{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0o6ydNXBF14"
      },
      "source": [
        "# Text task notebook template\n",
        "## Loading the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "collapsed": true,
        "id": "EqgD_yfoBF18",
        "outputId": "96e007c1-dbf7-457e-ca2e-8d9b56ac0f45"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'fastapi'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-dd0a0c7fd244>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfastapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAPIRouter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fastapi'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "from fastapi import APIRouter\n",
        "from datetime import datetime\n",
        "from datasets import load_dataset\n",
        "from sklearn.metrics import accuracy_score\n",
        "import random\n",
        "\n",
        "import sys\n",
        "sys.path.append('../tasks')\n",
        "\n",
        "from utils.evaluation import AudioEvaluationRequest\n",
        "from utils.emissions import tracker, clean_emissions_data, get_space_info\n",
        "\n",
        "\n",
        "# Define the label mapping\n",
        "LABEL_MAPPING = {\n",
        "    \"chainsaw\": 0,\n",
        "    \"environment\": 1\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RB3_Wg3oBF2B"
      },
      "source": [
        "## Loading the datasets and splitting them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "668da7bf85434e098b95c3ec447d78fe",
            "5b68d43359eb429395da8be7d4b15556",
            "140a304773914e9db8f698eabeb40298",
            "6d04e8ab1906400e8e0029949dc523a5"
          ]
        },
        "id": "TA2feB2HBF2B",
        "outputId": "a72743ae-c969-473a-c9be-8a76e1122b9d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "668da7bf85434e098b95c3ec447d78fe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\datasets--QuotaClimat--frugalaichallenge-text-train. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
            "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
            "  warnings.warn(message)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5b68d43359eb429395da8be7d4b15556",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "train.parquet:   0%|          | 0.00/1.21M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "140a304773914e9db8f698eabeb40298",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/6091 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d04e8ab1906400e8e0029949dc523a5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/6091 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "request = AudioEvaluationRequest()\n",
        "\n",
        "# Load and prepare the dataset\n",
        "dataset = load_dataset(request.dataset_name)\n",
        "\n",
        "# Split dataset\n",
        "train_test = dataset[\"train\"]\n",
        "test_dataset = dataset[\"test\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGvNFfpBBF2C"
      },
      "source": [
        "## Random Baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ogi8skzqBF2E"
      },
      "outputs": [],
      "source": [
        "# Start tracking emissions\n",
        "tracker.start()\n",
        "tracker.start_task(\"inference\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_g9uaAC3BF2E",
        "outputId": "c4331390-f24f-46af-d9d1-e41273ee1ca3"
      },
      "outputs": [
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            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "#--------------------------------------------------------------------------------------------\n",
        "# YOUR MODEL INFERENCE CODE HERE\n",
        "# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
        "#--------------------------------------------------------------------------------------------\n",
        "\n",
        "# Make random predictions (placeholder for actual model inference)\n",
        "true_labels = test_dataset[\"label\"]\n",
        "import torch\n",
        "from transformers import pipeline\n",
        "from sklearn import preprocessing\n",
        "#encoded_data_fine_tuned_model = train_test[\"train\"].map(preprocess_function, remove_columns=\"audio\", batched=True)\n",
        "\n",
        "from datasets import Dataset\n",
        "\n",
        "# Utilisation du pipeline directement sur le dataset\n",
        "classifier = pipeline(\"audio-classification\", model=\"CindyDelage/Challenge_HuggingFace_DFG_FrugalAI\", feature_extractor=feature_extractor)\n",
        "\n",
        "# Correctly access the audio data\n",
        "audio_data = [example[\"array\"] for example in dataset[\"test\"][\"audio\"]]\n",
        "\n",
        "# Prédiction sur tout le dataset\n",
        "results = classifier(audio_data, batch_size=8)\n",
        "\n",
        "predictions = []\n",
        "for result in results:\n",
        "    # Check if result is a dictionary\n",
        "    if isinstance(result, dict):\n",
        "        # Get the label with the highest score\n",
        "        predicted_label = result['label']\n",
        "    else:\n",
        "        # If result is not a dictionary, access it as a list\n",
        "        predicted_label = result[0]['label']  # Assuming the dictionary is the first element\n",
        "\n",
        "    # Assign 1 for \"environment\", 0 for \"chainsaw\"\n",
