{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFa5VyVNPCJY"
      },
      "source": [
        "# Project: Model Usage - Image Classification and Transfer Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9Z7RJrNvF8Q"
      },
      "source": [
        "**Instructions for Students:**\n",
        "\n",
        "Please carefully follow these steps to complete and submit your project:\n",
        "\n",
        "1. **Make a copy of the Project**: Please make a copy of this project either to your own Google Drive or download locally. Work on the copy of the project. The master project is **Read-Only**, meaning you can edit, but it will not be saved when you close the master project. To avoid total loss of your work, remember to make a copy.\n",
        "\n",
        "2. **Completing the Project**: You are required to work on and complete all tasks in the provided project. Be disciplined and ensure that you thoroughly engage with each task.\n",
        "   \n",
        "3. **Creating a Google Drive Folder**: Each of you must create a new folder on your Google Drive. This will be the repository for all your completed project files, aiding you in keeping your work organized and accessible.\n",
        "   \n",
        "4. **Uploading Completed Project**: Upon completion of your project, make sure to upload all necessary files, involving codes, reports, and related documents into the created Google Drive folder. Save this link in the 'Student Identity' section and also provide it as the last parameter in the `submit` function that has been provided.\n",
        "   \n",
        "5. **Sharing Folder Link**: You're required to share the link to your project Google Drive folder. This is crucial for the submission and evaluation of your project.\n",
        "   \n",
        "6. **Setting Permission to Public**: Please make sure your Google Drive folder is set to public. This allows your instructor to access your solutions and assess your work correctly.\n",
        "\n",
        "Adhering to these procedures will facilitate a smooth project evaluation process for you and the reviewers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Qg7V4zoPCJi"
      },
      "source": [
        "## Project Description:\n",
        "\n",
        "Welcome to your new project! You will have the opportunity to apply the knowledge and skills you've learned in class.\n",
        "\n",
        "The tasks are divided into two parts, the first part is to create an image classification project that predicts a person's age based on their photograph. You will be utilizing the power of machine learning pipelines to streamline your workflow and effectively manage the different stages of this project, from data preprocessing to model training and evaluation.\n",
        "\n",
        "In the second part is transfer learning where you'll use a [Vision Transformer (ViT)](https://huggingface.co/google/vit-base-patch16-224-in21k) model pre-trained on ImageNet-21k and fine-tune it on the [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset for emotion recognition, with the final step being the publication of your trained model to the Hugging Face Model Hub.\n",
        "\n",
        "Remember, the goal of this assignment is not just to build a model that makes accurate predictions, but also to understand the process of developing a machine-learning pipeline and the role each component plays in this process.\n",
        "\n",
        "We encourage you to be creative, explore different strategies, and most importantly, have fun while learning. We can't wait to see the innovative solutions you come up with! Best of luck!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MDaD2U_vF8S"
      },
      "source": [
        "## Grading Criteria\n",
        "\n",
        "There are 2 tasks in this project with 5 criterias for scoring, all except Criteria 4 have the same weight. Each criteria except Criteria 4 will give you either 100 point if you are correct and 0 if you are wrong. The final score for the project will the the average of all 5 criterias from both projects.\n",
        "\n",
        "* Task-1 Criteria 1: This task will assess your ability to understand how a model is likely to be used, in this use a model from Huggingface (HF) preferably using HF Pipeline, pass the input and get the correct answer form the model's output.\n",
        "\n",
        "* Task-1 Criteria 2: This task will assess your ability to use Gradio as a UI (User Interface) and interact with the model, in this case, the model used in Task-1 Criteria 1.\n",
        "\n",
        "* Task-2 Criteria 3: The task will assess your ability to perform transfer learning using a model from Huggingface and publish the new model to Huggingface platform.\n",
        "\n",
