocessing image: {str(e)}")
        import traceback
        log_debug(f"Preprocessing error details: {traceback.format_exc()}")
            
        # Return None values to indicate failure
        return None, None, None

# Main app
def main():
    # Initialize session state variables if not present
    if 'xception_model_loaded' not in st.session_state:
        st.session_state.xception_model_loaded = False
        st.session_state.xception_model = None
        
    if 'llm_model_loaded' not in st.session_state:
        st.session_state.llm_model_loaded = False
        st.session_state.llm_model = None
        st.session_state.tokenizer = None
    
    if 'blip_model_loaded' not in st.session_state:
        st.session_state.blip_model_loaded = False
        st.session_state.original_processor = None
        st.session_state.original_model = None
        st.session_state.finetuned_processor = None
        st.session_state.finetuned_model = None
    
    # Initialize chat history
    if 'chat_history' not in st.session_state:
        st.session_state.chat_history = []
    
    # Create multi-tab interface
    tab1, tab2, tab3 = st.tabs(["Deepfake Detection", "Image Captions", "LLM Analysis"])
    
    # Tab 1: Deepfake Detection
    with tab1:
        st.header("Deepfake Detection")
        
        # Image upload section
        st.subheader("Upload an Image")
        
        # Add alternative upload methods
        upload_tab1, upload_tab2 = st.tabs(["File Upload", "URL Input"])
        
        uploaded_image = None
        
        with upload_tab1:
            uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
            if uploaded_file is not None:
                try:
                    # Simple direct approach - load the image directly
                    image = Image.open(uploaded_file).convert("RGB")
                    uploaded_image = image
                    st.session_state.upload_method = "file"
                except Exception as e:
                    st.error(f"Error loading image: {str(e)}")
                    import traceback
                    st.error(traceback.format_exc())
        
        with upload_tab2:
            url = st.text_input("Enter image URL:")
            if url and url.strip():
                try:
                    import requests
                    # Simplified URL handling with more robust approach
                    headers = {
                        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
                        'Accept': 'image/jpeg, image/png, image/*, */*',
                        'Referer': 'https://huggingface.co/'
                    }
                    
                    # Try three different methods to handle various API restrictions
                    try_methods = True
                    
                    # Method 1: Direct requests
                    if try_methods:
                        try:
                            response = requests.get(url, stream=True, headers=headers, timeout=10)
                            if response.status_code == 200 and 'image' in response.headers.get('Content-Type', ''):
                                try:
                                    image = Image.open(io.BytesIO(response.content)).convert("RGB")
                                    uploaded_image = image
                                    st.session_state.upload_method = "url_direct"
                                    try_methods = False
                                    st.success("✅ Image loaded via direct request")
                                except Exception as e:
                                    st.warning(f"Direct method received data but couldn't process as image: {str(e)}")
                            else:
                                st.info(f"Direct method failed: Status {response.status_code}, trying alternative method...")
                        except Exception as e:
                            st.info(f"Direct method error: {str(e)}, trying alternative method...")
                    
                    # Method 2: Use Python's urllib as fallback
                    if try_methods:
                        try:
                            import urllib.request
                            from urllib.error import HTTPError
                            
                            opener = urllib.request.build_opener()
                            opener.addheaders = [('User-agent', headers['User-Agent'])]
                            urllib.request.install_opener(opener)
                            
                            with urllib.request.urlopen(url, timeout=10) as response:
                                image_data = response.read()
                                image = Image.open(io.BytesIO(image_data)).convert("RGB")
                                uploaded_image = image
                                st.session_state.upload_method = "url_urllib"
                                try_methods = False
                                st.success("✅ Image loaded via urllib")
                        except HTTPError as e:
                            st.info(f"urllib method failed: HTTP error {e.code}, trying next method...")
                        except Exception as e:
                            st.info(f"urllib method error: {str(e)}, trying next method...")
                    
