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# app.py
import streamlit as st
import torch
from PIL import Image
import io
from transformers import AutoProcessor, AutoModelForCausalLM
from peft import PeftModel
# Page config
st.set_page_config(
page_title="Deepfake Explainer",
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")
@st.cache_resource
def load_model():
"""Load model and processor (cached to avoid reloading)"""
# Load base model
base_model_id = "unsloth/llama-3.2-11b-vision-instruct"
processor = AutoProcessor.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(
base_model_id,
device_map="auto",
torch_dtype=torch.float16
)
# Load adapter
adapter_id = "saakshigupta/deepfake-explainer-1"
model = PeftModel.from_pretrained(model, adapter_id)
return model, processor
# Function to fix cross-attention masks
def fix_processor_outputs(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 # The exact dimension we fixed 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
st.write("✅ Fixed cross-attention mask dimensions")
return inputs
# Load model on first run
with st.spinner("Loading model... this may take a minute."):
model, processor = load_model()
st.success("Model loaded successfully!")
# 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.")
st.markdown("Model by [saakshigupta](https://huggingface.co/saakshigupta/deepfake-explainer-1)")
# Main content area - file uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
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
if st.button("Analyze Image"):
with st.spinner("Analyzing the image..."):
# Process the image
inputs = processor(text=custom_prompt, images=image, return_tensors="pt")
# Fix cross-attention mask
inputs = fix_processor_outputs(inputs)
# 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)
else:
st.info("Please upload an image to begin analysis") |