        "    if predicted_label == 'environment':\n",
        "        predictions.append(1)\n",
        "    else:\n",
        "        predictions.append(0)\n",
        "\n",
        "predictions\n",
        "\n",
        "#--------------------------------------------------------------------------------------------\n",
        "# YOUR MODEL INFERENCE STOPS HERE\n",
        "#--------------------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XyF13FJeBF2E",
        "outputId": "258cf14c-1653-44dc-95b8-920df4dc5d32"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[codecarbon WARNING @ 19:53:32] Background scheduler didn't run for a long period (47s), results might be inaccurate\n",
            "[codecarbon INFO @ 19:53:32] Energy consumed for RAM : 0.000156 kWh. RAM Power : 11.755242347717285 W\n",
            "[codecarbon INFO @ 19:53:32] Delta energy consumed for CPU with constant : 0.000564 kWh, power : 42.5 W\n",
            "[codecarbon INFO @ 19:53:32] Energy consumed for All CPU : 0.000564 kWh\n",
            "[codecarbon INFO @ 19:53:32] 0.000720 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "EmissionsData(timestamp='2025-01-21T19:53:32', project_name='codecarbon', run_id='908f2e7e-4bb2-4991-a0f6-56bf8d7eda21', experiment_id='5b0fa12a-3dd7-45bb-9766-cc326314d9f1', duration=47.736408500000834, emissions=4.032368007471064e-05, emissions_rate=8.444466886328872e-07, cpu_power=42.5, gpu_power=0.0, ram_power=11.755242347717285, cpu_energy=0.0005636615353475565, gpu_energy=0, ram_energy=0.00015590305493261682, energy_consumed=0.0007195645902801733, country_name='France', country_iso_code='FRA', region='île-de-france', cloud_provider='', cloud_region='', os='Windows-11-10.0.22631-SP0', python_version='3.12.7', codecarbon_version='3.0.0_rc0', cpu_count=12, cpu_model='13th Gen Intel(R) Core(TM) i7-1365U', gpu_count=None, gpu_model=None, longitude=2.3494, latitude=48.8558, ram_total_size=31.347312927246094, tracking_mode='machine', on_cloud='N', pue=1.0)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Stop tracking emissions\n",
        "emissions_data = tracker.stop_task()\n",
        "emissions_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wBVdK2TaBF2F",
        "outputId": "39596c2e-2734-4b37-922d-647d771efe0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.10090237899917966"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(true_labels, predictions)\n",
        "accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eIjuJJrKBF2G",
        "outputId": "bf691025-6f9d-4e3d-9e1e-75f4bfbcd70c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'submission_timestamp': '2025-01-21T19:53:46.639165',\n",
              " 'accuracy': 0.10090237899917966,\n",
              " 'energy_consumed_wh': 0.7195645902801733,\n",
              " 'emissions_gco2eq': 0.040323680074710634,\n",
              " 'emissions_data': {'run_id': '908f2e7e-4bb2-4991-a0f6-56bf8d7eda21',\n",
              "  'duration': 47.736408500000834,\n",
              "  'emissions': 4.032368007471064e-05,\n",
              "  'emissions_rate': 8.444466886328872e-07,\n",
              "  'cpu_power': 42.5,\n",
              "  'gpu_power': 0.0,\n",
              "  'ram_power': 11.755242347717285,\n",
              "  'cpu_energy': 0.0005636615353475565,\n",
              "  'gpu_energy': 0,\n",
              "  'ram_energy': 0.00015590305493261682,\n",
              "  'energy_consumed': 0.0007195645902801733,\n",
              "  'country_name': 'France',\n",
              "  'country_iso_code': 'FRA',\n",
              "  'region': 'île-de-france',\n",
              "  'cloud_provider': '',\n",
              "  'cloud_region': '',\n",
              "  'os': 'Windows-11-10.0.22631-SP0',\n",
              "  'python_version': '3.12.7',\n",
              "  'codecarbon_version': '3.0.0_rc0',\n",
              "  'cpu_count': 12,\n",
              "  'cpu_model': '13th Gen Intel(R) Core(TM) i7-1365U',\n",
              "  'gpu_count': None,\n",
              "  'gpu_model': None,\n",
              "  'ram_total_size': 31.347312927246094,\n",
              "  'tracking_mode': 'machine',\n",
              "  'on_cloud': 'N',\n",
              "  'pue': 1.0},\n",
              " 'dataset_config': {'dataset_name': 'QuotaClimat/frugalaichallenge-text-train',\n",
              "  'test_size': 0.2,\n",
              "  'test_seed': 42}}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Prepare results dictionary\n",
        "results = {\n",
        "    \"submission_timestamp\": datetime.now().isoformat(),\n",
        "    \"accuracy\": float(accuracy),\n",
        "    \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
        "    \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
        "    \"emissions_data\": clean_emissions_data(emissions_data),\n",
        "    \"dataset_config\": {\n",
        "        \"dataset_name\": request.dataset_name,\n",
        "        \"test_size\": request.test_size,\n",
        "        \"test_seed\": request.test_seed\n",
        "    }\n",
        "}\n",
        "\n",
        "results"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "base",
      "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": "3.12.7"
    },
    "colab": {
      "provenance": []
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  "nbformat": 4,
  "nbformat_minor": 0
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