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Create app.py
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app.py
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import gradio as gr
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing import image
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import numpy as np
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from huggingface_hub import from_pretrained_keras
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# Load the models
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model1 = from_pretrained_keras("arsath-sm/face_classification_model1")
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model2 = from_pretrained_keras("arsath-sm/face_classification_model2")
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# Preprocess the image
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def preprocess_image(img):
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img = image.img_to_array(img)
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img = np.expand_dims(img, axis=0)
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img = img / 255.0
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return img
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# Make predictions
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def predict(img):
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preprocessed_img = preprocess_image(img)
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prediction1 = model1.predict(preprocessed_img)[0][0]
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prediction2 = model2.predict(preprocessed_img)[0][0]
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result1 = "Real" if prediction1 > 0.5 else "Fake"
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result2 = "Real" if prediction2 > 0.5 else "Fake"
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confidence1 = prediction1 if result1 == "Real" else 1 - prediction1
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confidence2 = prediction2 if result2 == "Real" else 1 - prediction2
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return {
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"Model 1 Prediction": f"{result1} (Confidence: {confidence1:.2f})",
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"Model 2 Prediction": f"{result2} (Confidence: {confidence2:.2f})"
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}
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# Create the Gradio interface
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iface = gr.Interface(
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fn=predict,
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inputs=gr.Image(type="pil"),
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outputs={
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"Model 1 Prediction": gr.Textbox(),
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"Model 2 Prediction": gr.Textbox()
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},
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title="Real vs AI Face Classification",
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description="Upload an image to classify whether it's a real face or an AI-generated face using two different models."
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)
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# Launch the app
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iface.launch()
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