                    # Method 3: Use a proxy service as last resort
                    if try_methods:
                        try:
                            # This uses an image proxy service to bypass CORS issues
                            # Only as last resort since it depends on external service
                            proxy_url = f"https://images.weserv.nl/?url={url}"
                            response = requests.get(proxy_url, stream=True, timeout=10)
                            if response.status_code == 200:
                                image = Image.open(io.BytesIO(response.content)).convert("RGB")
                                uploaded_image = image
                                st.session_state.upload_method = "url_proxy"
                                try_methods = False
                                st.success("✅ Image loaded via proxy service")
                            else:
                                st.error(f"All methods failed to load the image from URL. Last status: {response.status_code}")
                        except Exception as e:
                            st.error(f"All methods failed. Final error: {str(e)}")
                    
                    if not uploaded_image:
                        st.error("Failed to load image using all available methods.")
                        
                except Exception as e:
                    st.error(f"Error processing URL: {str(e)}")
                    if st.session_state.debug:
                        import traceback
                        st.error(traceback.format_exc())
        
        # If we have an uploaded image, process it
        if uploaded_image is not None:
            # Display the image
            image = uploaded_image
            col1, col2 = st.columns([1, 2])
            with col1:
                st.image(image, caption="Uploaded Image", width=300)
            
            # Continue with Xception model analysis
            if st.session_state.xception_model_loaded:
                try:
                    with st.spinner("Analyzing image with Xception model..."):
                        # Preprocess image for Xception
                        input_tensor, original_image, face_box = preprocess_image_xception(image)
                        
                        if input_tensor is None:
                            st.error("Failed to preprocess image. Please try another image.")
                            st.stop()
                            
                        # Get device and model
                        device = st.session_state.device
                        model = st.session_state.xception_model
                        
                        # Ensure model is in eval mode and on the correct device
                        model = model.to(device)
                        model.eval()
                        
                        # Move tensor to device
                        input_tensor = input_tensor.to(device)
                        
                        # Forward pass with proper error handling
                        try:
                            with torch.no_grad():
                                logits = model(input_tensor)
                                probabilities = torch.softmax(logits, dim=1)[0]
                                pred_class = torch.argmax(probabilities).item()
                                confidence = probabilities[pred_class].item()
                                
                                # Explicit class mapping - 0 = Real, 1 = Fake
                                pred_label = "Real" if pred_class == 0 else "Fake"
                        except Exception as e:
                            st.error(f"Error in model inference: {str(e)}")
                            import traceback
                            st.error(traceback.format_exc())
                            # Set default values
                            pred_class = 0
                            confidence = 0.5
                            pred_label = "Error in prediction"
                        
                        # Display results
                        with col2:
                            st.markdown("### Detection Result")
                            st.markdown(f"**Classification:** {pred_label} (Confidence: {confidence:.2%})")
                        
                        # GradCAM visualization with error handling
                        st.subheader("GradCAM Visualization")
                        try:
                            cam, overlay, comparison, detected_face_box = process_image_with_xception_gradcam(
                                image, model.to(device), device, pred_class
                            )
                            
                            if comparison:
                                # Display GradCAM results (controlled size)
                                st.image(comparison, caption="Original | CAM | Overlay", width=700)
                                
                                # Save for later use
                                st.session_state.comparison_image = comparison
                            else:
                                st.error("GradCAM visualization failed - comparison image not generated")
                        
                            # Generate caption for GradCAM overlay image if BLIP model is loaded
                            if st.session_state.blip_model_loaded and overlay:
                                with st.spinner("Analyzing GradCAM visualization..."):
                                    gradcam_caption = generate_gradcam_caption(
                                        overlay,
                                        st.session_state.finetuned_processor,
                                        st.session_state.finetuned_model
                                    )
                                    st.session_state.gradcam_caption = gradcam_caption
                                    
                                    # Remove the display from Detection tab
                                    # Keep only saving to session state for use in Image Captions tab
                        except Exception as e:
                            st.error(f"Error generating GradCAM: {str(e)}")
                            import traceback
                            st.error(traceback.format_exc())
                        
                        # Save results in session state for use in other tabs
                        st.session_state.current_image = image
                        st.session_state.current_overlay = overlay if 'overlay' in locals() else None
                        st.session_state.current_face_box = detected_face_box if 'detected_face_box' in locals() else None
                        st.session_state.current_pred_label = pred_label
                        st.session_state.current_confidence = confidence
                        
                        st.success("✅ Initial detection and GradCAM visualization complete!")
                except Exception as e:
                    st.error(f"Overall error in Xception processing: {str(e)}")
                    import traceback
                    st.error(traceback.format_exc())
            else:
                st.warning("⚠️ Please load the Xception model from the sidebar first.")
    
