{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model('./Trained_Model.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor'] \n",
    "\n",
    "def preprocess_image(image_path):\n",
    "    img = Image.open(image_path).convert('RGB') \n",
    "    img_array = img.resize((128, 128)) \n",
    "    return np.expand_dims(img_array, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_gradio(image):\n",
    "    img_array = preprocess_image(image)\n",
    "    predictions = model.predict(img_array)\n",
    "    predicted_class = np.argmax(predictions, axis=1)[0]\n",
    "    return classes[predicted_class]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7864\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7864/\" 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": 21,
     "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[1m0s\u001b[0m 465ms/step\n"
     ]
    }
   ],
   "source": [
    "interface = gr.Interface(\n",
    "    fn=predict_gradio,\n",
    "    inputs=gr.Image(type=\"filepath\"),\n",
    "    outputs=\"text\",\n",
    "    title=\"Brain Tumor Prediction\",\n",
    "    description=\"Upload an MRI image, and the model will predict the class.\"\n",
    ")\n",
    "\n",
    "interface.launch(server_port=7864)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "anway",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
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