app.py

import streamlit as st
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms
from transformers import CLIPModel, BlipProcessor, BlipForConditionalGeneration
from transformers.models.clip import CLIPModel  
from PIL import Image
import numpy as np
import io
import base64
import cv2
import matplotlib.pyplot as plt
from peft import PeftModel
from unsloth import FastVisionModel
import os
import tempfile
import warnings
warnings.filterwarnings("ignore", category=UserWarning)

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

# 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("About")
st.sidebar.markdown("""
This tool detects deepfakes using four AI models:
- **CLIP**: Initial Real/Fake classification
- **GradCAM**: Highlights suspicious regions
- **BLIP**: Describes image content
- **Llama 3.2**: Explains potential manipulations

### Quick Start
1. **Load Models** - Start with CLIP, add others as needed
2. **Upload Image** - View classification and heat map
3. **Analyze** - Get explanations and ask questions

*GPU recommended for better performance*
""")

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

# Custom instruction text area in sidebar
use_custom_instructions = st.sidebar.toggle("Enable Custom Instructions", value=False, help="Toggle to enable/disable custom instructions")

if use_custom_instructions:
    custom_instruction = st.sidebar.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 = ""

# ----- GradCAM Implementation -----

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
            if self.transform:
                face_tensor = self.transform(face_img)
            else:
                face_tensor = transforms.ToTensor()(face_img)
            
            return face_tensor, label, "uploaded_image", original_image, face_box, self.dataset_name
        else:
            # Process the whole image
            if self.transform:
                image_tensor = self.transform(self.image)
            else:
                image_tensor = transforms.ToTensor()(self.image)
                
            return image_tensor, label, "uploaded_image", original_image, None, self.dataset_name

class GradCAM:
    def __init__(self, model, target_layer):
        self.model = model
        self.target_layer = target_layer
        self.gradients = None
        self.activations = None
        self._register_hooks()

    def _register_hooks(self):
        def forward_hook(module, input, output):
            if isinstance(output, tuple):
                self.activations = output[0]
            else:
                self.activations = output

        def backward_hook(module, grad_in, grad_out):
            if isinstance(grad_out, tuple):
                self.gradients = grad_out[0]
            else:
                self.gradients = grad_out

        layer = dict([*self.model.named_modules()])[self.target_layer]
        layer.register_forward_hook(forward_hook)
        layer.register_backward_hook(backward_hook)

    def generate(self, input_tensor, class_idx):
        self.model.zero_grad()
        
        try:
            # Use only the vision part of the model for gradient calculation
            vision_outputs = self.model.vision_model(pixel_values=input_tensor)
            
            # Get the pooler output
            features = vision_outputs.pooler_output
            
            # Create a dummy gradient for the feature based on the class idx
            one_hot = torch.zeros_like(features)
            one_hot[0, class_idx] = 1
            
            # Manually backpropagate
            features.backward(gradient=one_hot)

            # Check for None values
            if self.gradients is None or self.activations is None:
                st.warning("Warning: Gradients or activations are None. Using fallback CAM.")
                return np.ones((14, 14), dtype=np.float32) * 0.5

            # Process gradients and activations for transformer-based model
            gradients = self.gradients.cpu().detach().numpy()
            activations = self.activations.cpu().detach().numpy()
            
            if len(activations.shape) == 3:  # [batch, sequence_length, hidden_dim]
                seq_len = activations.shape[1]
                
                # CLIP ViT typically has 196 patch tokens (14×14) + 1 class token = 197
                if seq_len >= 197:
                    # Skip the class token (first token) and reshape the patch tokens into a square
                    patch_tokens = activations[0, 1:197, :]  # Remove the class token
                    # Take the mean across the hidden dimension
                    token_importance = np.mean(np.abs(patch_tokens), axis=1)
                    # Reshape to the expected grid size (14×14 for CLIP ViT)
                    cam = token_importance.reshape(14, 14)
                else:
                    # Try to find factors close to a square
                    side_len = int(np.sqrt(seq_len))
                    # Use the mean across features as importance
                    token_importance = np.mean(np.abs(activations[0]), axis=1)
                    # Create as square-like shape as possible
                    cam = np.zeros((side_len, side_len))
                    # Fill the cam with available values
                    flat_cam = cam.flatten()
                    flat_cam[:min(len(token_importance), len(flat_cam))] = token_importance[:min(len(token_importance), len(flat_cam))]
                    cam = flat_cam.reshape(side_len, side_len)
            else:
                # Fallback
                st.info("Using fallback CAM shape (14x14)")
                cam = np.ones((14, 14), dtype=np.float32) * 0.5  # Default fallback

