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app.py

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+# Write a Simple gradio app to take image as input run a model on it and Returnt the Probability (0 to 1) as a confidence bar
+
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+from huggingface_hub import from_pretrained_keras
+
+REPO_ID = "ai-or-not/ai-or-not-model"
+
+model = from_pretrained_keras(REPO_ID)
+
+
+# Define the function
+def classify_image(array):
+    # image is numpy array
+    array = array / 255.0
+    image = tf.image.resize_with_pad(array, 224, 224)
+    image = np.expand_dims(image, axis=0)
+    print(image.shape)
+    prediction = model(image)
+    # there are 3 class probabilities in the model 0: "REAL", 1: "GAN", 2: "DIFFUSION"
+    real = prediction[0][0]
+    ai = 1 - real
+
+    return {"REAL": real, "AI": ai}
+
+
+demo = gr.Interface(fn=classify_image, inputs="image", outputs="label", examples="examples")
+demo.launch(
+    debug=True,
+)