        "* Task-2 Criteria 4: This task will assess your ability to perform transfer learning and perform an evaluation. The accuracy submitted will be used in a Bell Curve Distribution where the average accuracy score will be mapped to a score of 70. This ensures fairness since the accuracy of all students who submit their accuracy score are taken into account and distributed evenly. For example, if the average students score is 56, those who submit their accuracy as 56 will get a score of 70; student with accuracy of 43 will get a score of 60; student with accuracy of 70 will get 80; naturally there is a gradation, meaning the accuracy between 43-56 will get a score between 60 to 70 and so on.\n",
        "\n",
        "* Task-2 Criteria 5: This task will assess your ability to use Gradio as a UI and interact with more than one models, in this case the model from Task-1 Criteria 1 and Task-2 Criteria 3.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZysTKHbGioh8"
      },
      "source": [
        "## Student Identity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJWjH2kGV49k"
      },
      "source": [
        "## Installation and Import Package"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "8wWESOr0PCJk"
      },
      "outputs": [],
      "source": [
        "# Install necessary packages\n",
        "#!pip install rggrader\n",
        "#from rggrader import submit, submit_image\n",
        "#!pip install transformers datasets evaluate huggingface_hub gradio rggrader accelerate -U\n",
        "# Put your code here:\n",
        "import torch\n",
        "import numpy as np\n",
        "from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification, TrainingArguments, Trainer, DefaultDataCollator\n",
        "from datasets import load_dataset\n",
        "from evaluate import load\n",
        "import gradio as gr\n",
        "from PIL import Image\n",
        "import requests\n",
        "from io import BytesIO\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor\n",
        "from huggingface_hub import notebook_login\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_mbLFq9Vvcg"
      },
      "source": [
        "## Task 1 Image Classification using Pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YwFqrph-vF8W"
      },
      "source": [
        "### Step 1: Image Classification using Hugging Face's Model\n",
        "\n",
        "In this first task, your task is to develop an image classification pipeline that takes **an image URL as input**, displays the image, and uses the Hugging Face's model to predict the age of the person in the image. You can get the model [here](https://huggingface.co/nateraw/vit-age-classifier).\n",
        "\n",
        "Here are the key steps that you might be able to follow:\n",
        "\n",
        "1. **Image URL Input:** Your program should accept an image URL as input. Make sure to handle potential issues with invalid URLs or inaccessible images.\n",
        "2. **Image Display:** Display the image from the URL in your notebook. This will provide a visual confirmation that the correct image is being processed.\n",
        "3. **Model Loading and Prediction:** Load the 'nateraw/vit-age-classifier' model from Hugging Face's model hub and pass the image URL to the model to obtain the prediction. The model should predict the age of the person in the image.\n",
        "4. **Output Display:** Display the output from the model in a clear and understandable manner.\n",
        "\n",
        "#### Submission\n",
        "\n",
        "- What percentage is the person in this picture (https://images.unsplash.com/photo-1596392927852-2a18c336fb78?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1280&q=80) is between age of \"3-9\"?\n",
        "\n",
        "Submit in the numeric format up to 5 digits behind the decimal point. For example in below output:\n",
        "\n",
        "```\n",
        "{'0-2': '0.00152',\n",
        " '3-9': '0.00105',\n",
        " '10-19': '0.02567',\n",
        " '20-29': '3.32545',\n",
        " '30-39': '51.75200',\n",
        " '40-49': '40.24234',\n",
        " '50-59': '4.47803',\n",
        " '60-69': '0.17092',\n",
        " 'more than 70': '0.00304'}\n",
        "```\n",
        "\n",
        "The answer would be `0.00105`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "H5LA1LcdPCJm"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Fast image processor class <class 'transformers.models.vit.image_processing_vit_fast.ViTImageProcessorFast'> is available for this model. Using slow image processor class. To use the fast image processor class set `use_fast=True`.\n",
            "Device set to use cpu\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'3-9': '0.81030', '0-2': '0.09773', '10-19': '0.04795', '20-29': '0.01472', '30-39': '0.01173'}\n",
            "0.81030\n"
          ]
        }
      ],
      "source": [
        "# @title #### 01. Image Classification using Hugging Face's Model\n",
        "\n",
        "# Put your code here:\n",
        "def predict_age_from_url(image_url):\n",
        "    # Load the model pipeline\n",
        "    classifier = pipeline(\"image-classification\", model=\"nateraw/vit-age-classifier\")\n",
        "    \n",
        "    # Get the image from URL\n",
        "    response = requests.get(image_url)\n",