            # Ensure we have valid values
            cam = np.maximum(cam, 0)
            if np.max(cam) > 0:
                cam = cam / np.max(cam)
            
            return cam
            
        except Exception as e:
            st.error(f"Error in GradCAM.generate: {str(e)}")
            return np.ones((14, 14), dtype=np.float32) * 0.5

def overlay_cam_on_image(image, cam, face_box=None, alpha=0.5):
    """Overlay the CAM on the image"""
    if face_box is not None:
        x, y, w, h = face_box
        # Create a mask for the entire image (all zeros initially)
        img_np = np.array(image)
        full_h, full_w = img_np.shape[:2]
        full_cam = np.zeros((full_h, full_w), dtype=np.float32)
        
        # Resize CAM to match face region
        face_cam = cv2.resize(cam, (w, h))
        
        # Copy the face CAM into the full image CAM at the face position
        full_cam[y:y+h, x:x+w] = face_cam
        
        # Convert full CAM to image
        cam_resized = Image.fromarray((full_cam * 255).astype(np.uint8))
        cam_colormap = plt.cm.jet(np.array(cam_resized) / 255.0)[:, :, :3]  # Apply colormap
        cam_colormap = (cam_colormap * 255).astype(np.uint8)
    else:
        # Resize CAM to match image dimensions
        img_np = np.array(image)
        h, w = img_np.shape[:2]
        cam_resized = cv2.resize(cam, (w, h))
        
        # Apply colormap
        cam_colormap = plt.cm.jet(cam_resized)[:, :, :3]  # Apply colormap
        cam_colormap = (cam_colormap * 255).astype(np.uint8)

    # Blend the original image with the colormap
    img_np_float = img_np.astype(float) / 255.0
    cam_colormap_float = cam_colormap.astype(float) / 255.0
    
    blended = img_np_float * (1 - alpha) + cam_colormap_float * alpha
    blended = (blended * 255).astype(np.uint8)
    
    return Image.fromarray(blended)

def save_comparison(image, cam, overlay, face_box=None):
    """Create a side-by-side comparison of the original, CAM, and overlay"""
    fig, axes = plt.subplots(1, 3, figsize=(15, 5))

    # Original Image
    axes[0].imshow(image)
    axes[0].set_title("Original")
    if face_box is not None:
        x, y, w, h = face_box
        rect = plt.Rectangle((x, y), w, h, edgecolor='lime', linewidth=2, fill=False)
        axes[0].add_patch(rect)
    axes[0].axis("off")

    # CAM
    if face_box is not None:
        # Create a full image CAM that highlights only the face
        img_np = np.array(image)
        h, w = img_np.shape[:2]
        full_cam = np.zeros((h, w))
        
        x, y, fw, fh = face_box
        # Resize CAM to face size
        face_cam = cv2.resize(cam, (fw, fh))
        # Place it in the right position
        full_cam[y:y+fh, x:x+fw] = face_cam
        axes[1].imshow(full_cam, cmap="jet")
    else:
        cam_resized = cv2.resize(cam, (image.width, image.height))
        axes[1].imshow(cam_resized, cmap="jet")
    axes[1].set_title("CAM")
    axes[1].axis("off")

    # Overlay
    axes[2].imshow(overlay)
    axes[2].set_title("Overlay")
    axes[2].axis("off")

    plt.tight_layout()
    
