{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[?25hBuilding wheels for collected packages: ffmpy\n",
      "  Building wheel for ffmpy (setup.py) ... \u001b[?25ldone\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# pip install gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mabelang/Documents/Z. AI apps/AI Task 2_Pokemon Classifier/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = \"mabel_transferlearning.keras\"\n",
    "model = tf.keras.models.load_model(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the core prediction function\n",
    "def predict_pokemons(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",
    "    # Apply sigmoid to get probabilities\n",
    "    prediction_prob = tf.sigmoid(prediction).numpy()\n",
    "\n",
    "    p_Abra = round(prediction_prob[0][0], 2)\n",
    "    p_Pikachu = round(prediction_prob[0][1], 2)\n",
    "    p_Beedrill = round(prediction_prob[0][2], 2)\n",
    "\n",
    "    return{'Abra': p_Abra, 'Pikachu': p_Pikachu, 'Beedrill': p_Beedrill}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7863\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7863/\" 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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the Gradio interface\n",
    "input_image = gr.Image()\n",
    "iface = gr.Interface(\n",
    "    fn=predict_pokemons,\n",
    "    inputs=input_image, \n",
    "    outputs=gr.Label(),\n",
    "    examples=[\"Abra1.png\", \"Abra2.png\", \"Abra3.jpg\", \"Beedrill1.jpg\", \"Beedrill2.jpg\", \"Beedrill3.png\", \"Pikachu1.png\", \"Pikachu2.jpg\", \"Pikachu3.png\"], \n",
    "    description=\"Pokemon Classifier\")\n",
    "iface.launch()"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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