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import streamlit as st
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image
import os
model = tf.keras.models.load_model('my_cnn_model_7.h5')
def predict_image(img):
img = img.resize((64, 64))
img_array = np.array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array / 255.0
predictions = model.predict(img_array)
prediction_label = (predictions > 0.5).astype("int32")
return prediction_label[0][0]
# Streamlit UI
st.title("Image Classifier: Real vs Fake")
st.write("Upload an image to classify it as 'Real' or 'Fake'.")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
img = image.open(uploaded_file)
st.image(img, caption="Uploaded Image.", use_column_width=True)
if st.button('Classify'):
prediction = predict_image(img)
if prediction == 1:
st.write("Prediction: The given image is **Real**")
else:
st.write("Prediction: The given image is **Fake**") |