    # Convert plot to PIL Image for Streamlit display
    buf = io.BytesIO()
    plt.savefig(buf, format="png", bbox_inches="tight")
    plt.close()
    buf.seek(0)
    return Image.open(buf)

# Function to load GradCAM CLIP model
@st.cache_resource
def load_clip_model():
    with st.spinner("Loading CLIP model for GradCAM..."):
        try:
            model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
            
            # Apply a simple classification head
            model.classification_head = nn.Linear(1024, 2)
            model.classification_head.weight.data.normal_(mean=0.0, std=0.02)
            model.classification_head.bias.data.zero_()
            
            model.eval()
            return model
        except Exception as e:
            st.error(f"Error loading CLIP model: {str(e)}")
            return None

def get_target_layer_clip(model):
    """Get the target layer for GradCAM"""
    return "vision_model.encoder.layers.23"

def process_image_with_gradcam(image, model, device, pred_class):
    """Process an image with GradCAM"""
    # Set up transformations
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]),
    ])
    
    # Create dataset for the single image
    dataset = ImageDataset(image, transform=transform, face_only=True)
    
    # Custom collate function
    def custom_collate(batch):
        tensors = [item[0] for item in batch]
        labels = [item[1] for item in batch]
        paths = [item[2] for item in batch]
        images = [item[3] for item in batch]
        face_boxes = [item[4] for item in batch]
        dataset_names = [item[5] for item in batch]
        
        tensors = torch.stack(tensors)
        labels = torch.tensor(labels)
        
        return tensors, labels, paths, images, face_boxes, dataset_names
    
    # Create dataloader
    dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=custom_collate)
    
    # Extract the batch
    for batch in dataloader:
        input_tensor, label, img_paths, original_images, face_boxes, dataset_names = batch
        original_image = original_images[0]
        face_box = face_boxes[0]
        
        # Move tensors and model to device
        input_tensor = input_tensor.to(device)
        model = model.to(device)
        
        try:
            # Create GradCAM extractor
            target_layer = get_target_layer_clip(model)
            cam_extractor = GradCAM(model, target_layer)
            
            # Generate CAM
            cam = cam_extractor.generate(input_tensor, pred_class)
            
            # Create visualizations
            overlay = overlay_cam_on_image(original_image, cam, face_box)
            comparison = save_comparison(original_image, cam, overlay, face_box)
            
            # Return results
            return cam, overlay, comparison, face_box
            
        except Exception as e:
            st.error(f"Error processing image with GradCAM: {str(e)}")
            # Return default values
            default_cam = np.ones((14, 14), dtype=np.float32) * 0.5
            overlay = overlay_cam_on_image(original_image, default_cam, face_box)
            comparison = save_comparison(original_image, default_cam, overlay, face_box)
            return default_cam, overlay, comparison, face_box

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

# Function to load BLIP captioning models
@st.cache_resource
def load_blip_models():
    with st.spinner("Loading BLIP captioning models..."):
        try:
            # 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/deepfake-blip-large")
            finetuned_model = BlipForConditionalGeneration.from_pretrained("saakshigupta/deepfake-blip-large")
            
            return original_processor, original_model, finetuned_processor, finetuned_model
        except Exception as e:
            st.error(f"Error loading BLIP models: {str(e)}")
            return None, None, None, None

# 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)
        
        # Extract descriptions using the full text
        high_match = caption.split("high activation :")[1].split("moderate")[0] if "high activation :" in caption else ""
        moderate_match = caption.split("moderate activation :")[1].split("low")[0] if "moderate activation :" in caption else ""
        low_match = caption.split("low activation :")[1] if "low activation :" in caption else ""
        
        # Format the output
        formatted_text = ""
        if high_match:
            formatted_text += f"**High activation**:\n{high_match.strip()}\n\n"
        if moderate_match:
            formatted_text += f"**Moderate activation**:\n{moderate_match.strip()}\n\n"
        if low_match:
            formatted_text += f"**Low activation**:\n{low_match.strip()}"
            
        return formatted_text.strip()
        
    except Exception as e:
        st.error(f"Error analyzing GradCAM: {str(e)}")
        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

