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import streamlit as st
import numpy as np
import tensorflow as tf
from PIL import Image
import cv2
# Load the model
model = tf.keras.models.load_model('cnn_real_fake_4.h5')
# Define a function to preprocess the uploaded image
def preprocess_image(image):
img_size = (256, 256)
image = image.resize(img_size) # Resize image
image = np.array(image) # Convert to numpy array
image = image / 255.0 # Normalize to [0, 1]
image = np.expand_dims(image, axis=0) # Add batch dimension
return image
# Define a function for making predictions
def predict_image(image):
preprocessed_image = preprocess_image(image)
prediction = model.predict(preprocessed_image)
if prediction > 0.5:
return 'Fake'
else:
return 'Real'
# Streamlit app interface
with open("style.css") as f:
st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
st.title('Real or Fake Image Classifier')
st.write("""
This app allows you to upload an image and classify it as either Real or Fake.
""")
# Upload an image
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
if uploaded_image is not None:
# Display the uploaded image
image = Image.open(uploaded_image)
img_resized = image.resize((100, 100))
st.image(img_resized, caption='Uploaded Image', use_container_width=False)
# Predict the image
st.write("")
st.write("Classifying the image...")
result = predict_image(image)
# Display the result
st.write(f"The uploaded image is classified as: **{result}**")
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