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import streamlit as st
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image
# Load the trained model
model = tf.keras.models.load_model('deepfake_detection.h5')
# Function to load and preprocess the image
def load_and_preprocess_image(uploaded_image):
img = Image.open(uploaded_image)
img = img.resize((150, 150)) # Resize image to match the input size expected by the model
img_array = image.img_to_array(img) # Convert the image to a numpy array
img_array = np.expand_dims(img_array, axis=0) # Expand dimensions to match the input shape (1, 150, 150, 3)
img_array = img_array / 255.0 # Rescale the image array
return img_array
# Function to predict whether the image is real or fake
def predict_image(uploaded_image):
img_array = load_and_preprocess_image(uploaded_image)
prediction = model.predict(img_array)
if prediction < 0.5:
return "Fake"
else:
return "Real"
# Streamlit app layout
st.title("Deepfake Image Classification")
st.write("Upload an image and the model will predict whether it's Real or Fake.")
# Image uploader
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg"])
# Prediction button
if uploaded_image is not None:
st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
st.write("")
if st.button("Predict"):
result = predict_image(uploaded_image)
if result == "Fake":
st.write("The image is **<span style='color:red;'>Fake</span>**", unsafe_allow_html=True)
else:
st.write("The image is **<span style='color:cyan;'>Real</span>**", unsafe_allow_html=True)
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