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Untitled6.ipynb

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+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install requests transformers"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qgjLhqhtRoYL",
+        "outputId": "6bb6ee0e-a189-4fe5-c2f9-7e043734db1e"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)\n",
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2024.2.2)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.14.0)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
+            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
+            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
+            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (2023.6.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers) (4.11.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import requests\n",
+        "import zipfile\n",
+        "import os\n",
+        "\n",
+        "# URL du fichier zip sur Hugging Face\n",
+        "zip_url = 'https://huggingface.co/datasets/Dabococo/wheeloh_dataset/images'\n",
+        "\n",
+        "# Nom local du fichier zip\n",
+        "zip_file = 'images.zip'\n",
+        "\n",
+        "# Télécharger le fichier zip\n",
+        "response = requests.get(zip_url)\n",
+        "content_type = response.headers.get('Content-Type')\n",
+        "\n",
+        "# Vérifier que le fichier est un fichier zip\n",
+        "if 'zip' not in content_type:\n",
+        "    raise ValueError(\"Le fichier téléchargé n'est pas un fichier zip. Content-Type: {}\".format(content_type))\n",
+        "\n",
+        "# Sauvegarder le contenu téléchargé dans un fichier\n",
+        "with open(zip_file, 'wb') as f:\n",
+        "    f.write(response.content)\n",
+        "\n",
+        "# Vérifier la taille du fichier\n",
+        "file_size = os.path.getsize(zip_file)\n",
+        "print(\"Taille du fichier téléchargé:\", file_size, \"octets\")\n",
+        "\n",
+        "# Afficher le début du contenu du fichier pour vérifier\n",
+        "with open(zip_file, 'rb') as f:\n",
+        "    print(f.read(100))  # Lire les 100 premiers octets\n",
+        "\n",
+        "# Créer un répertoire pour extraire les fichiers\n",
+        "extract_dir = 'extracted_files'\n",
+        "os.makedirs(extract_dir, exist_ok=True)\n",
+        "\n",
+        "# Extraire le contenu du fichier zip\n",
+        "try:\n",
+        "    with zipfile.ZipFile(zip_file, 'r') as zip_ref:\n",
+        "        zip_ref.extractall(extract_dir)\n",
+        "    # Afficher les fichiers extraits\n",
+        "    extracted_files = os.listdir(extract_dir)\n",
+        "    print(\"Fichiers extraits :\", extracted_files)\n",
+        "except zipfile.BadZipFile:\n",
+        "    print(\"Erreur : le fichier téléchargé n'est pas un fichier zip valide.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "WX_U7Z6GRpTR",
+        "outputId": "5efdb53c-196c-4692-91ff-e9c131fce1f4"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "Le fichier téléchargé n'est pas un fichier zip. Content-Type: text/html; charset=utf-8",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-22-9e950cb9106e>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Vérifier que le fichier est un fichier zip\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'zip'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent_type\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Le fichier téléchargé n'est pas un fichier zip. Content-Type: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Sauvegarder le contenu téléchargé dans un fichier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Le fichier téléchargé n'est pas un fichier zip. Content-Type: text/html; charset=utf-8"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from torchvision.datasets import ImageFolder\n",
+        "import os\n",
+        "from torchvision.datasets.folder import has_file_allowed_extension, IMG_EXTENSIONS, default_loader\n",
+        "\n",
+        "class CustomImageFolder(ImageFolder):\n",
+        "    def __init__(self, root, transform=None, loader=default_loader, is_valid_file=None):\n",
+        "        super().__init__(root, transform=transform, loader=loader, is_valid_file=is_valid_file)\n",
+        "\n",
+        "    def find_classes(self, directory):\n",
+        "        # Ignorer les répertoires cachés\n",
+        "        classes = [d.name for d in os.scandir(directory) if d.is_dir() and not d.name.startswith('.')]\n",
+        "        classes.sort()\n",
+        "        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}\n",
+        "        return classes, class_to_idx\n",
+        "\n",
+        "    def make_dataset(self, directory, class_to_idx, extensions=None, is_valid_file=None, allow_empty=False):\n",
+        "        instances = []\n",
+        "        directory = os.path.expanduser(directory)\n",
+        "        both_none = extensions is None and is_valid_file is None\n",
