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import streamlit as st
from PIL import Image
import requests
from transformers import pipeline
# Load the pipeline
model_name = "vm24bho/net_dfm_myimg"
pipe = pipeline('image-classification', model=model_name)
st.title("Deepfake vs Real Image Detection")
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image.', use_column_width=True)
st.write("")
st.write("Classifying...")
# Apply the model
result = pipe(image)
# Display the result
st.write("**Classification Result:**")
st.write("---------------")
for i, res in enumerate(result):
label = res["label"]
score = res["score"] * 100 # Convert to percentage
st.write(f"**{i+1}. {label}**: {score:.2f}%")
st.write("---------------")
# Determine the majority score
real_score = result[0]["score"] * 100
fake_score = result[1]["score"] * 100
if real_score > fake_score:
majority_label = "Real"
else:
majority_label = "Fake"
# Display the final result
st.write(f"**Given image is {majority_label}**") |