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import streamlit as st
from PIL import Image
from transformers import pipeline
import requests
# Set the title and sidebar
st.set_page_config(page_title="Deepfake vs Real Image Detection", page_icon="🤖")
st.title("Deepfake vs Real Image Detection")
st.sidebar.title("Options")
# Description
st.markdown(
"""
Welcome to the Deepfake vs Real Image Detection app!
Upload an image and let our model determine if it is real or a deepfake.
"""
)
# Load the pipeline
model_name = "vm24bho/net_dfm_myimg"
try:
pipe = pipeline('image-classification', model=model_name)
except Exception as e:
st.error(f"Error loading model: {e}")
st.stop()
# Upload image
st.sidebar.subheader("Upload Image")
uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
# Add a sample image option
sample_image = None
if st.sidebar.button("Use Sample Image"):
sample_url = "https://drive.google.com/file/d/15zh_XjwH9gGAzNdNUEgHraYAzE4GdSRU/view?usp=drive_link" # Replace with a valid sample image URL
try:
response = requests.get(sample_url, stream=True)
sample_image = Image.open(response.raw)
except Exception as e:
st.error(f"Error loading sample image: {e}")
st.stop()
if uploaded_file is not None:
image = Image.open(uploaded_file)
elif sample_image is not None:
image = sample_image
else:
image = None
# Display the uploaded image
if image is not None:
st.image(image, caption='Uploaded Image', use_column_width=True)
st.write("")
st.write("Classifying...")
# Apply the model
try:
result = pipe(image)
except Exception as e:
st.error(f"Error classifying image: {e}")
st.stop()
# Determine if the image is classified as real or fake
if result[0]['label'] == 'FAKE':
st.write("This Image is: Fake")
else:
st.write("This Image is: Real")
# Display the result
st.subheader("Classification Result")
for res in result:
st.write(f"**{res['label']}**: {res['score']*100:.2f}%")
else:
st.sidebar.info("Upload an image to get started or use the sample image.")
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