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import streamlit as st
from transformers import pipeline
from PIL import Image
import os
# Global variable to store the pipeline
deepfake_pipe = None
def load_model():
"""Load the model once and cache it"""
global deepfake_pipe
if deepfake_pipe is None:
st.info("Loading Deepfake Detection Model...")
deepfake_pipe = pipeline(
"image-classification",
model="prithivMLmods/Deep-Fake-Detector-v2-Model"
)
st.success("Model loaded successfully!")
return deepfake_pipe
def predict_deepfake(image):
"""Predict if image is deepfake or real"""
try:
# Load model if not already loaded
pipe = load_model()
# Ensure image is in PIL format
if not isinstance(image, Image.Image):
image = Image.open(image)
# Make prediction
results = pipe(image)
# Format results
prediction = {result['label']: result['score'] for result in results}
# Determine final verdict
deepfake_score = prediction.get('Deepfake', 0)
realism_score = prediction.get('Realism', 0)
if deepfake_score > realism_score:
verdict = f"🚨 DEEPFAKE DETECTED ({deepfake_score:.2%} confidence)"
color = "red"
else:
verdict = f"✅ REAL IMAGE ({realism_score:.2%} confidence)"
color = "green"
return prediction, verdict, color
except Exception as e:
return {"Error": 1.0}, f"❌ Prediction failed: {str(e)}", "red"
# Streamlit app configuration
st.set_page_config(
page_title="Jerry - Deepfake Detector",
page_icon="🕵️",
layout="wide"
)
def main():
st.title("🕵️ Jerry - Deepfake Detection Tool")
st.markdown("**Upload an image to check if it's real or AI-generated!**")
# Use session state to store results and prevent unnecessary re-renders
if 'results' not in st.session_state:
st.session_state.results = None
st.session_state.verdict = None
st.session_state.color = None
# Create two columns with fixed widths
col1, col2 = st.columns([1, 1], gap="medium")
with col1:
st.subheader("Upload Image")
# Fixed-height container for image to prevent layout shifts
with st.container(height=400, border=True):
uploaded_file = st.file_uploader(
"Choose an image",
type=['png', 'jpg', 'jpeg'],
label_visibility="collapsed",
key="file_uploader"
)
if uploaded_file is not None:
# Display uploaded image with fixed dimensions
image = Image.open(uploaded_file)
st.image(
image,
caption="Uploaded Image",
use_container_width=True,
clamp=True
)
with col2:
st.subheader("Detection Results")
# Fixed-height container for results
with st.container(height=400, border=True):
if uploaded_file is not None:
if st.button("🔍 Analyze Image", type="primary"):
with st.spinner("Analyzing image..."):
# Perform prediction and store in session state
prediction, verdict, color = predict_deepfake(uploaded_file)
st.session_state.results = prediction
st.session_state.verdict = verdict
st.session_state.color = color
# Display results
st.write("**Prediction Scores:**")
for label, score in st.session_state.results.items():
st.write(f"{label}: {score:.2%}")
# Display verdict with colored text
st.markdown(
f"<p style='color:{st.session_state.color};font-size:18px;'><b>{st.session_state.verdict}</b></p>",
unsafe_allow_html=True
)
else:
st.write("Please upload an image to analyze.")
# Additional information
st.markdown(
"""
---
**How it works:**
- Upload any facial image
- Jerry analyzes it and gives appropriate verdict
- Returns confidence scores for "Deepfake" vs "Realism"
**Note:** The model loads only once when first used, then runs quickly for subsequent predictions!
"""
)
if __name__ == "__main__":
print("🚀 Starting Jerry - Deepfake Detection App...")
print("Model will load on first prediction...")
main() |