        "    image = Image.open(BytesIO(response.content))\n",
        "    \n",
        "    # Display the image\n",
        "    plt.imshow(image)\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "    \n",
        "    # Make prediction\n",
        "    predictions = classifier(image)\n",
        "    \n",
        "    # Convert to dictionary format\n",
        "    results = {pred['label']: f\"{pred['score']:.5f}\" for pred in predictions}\n",
        "    return results\n",
        "\n",
        "# Test the function with the given URL\n",
        "url = \"https://images.unsplash.com/photo-1596392927852-2a18c336fb78?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1280&q=80\"\n",
        "results = predict_age_from_url(url)\n",
        "answer = results[\"3-9\"]\n",
        "\n",
        "print(results)\n",
        "print(answer)\n",
        "\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2EBNKYYvF8Y"
      },
      "source": [
        "# Submit Method\n",
        "assignment_id = \"00_pipeline_and_gradio\"\n",
        "question_id = \"01_image_classification_using_hugging_faces_model\"\n",
        "answer = \"\" # Put your answer here\n",
        "submit(student_id, name, assignment_id, answer, question_id, drive_link)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2wOiPqDiojo"
      },
      "source": [
        "### Step 2: Image Classification using Hugging Face's Model and Gradio\n",
        "\n",
        "In this second task, you will create a user-friendly interface using Gradio for your image classification pipeline that you created in Task 1. The difference with task 1 is, that in this task, you use **image files as input**, process them through the Hugging Face model, and display predictions output. The output displayed is **only the results with the highest `score`**.\n",
        "\n",
        "Here are the key steps that you might be able to follow:\n",
        "\n",
        "1. **Image Input:** Create a function to accept an image file as input. The image should be in a format that can be processed by the model.\n",
        "2. **Model Loading and Prediction:** Load the model from Hugging Face's model hub and pass the image to the model to obtain the prediction. The model predicts the age of the person in the image.\n",
        "3. **Gradio Interface:** Use Gradio to create a user-friendly interface for your application. The interface should allow users to upload an image file, and it should display the model's output in a clear and understandable manner.\n",
        "4. **Interface Launch:** Launch the Gradio interface. Make sure that the interface is accessible and easy to use.\n",
        "\n",
        "#### Submisssion\n",
        "\n",
        "![Upload colab](https://storage.googleapis.com/rg-ai-bootcamp/project-3-pipeline-and-gradio/upload-colab.png)\n",
        "\n",
        "You need to submit screenshot of your Gradio's app. In Google Colab you can just use the \"Folder\" sidebar and click the upload button. Make sure your screenshot match below requirements:\n",
        "\n",
        "- You should upload a person's image to that app\n",
        "- The score should be included at the screenshot\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "fsMSIbrwTKuB"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running on local URL:  http://127.0.0.1:7860\n",
            "Running on public URL: https://8294fb012508eb916b.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://8294fb012508eb916b.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": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# @title #### 02. Image Classification using Hugging Face's Model and Gradio\n",
        "\n",
        "# Put your code here:\n",
        "\n",
        "def predict_age(image):\n",
        "    if isinstance(image, np.ndarray):  # If image is a NumPy array, convert it\n",
        "        image = Image.fromarray(image)\n",
        "\n",
        "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "    classifier = pipeline(\"image-classification\", model=\"nateraw/vit-age-classifier\")\n",
        "    predictions = classifier(image)\n",
        "    # Get the prediction with highest score\n",
        "    max_pred = max(predictions, key=lambda x: x['score'])\n",
        "    return {\"score\": round(max_pred[\"score\"], 10), \"label\": max_pred[\"label\"]}\n",
        "\n",
        "demo = gr.Interface(\n",
        "    fn=predict_age,\n",
        "    inputs=gr.Image(),\n",
        "    outputs=\"json\",\n",
        "    title=\"Age Classification\"\n",
        ")\n",
        "\n",
        "demo.launch(share=True)\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SHFWYR7qvF8Z"
      },
      "source": [
        "Example of Expected Output:\n",
        "\n",
        "![gradio-result](https://storage.googleapis.com/rg-ai-bootcamp/project-3-pipeline-and-gradio/gradio-result.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhJMv03IvF8Z"
      },
      "source": [
        "# Submit Method\n",
        "question_id = \"02_image_classification_using_hugging_faces_model_and_gradio\"\n",