+        "        if both_none:\n",
+        "            raise ValueError(\"Both extensions and is_valid_file cannot be None\")\n",
+        "        if extensions is not None:\n",
+        "            def is_valid_file(x):\n",
+        "                return has_file_allowed_extension(x, extensions)\n",
+        "\n",
+        "        for target_class in sorted(class_to_idx.keys()):\n",
+        "            class_index = class_to_idx[target_class]\n",
+        "            target_dir = os.path.join(directory, target_class)\n",
+        "            if not os.path.isdir(target_dir):\n",
+        "                continue\n",
+        "            for root, _, fnames in sorted(os.walk(target_dir)):\n",
+        "                for fname in sorted(fnames):\n",
+        "                    path = os.path.join(root, fname)\n",
+        "                    if is_valid_file(path) and not fname.startswith('.'):\n",
+        "                        item = path, class_index\n",
+        "                        instances.append(item)\n",
+        "\n",
+        "        if not allow_empty and len(instances) == 0:\n",
+        "            raise RuntimeError(f\"Found 0 files in subfolders of: {directory}. Supported extensions are: {','.join(extensions)}\")\n",
+        "\n",
+        "        return instances"
+      ],
+      "metadata": {
+        "id": "5y-PWXOnoH-k"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CuyZBWiyP88V",
+        "outputId": "fdef3381-9464-4433-c96d-2343bc22a1f6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:08<00:00,  1.86it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [1/10], Loss: 0.2799\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:08<00:00,  1.96it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [2/10], Loss: 0.0916\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:07<00:00,  2.08it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [3/10], Loss: 0.0356\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:08<00:00,  1.86it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [4/10], Loss: 0.0253\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:07<00:00,  2.25it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [5/10], Loss: 0.0101\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:09<00:00,  1.73it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [6/10], Loss: 0.0089\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:08<00:00,  1.81it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [7/10], Loss: 0.0096\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:07<00:00,  2.23it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [8/10], Loss: 0.0101\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:08<00:00,  1.85it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [9/10], Loss: 0.0631\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:07<00:00,  2.20it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [10/10], Loss: 0.0186\n",
+            "Finished Training\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "from torch.utils.data import DataLoader\n",
+        "from torchvision import transforms, models\n",
+        "from tqdm import tqdm\n",
+        "\n",
+        "# Configuration\n",
+        "batch_size = 32\n",
+        "num_epochs = 10\n",
+        "learning_rate = 0.001\n",
+        "num_classes = 2\n",
+        "\n",
+        "# Préparer les transformations\n",
+        "transform = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.ToTensor(),\n",
+        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+        "])\n",
+        "\n",
+        "# Charger les données d'entraînement avec CustomImageFolder\n",
+        "train_dataset = CustomImageFolder(root='/content/dataset/train', transform=transform)\n",
+        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n",
+        "\n",
+        "# Définir le modèle\n",
+        "model = models.resnet18(pretrained=True)\n",
+        "model.fc = nn.Linear(model.fc.in_features, num_classes)\n",
+        "model = model.to('cuda')\n",
+        "\n",
+        "# Définir la perte et l'optimiseur\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
+        "\n",
+        "# Utiliser le scaler pour l'AMP\n",
+        "scaler = torch.cuda.amp.GradScaler()\n",
+        "\n",
+        "# Entraînement\n",
+        "for epoch in range(num_epochs):\n",
+        "    model.train()\n",
+        "    running_loss = 0.0\n",
+        "    for inputs, labels in tqdm(train_loader):\n",
+        "        inputs, labels = inputs.to('cuda'), labels.to('cuda')\n",
+        "\n",
+        "        # Zero the parameter gradients\n",
+        "        optimizer.zero_grad()\n",
+        "\n",
+        "        # Forward pass with autocast\n",
+        "        with torch.cuda.amp.autocast():\n",
+        "            outputs = model(inputs)\n",
+        "            loss = criterion(outputs, labels)\n",
+        "\n",
+        "        # Backward pass with scaler\n",
+        "        scaler.scale(loss).backward()\n",
