app.py

import streamlit as st
import torch
from PIL import Image
import os
import gc
from transformers import AutoProcessor, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

# Page config
st.set_page_config(
    page_title="Deepfake Image Analyzer",
    page_icon="🔍",
    layout="wide"
)

# App title and description
st.title("Deepfake Image Analyzer")
st.markdown("Upload an image to analyze it for possible deepfake manipulation")

# Function to free up memory
def free_memory():
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

# Helper function to check CUDA
def init_device():
    if torch.cuda.is_available():
        st.sidebar.success("✓ GPU available: Using CUDA")
        return "cuda"
    else:
        st.sidebar.warning("⚠️ No GPU detected: Using CPU (analysis will be slow)")
        return "cpu"

# Set device
device = init_device()

@st.cache_resource
def load_model():
    """Load model with proper quantization handling"""
    try:
        # Using your original base model
        base_model_id = "unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit"
        
        # Load processor
        processor = AutoProcessor.from_pretrained(base_model_id)
        
        # Load the model with proper quantization settings
        model = AutoModelForCausalLM.from_pretrained(
            base_model_id,
            device_map="auto",
            quantization_config=BitsAndBytesConfig(
                load_in_4bit=True,
                bnb_4bit_compute_dtype=torch.float16,
                bnb_4bit_use_double_quant=True,
                bnb_4bit_quant_type="nf4"
            ),
            torch_dtype=torch.float16
        )
        
        # Load adapter
        adapter_id = "saakshigupta/deepfake-explainer-1"
        model = PeftModel.from_pretrained(model, adapter_id)
        
        return model, processor
    
    except Exception as e:
        st.error(f"Error loading model: {str(e)}")
        st.exception(e)
        return None, None

# Function to fix cross-attention masks
def fix_processor_outputs(inputs):
    """Fix cross-attention mask dimensions if needed"""
    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  # The exact dimension used in training
        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 True, inputs
    return False, inputs

# Create sidebar with options
with st.sidebar:
    st.header("Options")
    temperature = st.slider("Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.1,
                           help="Higher values make output more random, lower values more deterministic")
    max_length = st.slider("Maximum response length", min_value=100, max_value=1000, value=500, step=50)
    
    custom_prompt = st.text_area(
        "Custom instruction (optional)",
        value="Analyze this image and determine if it's a deepfake. Provide both technical and non-technical explanations.",
        height=100
    )
    
    st.markdown("### About")
    st.markdown("""
    This app uses a fine-tuned Llama 3.2 Vision model to detect and explain deepfakes.
    
    The analyzer looks for:
    - Inconsistencies in facial features
    - Unusual lighting or shadows
    - Unnatural blur patterns
    - Artifacts around edges
    - Texture inconsistencies
    
    Model by [saakshigupta](https://huggingface.co/saakshigupta/deepfake-explainer-1)
    """)

# Load model button
if st.button("Load Model"):
    with st.spinner("Loading model... this may take several minutes"):
        try:
            model, processor = load_model()
            if model is not None and processor is not None:
                st.session_state['model'] = model
                st.session_state['processor'] = processor
                st.success("Model loaded successfully!")
            else:
                st.error("Failed to load model.")
        except Exception as e:
            st.error(f"Error during model loading: {str(e)}")
            st.exception(e)

# Main content area - file uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

# Check if model is loaded
model_loaded = 'model' in st.session_state and st.session_state['model'] is not None

if uploaded_file is not None:
    # Display the image
    image = Image.open(uploaded_file).convert('RGB')
    st.image(image, caption="Uploaded Image", use_column_width=True)
    
    # Analyze button (only enabled if model is loaded)
    if st.button("Analyze Image", disabled=not model_loaded):
        if not model_loaded:
            st.warning("Please load the model first by clicking the 'Load Model' button.")
        else:
            with st.spinner("Analyzing the image... This may take 15-30 seconds"):
                try:
                    # Get components from session state
                    model = st.session_state['model']
                    processor = st.session_state['processor']
                    
                    # Process the image using the processor
                    inputs = processor(text=custom_prompt, images=image, return_tensors="pt")
                    
                    # Fix cross-attention mask if needed
                    fixed, inputs = fix_processor_outputs(inputs)
                    if fixed:
                        st.info("Fixed cross-attention mask dimensions")
                    
                    # Move to device
                    inputs = {k: v.to(model.device) for k, v in inputs.items() if isinstance(v, torch.Tensor)}
                    
                    # Generate the analysis
                    with torch.no_grad():
                        output_ids = model.generate(
                            **inputs,
                            max_new_tokens=max_length,
                            temperature=temperature,
                            top_p=0.9
                        )
                    
                    # Decode the output
                    response = processor.decode(output_ids[0], skip_special_tokens=True)
                    
                    # Extract the actual response (removing the prompt)
                    if custom_prompt in response:
                        result = response.split(custom_prompt)[-1].strip()
                    else:
                        result = response
                    
                    # Display result in a nice format
                    st.success("Analysis complete!")
                    
                    # Show technical and non-technical explanations separately if they exist
                    if "Technical Explanation:" in result and "Non-Technical Explanation:" in result:
                        technical, non_technical = result.split("Non-Technical Explanation:")
                        technical = technical.replace("Technical Explanation:", "").strip()
                        
                        col1, col2 = st.columns(2)
                        with col1:
                            st.subheader("Technical Analysis")
                            st.write(technical)
                        
                        with col2:
                            st.subheader("Simple Explanation")
                            st.write(non_technical)
                    else:
                        st.subheader("Analysis Result")
                        st.write(result)
                    
                    # Free memory after analysis
                    free_memory()
                    
                except Exception as e:
                    st.error(f"Error analyzing image: {str(e)}")
                    st.exception(e)
    elif not model_loaded:
        st.warning("Please load the model first by clicking the 'Load Model' button at the top of the page.")
else:
    st.info("Please upload an image to begin analysis")

# Add footer
st.markdown("---")
st.markdown("Deepfake Image Analyzer")