        "submit_image(student_id, question_id, './submission.jpg')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t8KSCR8OvF8Z"
      },
      "source": [
        "> Note: If your submission for Task-2 did not run (After you run it never changes from \"*\" to a number), stop the Code block that's running the Gradio app, then the submission will run. To stop the Code block, you can click on the Code block and then click the stop button."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SC8oMewavF8Z"
      },
      "source": [
        "# Task 2: Transfer Learning for Emotion Recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYbbazOuvF8Z"
      },
      "source": [
        "### Step 1: Environment Setup\n",
        "\n",
        "In this section, we start by installing the necessary packages and logging into Hugging Face's platform:\n",
        "- `transformers`\n",
        "- `datasets`\n",
        "- `evaluate`\n",
        "- `huggingface_hub`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "L8tVuUfnvF8a"
      },
      "outputs": [],
      "source": [
        "# Install necessary packages\n",
        "#!pip install datasets evaluate huggingface_hub accelerate -U\n",
        "# Put your code here:\n",
        "from transformers import AutoImageProcessor, AutoModelForImageClassification\n",
        "from transformers import TrainingArguments, Trainer, DefaultDataCollator\n",
        "from datasets import load_dataset\n",
        "from evaluate import load\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pp8aOoWDvF8a"
      },
      "source": [
        "After installing, use the Hugging Face's notebook login function to log into Hugging Face's platform. Execute the following commands in your cell:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "z98RbfLwvF8a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "73be34b7bf9145ca817702d7731b2d9e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Log into Hugging Face's platform\n",
        "\n",
        "# Put your code here:\n",
        "# Login to Hugging Face\n",
        "notebook_login()\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4abRjUVvF8a"
      },
      "source": [
        "### Step 2: Load the Dataset\n",
        "\n",
        "Load the \"FastJobs/Visual_Emotional_Analysis\" dataset and split it into training and test sets with a test size of 0.2.\n",
        "\n",
        "> **Note**: please assign to variable `emotion`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "zygwYIo3vF8a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e51a255537d44119a48fde9c5b9a2253",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Resolving data files:   0%|          | 0/800 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Load the dataset and split it\n",
        "\n",
        "# Update your code here:\n",
        "emotion = load_dataset(\"FastJobs/Visual_Emotional_Analysis\")\n",
        "emotion = emotion[\"train\"].train_test_split(test_size=0.2)\n",
        "\n",
        "# Create label mappings\n",
        "labels = emotion[\"train\"].features[\"label\"].names\n",
        "label2id = {label: str(i) for i, label in enumerate(labels)}\n",
        "id2label = {str(i): label for i, label in enumerate(labels)}\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-LwIylpvF8b"
      },
      "source": [
        "**Label Mapping**\n",
        "\n",
        "> **Note**: no need to change the code below! Just run it to map labels from the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "524AEqr1vF8b"
      },
      "outputs": [],
      "source": [
        "labels = emotion[\"train\"].features[\"label\"].names\n",
        "label2id, id2label = dict(), dict()\n",
        "for i, label in enumerate(labels):\n",
        "    label2id[label] = str(i)\n",
        "    id2label[str(i)] = label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZUaw3twvF8b"
      },
      "source": [
        "### Step 3: Explore and Visualize the Dataset\n",
        "\n",
        "In this step, you are required to visualize the first instance in the training dataset.\n",
        "\n",
        "> **Note**: no need to change the code below! Just run it to visualize the dataset based on index."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "F1qKjbgcvWJj"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the function to convert label index to label name\n",
        "id2label_view = {str(i): label for i, label in enumerate(labels)}\n",
        "\n",
        "# Use first training example\n",
        "image = emotion['train'][0]['image'] # Explore image by index\n",
        "label_id = str(emotion['train'][0]['label'])\n",
        "label_name = id2label_view[label_id]\n",
        "\n",
        "# Display the image and its corresponding label\n",
        "plt.imshow(image)\n",
        "plt.title(f'Label: {label_name} (ID: {label_id})')\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDm9XEzbvF8b"
      },
      "source": [
        "### Step 4: Preprocess the Data\n",
