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import tensorflow as tf |
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from tensorflow.keras.models import load_model |
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from PIL import Image |
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import requests |
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from io import BytesIO |
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from matplotlib import pyplot as plt |
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import numpy as np |
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import gradio as gr |
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import json |
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model = load_model('real-fake-best.h5') |
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def index(image_url): |
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response = requests.get(image_url) |
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img = Image.open(BytesIO(response.content)) |
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img = np.array(img) |
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resize = tf.image.resize(img, (32, 32)) |
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y_pred = model.predict(np.expand_dims(resize / 255, 0)) |
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predictions = {"Fake": y_pred[0][0]*100, "Real": y_pred[0][1]*100} |
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print("Predictions:", y_pred) |
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predicted_class = np.argmax(y_pred) |
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print("Predicted Class:", predicted_class) |
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return json.dumps(predictions) |
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inputs_image_url = [ |
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gr.Textbox(type="text", label="Image URL"), |
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] |
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outputs_result_dict = [ |
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gr.Textbox(type="text", label="Result Dictionary"), |
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] |
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interface_image_url = gr.Interface( |
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fn=index, |
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inputs=inputs_image_url, |
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outputs=outputs_result_dict, |
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title="AI Image Detection", |
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cache_examples=False, |
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) |
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gr.TabbedInterface( |
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[interface_image_url], |
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tab_names=['Image inference'] |
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).queue().launch() |
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