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 BlipProcessor, BlipForConditionalGeneration
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
from gradcam_xception import load_xception_model, generate_smoothgrad_visualizations_xception
warnings.filterwarnings("ignore", category=UserWarning)

# Define Xception transform function directly in app.py
def get_xception_transform():
    """Get the image transformation pipeline for Xception input."""
    # Standard Xception preprocessing
    transform = transforms.Compose([
        transforms.Resize((299, 299)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
    ])
    return transform

# 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 three AI models:
- **Xception**: Initial Real/Fake classification
- **BLIP**: Describes image content
- **Llama 3.2**: Explains potential manipulations

### Quick Start
1. **Load Models** - Start with Xception, 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 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
            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

# 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
        transform = get_xception_transform()
        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 our module"""
    with st.spinner("Loading Xception model for deepfake detection..."):
        try:
            model = load_xception_model()
            # Get the device
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            model.to(device)
            model.eval()
            return model, device
        except Exception as e:
            st.error(f"Error loading Xception model: {str(e)}")
            return None, None

# ----- 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-2"
            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."
    
    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 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)}")
        return f"Error analyzing image: {str(e)}"

# Preprocess image for Xception
def preprocess_image_xception(image):
    """Preprocesses image for Xception model input and face detection."""
    face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    image_np = np.array(image.convert('RGB'))  # Ensure RGB
    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:
        st.warning("No face detected, using whole image for prediction/CAM.")
    else:
        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)

    # Xception specific transform
    transform = get_xception_transform()
    # 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

# Main app
def main():
    # Initialize session state variables
    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 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
        xception_col, blip_col, llm_col = st.columns(3)
        
        with xception_col:
            if not st.session_state.xception_model_loaded:
                if st.button("📥 Load Xception Model for Detection", type="primary"):
                    # Load Xception model
                    model, device = load_detection_model_xception()
                    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 successfully!")
                    else:
                        st.error("❌ Failed to load Xception model.")
            else:
                st.success("✅ Xception 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
                
                # Detect with Xception model if loaded
                if st.session_state.xception_model_loaded:
                    with st.spinner("Analyzing image with Xception model..."):
                        # Preprocess image for Xception
                        input_tensor, original_image, face_box = preprocess_image_xception(image)
                        
                        # Get device and model
                        device = st.session_state.device
                        model = st.session_state.xception_model
                        
                        # Move tensor to device
                        input_tensor = input_tensor.to(device)
                        
                        # Forward pass
                        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()
                            pred_label = "Fake" if pred_class == 0 else "Real"  # Check class mapping
                        
                        # Display results
                        with col2:
                            st.markdown("### Detection Result")
                            st.markdown(f"**Classification:** {pred_label} (Confidence: {confidence:.2%})")
                            
                            # Display face box on image if detected
                            if face_box:
                                img_to_show = original_image.copy()
                                img_draw = np.array(img_to_show)
                                x, y, w, h = face_box
                                cv2.rectangle(img_draw, (x, y), (x + w, y + h), (0, 255, 0), 2)
                                st.image(Image.fromarray(img_draw), caption="Detected Face", width=300)
                        
                        # GradCAM visualization
                        st.subheader("GradCAM Visualization")
                        cam, overlay, comparison, detected_face_box = process_image_with_xception_gradcam(
                            image, model, 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
                        
                        # 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
                        
                        # 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 Xception 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 {{pred_label}}. Analyze all the provided images (original, GradCAM visualization, and comparison) to determine if this is a deepfake. Focus on 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("---")

    # Add model version indicator in sidebar
    st.sidebar.info("Using Xception + deepfake-explainer-2 models")

if __name__ == "__main__":
    main()