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"Running on local URL: http://127.0.0.1:7862\n",
"Running on public URL: https://59007d69f4a41b96f2.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
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"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 628ms/step\n",
"[[0.33698466 0.33483982 0.32817554]]\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
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"source": [
"import gradio as gr\n",
"import tensorflow as tf\n",
"from PIL import Image\n",
"import numpy as np\n",
"\n",
"# Load the pre-trained model\n",
"model = tf.keras.models.load_model('pokemon_transferlearning.keras')\n",
"\n",
"def classify_image(img):\n",
" \n",
" if isinstance(img, np.ndarray):\n",
" img = Image.fromarray(img.astype('uint8'), 'RGB')\n",
"\n",
" # Preprocess the image to fit the model's input requirements\n",
" img = img.resize((150, 150)) # Resize the image using PIL, which is intended here\n",
" img_array = np.array(img) # Convert the resized PIL image to a numpy array\n",
" img_array = img_array / 255.0 # Normalize pixel values to [0, 1]\n",
" img_array = np.expand_dims(img_array, axis=0) # Expand dimensions to fit model input shape\n",
"\n",
" # Make prediction\n",
" prediction = model.predict(img_array)\n",
"\n",
" # prediction = np.round(float(tf.sigmoid(prediction)), 2)\n",
" # p_cat = (1 - prediction)\n",
" # p_dog = prediction\n",
" # return {'cat': p_cat, 'dog': p_dog}\n",
"\n",
" print(prediction)\n",
"\n",
" probabilities = tf.sigmoid(prediction).numpy() # Convert tensor to numpy array if using \n",
"\n",
" # Formatting the probabilities\n",
" class_names = ['Hitchoman', 'Pikachu', 'Charmeleon']\n",
" results = {class_names[i]: float(prediction[0][i]) for i in range(3)} # Convert each probability to float\n",
" \n",
" return results\n",
"\n",
"# Create Gradio interface\n",
"iface = gr.Interface(fn=classify_image,\n",
" inputs=gr.Image(),\n",
" outputs=gr.Label(num_top_classes=3),\n",
" title=\"Pokemon Classifier\",\n",
" description=\"Upload an image of a pokemon to classify.\")\n",
"\n",
"# Launch the application\n",
"iface.launch(share=True)\n"
]
}
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