        "\n",
        "You need to define the transformation function for image preprocessing and apply it to the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "vNa7A9A4vF8b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Fast image processor class <class 'transformers.models.vit.image_processing_vit_fast.ViTImageProcessorFast'> is available for this model. Using slow image processor class. To use the fast image processor class set `use_fast=True`.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a17871c7b064a1885956e4d19c18768",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/640 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62bc3ba454b14aaca723dae76e0a7c33",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/160 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoImageProcessor\n",
        "from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor\n",
        "\n",
        "# Load the image processor, Define the transforms, Define the transformation function and Apply the transformation function\n",
        "\n",
        "# Put your code here:\n",
        "image_processor = AutoImageProcessor.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n",
        "normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)\n",
        "size = (\n",
        "    image_processor.size[\"height\"],\n",
        "    image_processor.size[\"width\"],\n",
        ")\n",
        "\n",
        "transform = Compose([RandomResizedCrop(size), ToTensor(),  Normalize(mean=image_processor.image_mean, std=image_processor.image_std)])\n",
        "\n",
        "def transform_example(example):\n",
        "    image = example[\"image\"]  # Extract image\n",
        "    image = transform(image)  # Apply transformation\n",
        "    example[\"pixel_values\"] = image  # Store transformed image\n",
        "    del example[\"image\"]  # Remove original image key to avoid errors\n",
        "    return example\n",
        "\n",
        "emotion_transformed = emotion.map(transform_example)\n",
        "\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1v9tSkvvF8c"
      },
      "source": [
        "### Step 5: Model Setup\n",
        "\n",
        "In this step, define the model architecture with the pre-trained ViT model and load it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "INJfQ16OvF8c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForImageClassification\n",
        "\n",
        "# Define the model\n",
        "\n",
        "# Put your code here:\n",
        "model = AutoModelForImageClassification.from_pretrained(\n",
        "    \"google/vit-base-patch16-224-in21k\",\n",
        "    num_labels=len(labels),\n",
        "    id2label=id2label,\n",
        "    label2id=label2id,\n",
        ")\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pppaVt5vF8c"
      },
      "source": [
        "### Step 6: Training Setup\n",
        "\n",
        "Define the training arguments and instantiate the trainer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "38g8OjfBvF8c"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "\n",
        "# Load accuracy metric\n",
        "\n",
        "# Put your code here:\n",
        "metric = load(\"accuracy\")\n",
        "\n",
        "\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "GccP5F4svF8c"
      },
      "outputs": [],
      "source": [
        "# Define compute metric function\n",
        "\n",
        "# Put your code here:\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = predictions.argmax(axis=1)\n",
        "    return metric.compute(predictions=predictions, references=labels)\n",
        "\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "sNC6OFLzvF8c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\lenovo\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\transformers\\training_args.py:1594: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "# Define training arguments\n",
        "\n",
        "# Update your code here:\n",
        "# Define training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"emotion-classifier\",\n",
        "    learning_rate=5e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    push_to_hub=True,\n",
        ")\n",
        "\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABFxOF9wvF8d"
      },
      "source": [
        "To use Hugging Face `Trainer` you need to install the `accelerate` library version `0.20.1` or later. It is used for performance enhancement on PyTorch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "NGeDe5JOvF8d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: accelerate in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (1.4.0)Note: you may need to restart the kernel to use updated packages.\n",
            "\n",
            "Requirement already satisfied: numpy<3.0.0,>=1.17 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from accelerate) (24.0)\n",
            "Requirement already satisfied: psutil in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from accelerate) (5.9.8)\n",