+        "        scaler.step(optimizer)\n",
+        "        scaler.update()\n",
+        "\n",
+        "        running_loss += loss.item() * inputs.size(0)\n",
+        "\n",
+        "    epoch_loss = running_loss / len(train_loader.dataset)\n",
+        "    print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {epoch_loss:.4f}')\n",
+        "\n",
+        "print('Finished Training')\n",
+        "\n",
+        "# Sauvegarder le modèle\n",
+        "torch.save(model.state_dict(), 'model.pth')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Ici pour charger le modèle"
+      ],
+      "metadata": {
+        "id": "aJBYukdNfx8P"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "from torchvision import models, transforms\n",
+        "from PIL import Image\n",
+        "\n",
+        "# Définir le modèle\n",
+        "num_classes = 2  # Canard et Perroquet\n",
+        "model = models.resnet18(pretrained=False)\n",
+        "model.fc = nn.Linear(model.fc.in_features, num_classes)\n",
+        "model = model.to('cuda')\n",
+        "\n",
+        "# Charger les poids du modèle enregistré\n",
+        "model.load_state_dict(torch.load('model.pth'))\n",
+        "model.eval()  # Mettre le modèle en mode évaluation\n",
+        "\n",
+        "# Définir les transformations\n",
+        "transform = transforms.Compose([\n",
+        "    transforms.Resize((224, 224)),\n",
+        "    transforms.ToTensor(),\n",
+        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+        "])\n",
+        "\n",
+        "# Charger et transformer une nouvelle image\n",
+        "def load_image(image_path):\n",
+        "    image = Image.open(image_path).convert('RGB')\n",
+        "    image = transform(image)\n",
+        "    image = image.unsqueeze(0)  # Ajouter une dimension pour le batch\n",
+        "    return image\n",
+        "\n",
+        "# Exemple de chargement d'une image\n",
+        "image_path = '/content/lg.jpeg'\n",
+        "image = load_image(image_path).to('cuda')\n",
+        "\n",
+        "# Passer l'image dans le modèle pour obtenir des prédictions\n",
+        "with torch.no_grad():  # Désactiver la grad pour l'inférence\n",
+        "    outputs = model(image)\n",
+        "    _, predicted = torch.max(outputs, 1)\n",
+        "\n",
+        "    classes = ['Alpine', 'Bugatti']\n",
+        "    predicted_class = classes[predicted.item()]\n",
+        "    print(f'Predicted class: {predicted_class}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oj0pgYdyQFXz",
+        "outputId": "13c95418-6dcd-440c-cb78-2edbfdb8b386"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class: Bugatti\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install transformers huggingface_hub"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cksp1PveZU22",
+        "outputId": "eb8bdf38-4cc2-42ce-a047-d02b22c004bb"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.0)\n",
+            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.23.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.14.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
+            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
+            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
+            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.11.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!huggingface-cli login"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c-3Qxby1ZWFV",
+        "outputId": "66148da3-bf28-4fa4-baf6-0034b8b5b35e"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
+            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
+            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
+            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
+            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
+            "\n",
+            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
+            "Enter your token (input will not be visible): \n",
+            "Add token as git credential? (Y/n) Y\n",
+            "Token is valid (permission: read).\n",
+            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
+            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
+            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
+            "\n",
+            "git config --global credential.helper store\n",
+            "\n",
+            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
+            "Token has not been saved to git credential helper.\n",
+            "Your token has been saved to /root/.cache/huggingface/token\n",
+            "Login successful\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import HfApi, HfFolder, Repository\n",
+        "\n",
+        "# Variables\n",
+        "model_path = \"Wheeloh-model_1.pth\"\n",
+        "repo_name = \"Wheeloh-model_1\"  # Remplacez par le nom de votre dépôt\n",
+        "commit_message = \"Initial commit\"\n",
+        "\n",
+        "# Se connecter à l'API\n",
+        "api = HfApi()\n",