    # Tab 2: Image Captions with BLIP models
    with tab2:
        st.header("Image Captions")
        
        # Image Caption Display
        if hasattr(st.session_state, 'current_image'):
            col1, col2 = st.columns([1, 2])
            
            with col1:
                st.image(st.session_state.current_image, caption="Original Image", width=300)
                
                if hasattr(st.session_state, 'current_overlay'):
                    st.image(st.session_state.current_overlay, caption="GradCAM Visualization", width=300)
            
            with col2:
                if not st.session_state.blip_model_loaded:
                    st.warning("⚠️ Please load the BLIP models from the sidebar first.")
                else:
                    # Button to generate captions if not already generated
                    if not hasattr(st.session_state, 'image_caption') or st.button("Regenerate Image Caption"):
                        with st.spinner("Generating image description..."):
                            caption = generate_image_caption(
                                st.session_state.current_image, 
                                st.session_state.original_processor, 
                                st.session_state.original_model
                            )
                            st.session_state.image_caption = caption
                    
                    # Display original image caption
                    if hasattr(st.session_state, 'image_caption'):
                        st.markdown("### Image Description")
                        st.markdown(st.session_state.image_caption)
                        st.markdown("---")
                    
                    # Display GradCAM caption if available
                    if hasattr(st.session_state, 'gradcam_caption'):
                        st.markdown("### GradCAM Analysis")
                        st.markdown(st.session_state.gradcam_caption)
                        
                        # Button to regenerate GradCAM caption
                        if hasattr(st.session_state, 'current_overlay') and st.button("Regenerate GradCAM Caption"):
                            with st.spinner("Reanalyzing GradCAM visualization..."):
                                gradcam_caption = generate_gradcam_caption(
                                    st.session_state.current_overlay,
                                    st.session_state.finetuned_processor,
                                    st.session_state.finetuned_model
                                )
                                st.session_state.gradcam_caption = gradcam_caption
                                st.rerun()
                    else:
                        if hasattr(st.session_state, 'current_overlay'):
                            if st.button("Generate GradCAM Caption"):
                                with st.spinner("Analyzing GradCAM visualization..."):
                                    gradcam_caption = generate_gradcam_caption(
                                        st.session_state.current_overlay,
                                        st.session_state.finetuned_processor,
                                        st.session_state.finetuned_model
                                    )
                                    st.session_state.gradcam_caption = gradcam_caption
                                    st.rerun()
                        else:
                            st.info("GradCAM visualization not available. Visit the Detection tab to generate it.")
        else:
            st.info("Please upload and analyze an image in the Detection tab first.")
    
    # Tab 3: LLM Analysis
    with tab3:
        st.header("LLM Analysis")
        
        # Chat Interface
        if hasattr(st.session_state, 'current_image') and st.session_state.llm_model_loaded:
            st.subheader("Deepfake Analysis Chat")
            
            # Display reference images in a sidebar-like column
            col_images, col_chat = st.columns([1, 3])
            
            with col_images:
                st.write("#### Reference Images")
                st.image(st.session_state.current_image, caption="Original", use_container_width=True)
                
                if hasattr(st.session_state, 'current_overlay'):
                    st.image(st.session_state.current_overlay, caption="GradCAM", use_container_width=True)
                
                if hasattr(st.session_state, 'comparison_image'):
                    st.image(st.session_state.comparison_image, caption="Comparison", use_container_width=True)
                    
                if hasattr(st.session_state, 'current_pred_label'):
                    st.info(f"**Classification:** {st.session_state.current_pred_label} (Confidence: {st.session_state.current_confidence:.2%})")
            
            with col_chat:
                # Display chat history
                for i, (question, answer) in enumerate(st.session_state.chat_history):
                    st.markdown(f"**Question {i+1}:** {question}")
                    st.markdown(f"**Answer:** {answer}")
                    st.markdown("---")
                
