{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "# Load the pre-trained model\n",
    "model = tf.keras.models.load_model(\"\")\n",
    "\n",
    "# Define the prediction function\n",
    "def classify_image(image):\n",
    "    # Preprocess the image\n",
    "    image = tf.image.resize(image, (224, 224))\n",
    "    image = tf.keras.applications.mobilenet_v2.preprocess_input(image)\n",
    "    image = np.expand_dims(image, axis=0)\n",
    "\n",
    "    # Make predictions\n",
    "    predictions = model.predict(image)\n",
    "    label = np.argmax(predictions[0])\n",
    "    confidence = predictions[0][label]\n",
    "\n",
    "    return label, confidence\n",
    "\n",
    "# Create the Gradio interface\n",
    "inputs = gr.inputs.Image()\n",
    "outputs = gr.outputs.Label(num_top_classes=3)\n",
    "\n",
    "# Launch the interface with a public link\n",
    "gr.Interface(\n",
    "    fn=classify_image, \n",
    "    inputs=inputs, \n",
    "    outputs=outputs, \n",
    "    title=\"Micro-Guru\", \n",
    "    description=\"Upload an image and the model will predict its class along with the confidence level.\"\n",
    ").launch(share=True)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}