+        "\n",
+        "# Obtenir le token d'authentification\n",
+        "token = HfFolder.get_token()\n",
+        "\n",
+        "# Cloner le dépôt Hugging Face\n",
+        "repo_url = api.create_repo(repo_name, token=token, exist_ok=True)\n",
+        "repo = Repository(local_dir=repo_name, clone_from=repo_url)\n",
+        "\n",
+        "# Copier le fichier du modèle dans le dépôt local\n",
+        "import shutil\n",
+        "shutil.copy(model_path, repo_name)\n",
+        "\n",
+        "# Pousser le modèle sur le hub\n",
+        "repo.push_to_hub(commit_message=commit_message)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 421,
+          "referenced_widgets": [
+            "a710457b00a849da9c24e1e1afb9d616",
+            "7b58972d4c704aa3bcc59a7a3c18682f",
+            "624e284bf09843a7a0b10256fbc416d0",
+            "e5b7191907914d75983b7e4b717b86d3",
+            "6434432da36a4755981ad001472e0cff",
+            "40a3244cfaa4404bb7ce4247564df6ed",
+            "234f2286e51c4a53b36584b49cd5a01f",
+            "66ade188379547aaae0724b4e3d87051",
+            "4f8c9691cd1b4faab91f7fb34348b716",
+            "7e6ba9a0ee6046b8961da2bf09d287ac",
+            "8ac32f54acd140b483ae523e22a9652b"
+          ]
+        },
+        "id": "LDyh9-MfZiMX",
+        "outputId": "e0e9ab72-d5df-4f21-eb3a-5226263071de"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:131: FutureWarning: 'Repository' (from 'huggingface_hub.repository') is deprecated and will be removed from version '1.0'. Please prefer the http-based alternatives instead. Given its large adoption in legacy code, the complete removal is only planned on next major release.\n",
+            "For more details, please read https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http.\n",
+            "  warnings.warn(warning_message, FutureWarning)\n",
+            "Cloning https://huggingface.co/Dabococo/Wheeloh-model_1 into local empty directory.\n",
+            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/Dabococo/Wheeloh-model_1 into local empty directory.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Upload file Wheeloh-model_1.pth:   0%|          | 1.00/42.7M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "a710457b00a849da9c24e1e1afb9d616"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "To https://huggingface.co/Dabococo/Wheeloh-model_1\n",
+            "   09e573f..8606a55  main -> main\n",
+            "\n",
+            "WARNING:huggingface_hub.repository:To https://huggingface.co/Dabococo/Wheeloh-model_1\n",
+            "   09e573f..8606a55  main -> main\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'https://huggingface.co/Dabococo/Wheeloh-model_1/commit/8606a554a318e75a54b8b841b31825c29f577de8'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    }
+  ]
+}

untitled6.py

@@ -0,0 +1,234 @@
+# -*- coding: utf-8 -*-
+"""Untitled6.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1b3-0ogrDvdw3WtHwOta0Ihlo46MAwDsk
+"""
+
+!pip install requests transformers
+
+import requests
+import zipfile
+import os
+
+# URL du fichier zip sur Hugging Face
+zip_url = 'https://huggingface.co/datasets/Dabococo/wheeloh_dataset/images'
+
+# Nom local du fichier zip
+zip_file = 'images.zip'
+
+# Télécharger le fichier zip
+response = requests.get(zip_url)
+content_type = response.headers.get('Content-Type')
+
+# Vérifier que le fichier est un fichier zip
+if 'zip' not in content_type:
+    raise ValueError("Le fichier téléchargé n'est pas un fichier zip. Content-Type: {}".format(content_type))
+
+# Sauvegarder le contenu téléchargé dans un fichier
+with open(zip_file, 'wb') as f:
+    f.write(response.content)
+
+# Vérifier la taille du fichier
+file_size = os.path.getsize(zip_file)
+print("Taille du fichier téléchargé:", file_size, "octets")
+
+# Afficher le début du contenu du fichier pour vérifier
+with open(zip_file, 'rb') as f:
+    print(f.read(100))  # Lire les 100 premiers octets
+
+# Créer un répertoire pour extraire les fichiers
+extract_dir = 'extracted_files'
+os.makedirs(extract_dir, exist_ok=True)
+
+# Extraire le contenu du fichier zip
+try:
+    with zipfile.ZipFile(zip_file, 'r') as zip_ref:
+        zip_ref.extractall(extract_dir)
+    # Afficher les fichiers extraits
+    extracted_files = os.listdir(extract_dir)
+    print("Fichiers extraits :", extracted_files)
+except zipfile.BadZipFile:
+    print("Erreur : le fichier téléchargé n'est pas un fichier zip valide.")
+
+from torchvision.datasets import ImageFolder
+import os
+from torchvision.datasets.folder import has_file_allowed_extension, IMG_EXTENSIONS, default_loader
+
+class CustomImageFolder(ImageFolder):
+    def __init__(self, root, transform=None, loader=default_loader, is_valid_file=None):
+        super().__init__(root, transform=transform, loader=loader, is_valid_file=is_valid_file)
+
+    def find_classes(self, directory):
+        # Ignorer les répertoires cachés
+        classes = [d.name for d in os.scandir(directory) if d.is_dir() and not d.name.startswith('.')]