            "Requirement already satisfied: pyyaml in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from accelerate) (6.0.1)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "[notice] A new release of pip is available: 24.0 -> 25.0.1\n",
            "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch>=2.0.0 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from accelerate) (2.5.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.21.0 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from accelerate) (0.29.1)\n",
            "Requirement already satisfied: safetensors>=0.4.3 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from accelerate) (0.4.5)\n",
            "Requirement already satisfied: filelock in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from huggingface-hub>=0.21.0->accelerate) (3.16.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from huggingface-hub>=0.21.0->accelerate) (2024.10.0)\n",
            "Requirement already satisfied: requests in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from huggingface-hub>=0.21.0->accelerate) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from huggingface-hub>=0.21.0->accelerate) (4.66.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from huggingface-hub>=0.21.0->accelerate) (4.12.2)\n",
            "Requirement already satisfied: networkx in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from torch>=2.0.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from torch>=2.0.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: sympy==1.13.1 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from torch>=2.0.0->accelerate) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from sympy==1.13.1->torch>=2.0.0->accelerate) (1.2.1)\n",
            "Requirement already satisfied: colorama in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from tqdm>=4.42.1->huggingface-hub>=0.21.0->accelerate) (0.4.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from jinja2->torch>=2.0.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->huggingface-hub>=0.21.0->accelerate) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->huggingface-hub>=0.21.0->accelerate) (2023.7.22)\n"
          ]
        }
      ],
      "source": [
        "%pip install accelerate -U"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h95mzDGEvF8d"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
            "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
            "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
            "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        }
      ],
      "source": [
        "from transformers import DefaultDataCollator\n",
        "\n",
        "# Instantiate the trainer\n",
        "\n",
        "# Update your code here:\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=emotion_transformed[\"train\"],\n",
        "    eval_dataset=emotion_transformed[\"test\"],\n",
        "    compute_metrics=compute_metrics,\n",
        "    data_collator=DefaultDataCollator(),\n",
        ")\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUYlq1oMvF8j"
      },
      "source": [
        "If there are problems when using the Trainer after installing `accelerate` you can restart the Kernel"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-G3c0F5ZvF8j"
      },
      "source": [
        "### Step 7: Train and Evaluate the Model\n",
        "\n",
        "Now, you are ready to train the model and evaluate it on the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2GvoiY4mvF8k"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2' max='120' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [  2/120 : < :, Epoch 0.03/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Train the model\n",
        "\n",
        "# Put your code here:\n",
        "trainer.train()\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uVPRQNIRvF8k"
      },
      "outputs": [],
      "source": [
        "# Evaluate the model\n",
        "\n",
        "# Update your code here:\n",
        "eval_result = trainer.evaluate()\n",
        "\n",
        "# ---- End of your code ----\n",
        "\n",
        "# Save the formatted accuracy in a variable\n",
        "accuracy_str = \"{:.4f}\".format(eval_result[\"eval_accuracy\"])\n",
        "print(f\"Accuracy: {accuracy_str}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIv95upxvF8k"
      },
      "source": [
        "### Step 8: Publishing the Trained Model\n",
        "\n",
        "Finally, make sure to push your trained model to the Hugging Face Model Hub.\n",
        "\n",
        "> **Note**: No need to change the code below! Just run to publish your model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aZe2lXMNvF8k"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PRRzcPkvF8l"
      },
      "source": [
        "Once you've trained your model and pushed it to the Hugging Face Model Hub, you'll have a link that points directly to your model's page. You can share this link with others, and they can use it to directly load your model for their own uses.\n",
