{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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      "Requirement already satisfied: markdown-it-py>=2.2.0 in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (from rich->keras>=3.0.0->tensorflow) (3.0.0)\n",
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      "Requirement already satisfied: exceptiongroup>=1.0.2 in /Users/chiarazbinden/Library/Python/3.9/lib/python/site-packages (from anyio->httpx>=0.24.1->gradio) (1.2.0)\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install tensorflow tensorflow-datasets gradio pillow matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = \"pokemon-model_transferlearning.keras\"\n",
    "model = tf.keras.models.load_model(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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 28x28 and converts it to gray scale\n",
    "    image = np.array(image)\n",
    "    image = np.expand_dims(image, axis=0) # same as image[None, ...]\n",
    "    \n",
    "    # Predict\n",
    "    prediction = model.predict(image)\n",
    "    \n",
    "    # Because the output layer was dense(0) without an activation function, we need to apply sigmoid to get the probability\n",
    "    # we could also change the output layer to dense(1, activation='sigmoid')\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}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "# Define the core prediction function\n",
    "def predict_pokemon(image):\n",
    "    # Preprocess 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) # Add batch dimension\n",
    "    \n",
    "    # Predict\n",
    "    prediction = model.predict(image)\n",
    "    \n",
    "    # Apply softmax to get probabilities for each class\n",
    "    probabilities = tf.nn.softmax(prediction)\n",
    "    \n",
    "    # Map probabilities to Pokemon classes\n",
    "    pokemon_classes = ['Articuno', 'Bulbasaur', 'Charmander']  \n",
    "    probabilities_dict = {pokemon_class: round(float(probability), 2) for pokemon_class, probability in zip(pokemon_classes, probabilities[0])}\n",
    "    \n",
    "    return probabilities_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7863\n",
      "IMPORTANT: You are using gradio version 4.27.0, however version 4.29.0 is available, please upgrade.\n",
      "--------\n",
      "Running on public URL: https://a1f42e2a7f46b68a92.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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://a1f42e2a7f46b68a92.gradio.live\" 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": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step\n",
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      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\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=[\"images/cat1.jpeg\", \"images/cat2.jpeg\", \"images/cat3.jpeg\", \"images/cat4.jpeg\", \"images/dog1.jpeg\", \"images/dog2.jpeg\", \"images/dog3.jpeg\"],   \n",
    "    description=\"A simple mlp classification model for image classification using the mnist dataset.\")\n",
    "iface.launch(share=True)"
   ]
  }
 ],
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