                # Custom instruction in the chat column
                use_custom_instructions = st.toggle("Enable Custom Instructions", key="llm_custom_instructions", value=False)
                if use_custom_instructions:
                    custom_instruction = st.text_area(
                        "Custom Instructions (Advanced)",
                        value="Specify your preferred style of explanation (e.g., 'Provide technical, detailed explanations' or 'Use simple, non-technical language'). You can also specify what aspects of the image to focus on.",
                        help="Add specific instructions for the analysis"
                    )
                else:
                    custom_instruction = ""
                
                # Include both captions in the prompt if available
                caption_text = ""
                if hasattr(st.session_state, 'image_caption'):
                    caption_text += f"\n\nImage Description:\n{st.session_state.image_caption}"
                    
                if hasattr(st.session_state, 'gradcam_caption'):
                    caption_text += f"\n\nGradCAM Analysis:\n{st.session_state.gradcam_caption}"
                
                # Default question with option to customize
                default_question = f"Ask your question about this image..."
                
                # User input for new question
                new_question = st.text_area("Ask a question about the image:", value=default_question if not st.session_state.chat_history else "", height=100)
                
                # Analyze button and Clear Chat button in the same row
                col1, col2 = st.columns([3, 1])
                with col1:
                    analyze_button = st.button("🔍 Send Question", type="primary")
                with col2:
                    clear_button = st.button("🗑️ Clear Chat History")
                
                if clear_button:
                    st.session_state.chat_history = []
                    st.rerun()
                    
                if analyze_button and new_question:
                    try:
                        # Add caption info if it's the first question
                        if not st.session_state.chat_history:
                            full_question = new_question + caption_text
                        else:
                            full_question = new_question
                        
                        result = analyze_image_with_llm(
                            st.session_state.current_image,
                            st.session_state.current_overlay,
                            st.session_state.current_face_box,
                            st.session_state.current_pred_label,
                            st.session_state.current_confidence,
                            full_question,
                            st.session_state.llm_model,
                            st.session_state.tokenizer,
                            temperature=temperature,
                            max_tokens=max_tokens,
                            custom_instruction=custom_instruction
                        )
                        
                        # Add to chat history
                        st.session_state.chat_history.append((new_question, result))
                        
                        # Display the latest result too
                        st.success("✅ Analysis complete!")
                        
                        # Check if the result contains both technical and non-technical explanations
                        if "Technical" in result and "Non-Technical" in result:
                            try:
                                # Split the result into technical and non-technical sections
                                parts = result.split("Non-Technical")
                                technical = parts[0]
                                non_technical = "Non-Technical" + parts[1]
                                
                                # Display in two columns
                                tech_col, simple_col = st.columns(2)
                                with tech_col:
                                    st.subheader("Technical Analysis")
                                    st.markdown(technical)
                                
                                with simple_col:
                                    st.subheader("Simple Explanation")
                                    st.markdown(non_technical)
                            except Exception as e:
                                # Fallback if splitting fails
                                st.subheader("Analysis Result")
                                st.markdown(result)
                        else:
                            # Just display the whole result
                            st.subheader("Analysis Result")
                            st.markdown(result)
                        
                        # Rerun to update the chat history display
                        st.rerun()
                        
                    except Exception as e:
                        st.error(f"Error during LLM analysis: {str(e)}")
        else:
            if not hasattr(st.session_state, 'current_image'):
                st.warning("⚠️ Please upload an image in the Detection tab first.")
            else:
                st.warning("⚠️ Please load the Vision LLM from the sidebar to perform detailed analysis.")
    
    # Footer
    st.markdown("---")

if __name__ == "__main__":
    main()