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()