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import streamlit as st |
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import tensorflow as tf |
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import numpy as np |
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from PIL import Image |
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import os |
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model = tf.keras.models.load_model('my_cnn_model_7.h5') |
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def predict_image(img): |
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img = img.resize((64, 64)) |
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img_array = np.array(img) |
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img_array = np.expand_dims(img_array, axis=0) |
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img_array = img_array / 255.0 |
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predictions = model.predict(img_array) |
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prediction_label = (predictions > 0.5).astype("int32") |
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return prediction_label[0][0] |
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st.title("Image Classifier: Real vs Fake") |
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st.write("Upload an image to classify it as 'Real' or 'Fake'.") |
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uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) |
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if uploaded_file is not None: |
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img = Image.open(uploaded_file) |
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img_resized = img.resize((100, 100)) |
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st.image(img_resized, caption="Uploaded Image.", use_container_width=False) |
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if st.button('Classify'): |
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prediction = predict_image(img) |
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if prediction == 1: |
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st.write("Prediction: The given image is **Real**") |
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else: |
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st.write("Prediction: The given image is **Fake**") |