# Load model function
@st.cache_resource
def load_llm_model():
    with st.spinner("Loading LLM vision model... This may take a few minutes. Please be patient..."):
        try:
            # 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-1"
            model = PeftModel.from_pretrained(model, adapter_id)

            # Set to inference mode
            FastVisionModel.for_inference(model)
            
            return model, tokenizer
        except Exception as e:
            st.error(f"Error loading model: {str(e)}")
            return None, None

# 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."
    
    # Format the message to include both the original image and the GradCAM visualization
    messages = [
        {"role": "user", "content": [
            {"type": "image", "image": image},  # Original image
            {"type": "image", "image": gradcam_overlay},  # GradCAM overlay
            {"type": "text", "text": full_prompt}
        ]}
    ]

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

    # Process with image
    inputs = tokenizer(
        [image, gradcam_overlay],  # Send both images
        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 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

# Main app
def main():
    # Initialize session state variables
    if 'clip_model_loaded' not in st.session_state:
        st.session_state.clip_model_loaded = False
        st.session_state.clip_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 expanders for each stage
    with st.expander("Stage 1: Model Loading", expanded=True):
        st.write("Please load the models using the buttons below:")
        
        # Button for loading models
        clip_col, blip_col, llm_col = st.columns(3)
        
        with clip_col:
            if not st.session_state.clip_model_loaded:
                if st.button("📥 Load CLIP Model for Detection", type="primary"):
                    # Load CLIP model
                    model = load_clip_model()
                    if model is not None:
                        st.session_state.clip_model = model
                        st.session_state.clip_model_loaded = True
                        st.success("✅ CLIP model loaded successfully!")
                    else:
                        st.error("❌ Failed to load CLIP model.")
            else:
                st.success("✅ CLIP model loaded and ready!")
        
        with blip_col:
            if not st.session_state.blip_model_loaded:
                if st.button("📥 Load BLIP for Captioning", type="primary"):
                    # Load BLIP models
                    original_processor, original_model, finetuned_processor, finetuned_model = load_blip_models()
                    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 captioning models loaded successfully!")
                    else:
                        st.error("❌ Failed to load BLIP models.")
            else:
                st.success("✅ BLIP captioning models loaded and ready!")
                
        with llm_col:
            if not st.session_state.llm_model_loaded:
                if st.button("📥 Load Vision LLM for Analysis", type="primary"):
                    # Load LLM model
                    model, tokenizer = load_llm_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 successfully!")
                    else:
                        st.error("❌ Failed to load Vision LLM.")
            else:
                st.success("✅ Vision LLM loaded and ready!")
    
    # Image upload section
    with st.expander("Stage 2: Image Upload & Initial Detection", expanded=True):
        st.subheader("Upload an Image")
        uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
        
        if uploaded_file is not None:
            try:
                # Load and display the image (with controlled size)
                image = Image.open(uploaded_file).convert("RGB")
                
                # Display the image with a controlled width
                col1, col2 = st.columns([1, 2])
                with col1:
                    st.image(image, caption="Uploaded Image", width=300)
                
                # Generate detailed caption for original image if BLIP model is loaded
                if st.session_state.blip_model_loaded:
                    with st.spinner("Generating image description..."):
                        caption = generate_image_caption(
                            image, 
                            st.session_state.original_processor, 
                            st.session_state.original_model
                        )
                        st.session_state.image_caption = caption
                        
                        # Store caption but don't display it yet
                
                # Detect with CLIP model if loaded
                if st.session_state.clip_model_loaded:
                    with st.spinner("Analyzing image with CLIP model..."):
                        # Preprocess image for CLIP
                        transform = transforms.Compose([
                            transforms.Resize((224, 224)),
                            transforms.ToTensor(),
                            transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]),
                        ])
                        
                        # Create a simple dataset for the image
                        dataset = ImageDataset(image, transform=transform, face_only=True)
                        tensor, _, _, _, face_box, _ = dataset[0]
                        tensor = tensor.unsqueeze(0)
                        
