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{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"model_path = \"transferlearning_pokemon.keras\"\n",
"model = tf.keras.models.load_model(model_path)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Define the core prediction function\n",
"def predict_pokemon(image):\n",
" # Preprocess image\n",
" print(type(image))\n",
" image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image\n",
" image = image.resize((150, 150)) # Resize the image to 150x150\n",
" image = np.array(image)\n",
" image = np.expand_dims(image, axis=0) # Expand dimensions to match the model input shape\n",
" \n",
" # Predict\n",
" prediction = model.predict(image)\n",
" \n",
" # Print the shape of the prediction to debug\n",
" print(f\"Prediction shape: {prediction.shape}\")\n",
" \n",
" # Assuming the output is already softmax probabilities\n",
" probabilities = prediction[0]\n",
" \n",
" # Print the probabilities array to debug\n",
" print(f\"Probabilities: {probabilities}\")\n",
" \n",
" # Assuming your model was trained with these class names\n",
" class_names = ['charmander', 'eevee', 'pikachuu'] # Replace 'another_pokemon' with your third class name\n",
" \n",
" # Create a dictionary of class probabilities\n",
" result = {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}\n",
" \n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7866\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7866/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [9.1263162e-31 1.1169604e-30 1.0000000e+00]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [4.4493477e-06 8.4401548e-01 1.5598010e-01]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [9.9999964e-01 1.0916104e-07 1.8336594e-07]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [5.0329237e-04 8.8987160e-01 1.0962512e-01]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [9.1263162e-31 1.1169604e-30 1.0000000e+00]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [4.4493477e-06 8.4401548e-01 1.5598010e-01]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [5.0329237e-04 8.8987160e-01 1.0962512e-01]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [5.0329237e-04 8.8987160e-01 1.0962512e-01]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [9.9999964e-01 1.0916104e-07 1.8336594e-07]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [4.0465540e-22 8.3268744e-22 1.0000000e+00]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [9.9999964e-01 1.0916104e-07 1.8336594e-07]\n",
"<class 'numpy.ndarray'>\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
"Prediction shape: (1, 3)\n",
"Probabilities: [5.0329237e-04 8.8987160e-01 1.0962512e-01]\n"
]
}
],
"source": [
"# Create the Gradio interface\n",
"input_image = gr.Image()\n",
"iface = gr.Interface(\n",
" fn=predict_pokemon,\n",
" inputs=input_image, \n",
" outputs=gr.Label(),\n",
" examples=[\"pokemon_examples/charmander.png\", \"pokemon_examples/charmander1.jpg\", \"pokemon_examples/eevee.png\", \"pokemon_examples/eevee1.jpg\", \"pokemon_examples/pika.png\", \"pokemon_examples/pika1.jpg\"], \n",
" description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
"iface.launch()"
]
}
],
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"kernelspec": {
"display_name": "venv_new",
"language": "python",
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