        "\n",
        "The following link is an example of what a trained model's page looks like: https://huggingface.co/aditira/emotion_classification. This is not your model, but rather an example of what your final result might resemble.\n",
        "\n",
        "Remember, for this project you should push your output model to your own Hugging Face account. The link for your model will be different and should reflect your own username and model name."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f72zoZKcvF8l"
      },
      "source": [
        "# Submit Method\n",
        "huggingface_model_link = \"\" # Put your model link\n",
        "\n",
        "assignment_id = \"00_transfer_learning\"\n",
        "question_id = \"00_emotion_recognition_huggingface\"\n",
        "submit(student_id, name, assignment_id, huggingface_model_link, question_id, drive_link)\n",
        "\n",
        "question_id = \"01_emotion_recognition_accuracy\"\n",
        "submit(student_id, name, assignment_id, accuracy_str, question_id, drive_link)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YNRc-bowvF8l"
      },
      "source": [
        "### Step 9:  Build an Interactive Application with Gradio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dt61YtQYvF8l"
      },
      "source": [
        "In this task, you will be building an interactive application using Gradio that will use your fine-tuned emotion recognition model along with another pretrained model ('`nateraw/vit-age-classifier`') to guess the emotion and age from an input image.\n",
        "\n",
        "Please make sure to:\n",
        "- Install the necessary package (`gradio`) for creating the web-based interface.\n",
        "- Load your fine-tuned model as well as the pretrained model '`nateraw/vit-age-classifier`'.\n",
        "- Define a function that will take an image as input and return the predicted emotion and age.\n",
        "- Utilize Gradio to create an Interface (UI) for your function, allowing users to upload images and see the predicted emotion and age."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VtyLNza9vF8l"
      },
      "outputs": [],
      "source": [
        "# Install Gradio\n",
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tD9pRSs2vF8m"
      },
      "source": [
        "#### Submisssion\n",
        "\n",
        "![Upload colab](https://storage.googleapis.com/rg-ai-bootcamp/project-3-pipeline-and-gradio/upload-colab.png)\n",
        "\n",
        "You need to submit screenshot of your Gradio's app. In Google Colab you can just use the \"Folder\" sidebar and click the upload button. Make sure your screenshot match below requirements:\n",
        "\n",
        "- Image name screenshot is `submission.jpg`\n",
        "- You should upload a person's image to that app\n",
        "- The score should be included at the screenshot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7nNft9g0vF8m"
      },
      "outputs": [],
      "source": [
        "# Put your code here:\n",
        "def predict_age_and_emotion(image):\n",
        "    # Age prediction\n",
        "    age_classifier = pipeline(\"image-classification\", model=\"nateraw/vit-age-classifier\")\n",
        "    age_pred = age_classifier(image)\n",
        "    max_age_pred = max(age_pred, key=lambda x: x['score'])\n",
        "    \n",
        "    # Emotion prediction\n",
        "    # Replace YOUR_USERNAME with your actual Hugging Face username\n",
        "    emotion_classifier = pipeline(\"image-classification\", model=\"YOUR_USERNAME/emotion-classifier\")\n",
        "    emotion_pred = emotion_classifier(image)\n",
        "    max_emotion_pred = max(emotion_pred, key=lambda x: x['score'])\n",
        "    \n",
        "    return (\n",
        "        f\"Age: {max_age_pred['label']} (confidence: {max_age_pred['score']:.5f})\",\n",
        "        f\"Emotion: {max_emotion_pred['label']} (confidence: {max_emotion_pred['score']:.5f})\"\n",
        "    )\n",
        "\n",
        "demo_combined = gr.Interface(\n",
        "    fn=predict_age_and_emotion,\n",
        "    inputs=gr.Image(),\n",
        "    outputs=[\"text\", \"text\"],\n",
        "    title=\"Age and Emotion Classification\"\n",
        ")\n",
        "\n",
        "demo_combined.launch()\n",
        "# ---- End of your code ----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7xHv6RycvF8m"
      },
      "source": [
        "Example of Expected Output:\n",
        "\n",
        "![gradio-result](https://storage.googleapis.com/rg-ai-bootcamp/project-4-transfer-learning/gradio_emotion_age_app.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYUSn0TgvF8m"
      },
      "source": [
        "> Note: If your submission for Task-2 did not run (After you run it never changes from \"*\" to a number), stop the Code block that's running the Gradio app, then the submission will run. To stop the Code block, you can click on the Code block and then click the stop button."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}