                        # Get device
                        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
                        
                        # Move model and tensor to device
                        model = st.session_state.clip_model.to(device)
                        tensor = tensor.to(device)
                        
                        # Forward pass
                        with torch.no_grad():
                            outputs = model.vision_model(pixel_values=tensor).pooler_output
                            logits = model.classification_head(outputs)
                            probs = torch.softmax(logits, dim=1)[0]
                            pred_class = torch.argmax(probs).item()
                            confidence = probs[pred_class].item()
                            pred_label = "Fake" if pred_class == 1 else "Real"
                        
                        # Display results
                        with col2:
                            st.markdown("### Detection Result")
                            st.markdown(f"**Classification:** {pred_label} (Confidence: {confidence:.2%})")
                        
                        # GradCAM visualization
                        st.subheader("GradCAM Visualization")
                        cam, overlay, comparison, detected_face_box = process_image_with_gradcam(
                            image, model, device, pred_class
                        )
                        
                        # Display GradCAM results (controlled size)
                        st.image(comparison, caption="Original | CAM | Overlay", width=700)
                        
                        # Generate caption for GradCAM overlay image if BLIP model is loaded
                        if st.session_state.blip_model_loaded:
                            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
                                
                                # Store caption but don't display it yet
                        
                        # Save results in session state for LLM analysis
                        st.session_state.current_image = image
                        st.session_state.current_overlay = overlay
                        st.session_state.current_face_box = detected_face_box
                        st.session_state.current_pred_label = pred_label
                        st.session_state.current_confidence = confidence
                        
                        st.success("✅ Initial detection and GradCAM visualization complete!")
                else:
                    st.warning("⚠️ Please load the CLIP model first to perform initial detection.")
            except Exception as e:
                st.error(f"Error processing image: {str(e)}")
                import traceback
                st.error(traceback.format_exc())  # This will show the full error traceback
    
    # Image Analysis Summary section - AFTER Stage 2
    if hasattr(st.session_state, 'current_image') and (hasattr(st.session_state, 'image_caption') or hasattr(st.session_state, 'gradcam_caption')):
        with st.expander("Image Analysis Summary", expanded=True):
            # Display images and analysis in organized layout
            col1, col2 = st.columns([1, 2])
            
            with col1:
                # Display original image
                st.image(st.session_state.current_image, caption="Original Image", width=300)
                # Display GradCAM overlay
                if hasattr(st.session_state, 'current_overlay'):
                    st.image(st.session_state.current_overlay, caption="GradCAM Visualization", width=300)
            
            with col2:
                # Image description
                if hasattr(st.session_state, 'image_caption'):
                    st.markdown("### Image Description")
                    st.markdown(st.session_state.image_caption)
                    st.markdown("---")
                
                # GradCAM analysis
                if hasattr(st.session_state, 'gradcam_caption'):
                    st.markdown("### GradCAM Analysis")
                    st.markdown(st.session_state.gradcam_caption)
                    st.markdown("---")
    
    # LLM Analysis section - AFTER Image Analysis Summary
    with st.expander("Stage 3: Detailed Analysis with Vision LLM", expanded=False):
        if hasattr(st.session_state, 'current_image') and st.session_state.llm_model_loaded:
            st.subheader("Detailed Deepfake Analysis")
            
            # 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("---")
            
            # 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"This image has been classified as {st.session_state.current_pred_label}. Analyze the key features that led to this classification, focusing on the highlighted areas in the GradCAM visualization. Provide both a technical explanation for experts and a simple explanation for non-technical users."
            
            # 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.experimental_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.experimental_rerun()
                    
                except Exception as e:
                    st.error(f"Error during LLM analysis: {str(e)}")
                    
        elif not hasattr(st.session_state, 'current_image'):
            st.warning("⚠️ Please upload an image and complete the initial detection first.")
        else:
            st.warning("⚠️ Please load the Vision LLM to perform detailed analysis.")
    
    # Footer
    st.markdown("---")

if __name__ == "__main__":
    main()