+        classes.sort()
+        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
+        return classes, class_to_idx
+
+    def make_dataset(self, directory, class_to_idx, extensions=None, is_valid_file=None, allow_empty=False):
+        instances = []
+        directory = os.path.expanduser(directory)
+        both_none = extensions is None and is_valid_file is None
+        if both_none:
+            raise ValueError("Both extensions and is_valid_file cannot be None")
+        if extensions is not None:
+            def is_valid_file(x):
+                return has_file_allowed_extension(x, extensions)
+
+        for target_class in sorted(class_to_idx.keys()):
+            class_index = class_to_idx[target_class]
+            target_dir = os.path.join(directory, target_class)
+            if not os.path.isdir(target_dir):
+                continue
+            for root, _, fnames in sorted(os.walk(target_dir)):
+                for fname in sorted(fnames):
+                    path = os.path.join(root, fname)
+                    if is_valid_file(path) and not fname.startswith('.'):
+                        item = path, class_index
+                        instances.append(item)
+
+        if not allow_empty and len(instances) == 0:
+            raise RuntimeError(f"Found 0 files in subfolders of: {directory}. Supported extensions are: {','.join(extensions)}")
+
+        return instances
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torchvision import transforms, models
+from tqdm import tqdm
+
+# Configuration
+batch_size = 32
+num_epochs = 10
+learning_rate = 0.001
+num_classes = 2
+
+# Préparer les transformations
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Charger les données d'entraînement avec CustomImageFolder
+train_dataset = CustomImageFolder(root='/content/dataset/train', transform=transform)
+train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
+
+# Définir le modèle
+model = models.resnet18(pretrained=True)
+model.fc = nn.Linear(model.fc.in_features, num_classes)
+model = model.to('cuda')
+
+# Définir la perte et l'optimiseur
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+# Utiliser le scaler pour l'AMP
+scaler = torch.cuda.amp.GradScaler()
+
+# Entraînement
+for epoch in range(num_epochs):
+    model.train()
+    running_loss = 0.0
+    for inputs, labels in tqdm(train_loader):
+        inputs, labels = inputs.to('cuda'), labels.to('cuda')
+
+        # Zero the parameter gradients
+        optimizer.zero_grad()
+
+        # Forward pass with autocast
+        with torch.cuda.amp.autocast():
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
+
+        # Backward pass with scaler
+        scaler.scale(loss).backward()
+        scaler.step(optimizer)
+        scaler.update()
+
+        running_loss += loss.item() * inputs.size(0)
+
+    epoch_loss = running_loss / len(train_loader.dataset)
+    print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {epoch_loss:.4f}')
+
+print('Finished Training')
+
+# Sauvegarder le modèle
+torch.save(model.state_dict(), 'model.pth')
+
+#Ici pour charger le modèle
+
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+
+# Définir le modèle
+num_classes = 2  # Canard et Perroquet
+model = models.resnet18(pretrained=False)
+model.fc = nn.Linear(model.fc.in_features, num_classes)
+model = model.to('cuda')
+
+# Charger les poids du modèle enregistré
+model.load_state_dict(torch.load('model.pth'))
+model.eval()  # Mettre le modèle en mode évaluation
+
+# Définir les transformations
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Charger et transformer une nouvelle image
+def load_image(image_path):
+    image = Image.open(image_path).convert('RGB')
+    image = transform(image)
+    image = image.unsqueeze(0)  # Ajouter une dimension pour le batch
+    return image
+
+# Exemple de chargement d'une image
+image_path = '/content/lg.jpeg'
+image = load_image(image_path).to('cuda')
+
+# Passer l'image dans le modèle pour obtenir des prédictions
+with torch.no_grad():  # Désactiver la grad pour l'inférence
+    outputs = model(image)
+    _, predicted = torch.max(outputs, 1)
+
+    classes = ['Alpine', 'Bugatti']
+    predicted_class = classes[predicted.item()]
+    print(f'Predicted class: {predicted_class}')
+
+!pip install transformers huggingface_hub
+
+!huggingface-cli login
+
+from huggingface_hub import HfApi, HfFolder, Repository
+
+# Variables
+model_path = "Wheeloh-model_1.pth"
+repo_name = "Wheeloh-model_1"  # Remplacez par le nom de votre dépôt
+commit_message = "Initial commit"
+
+# Se connecter à l'API
+api = HfApi()
+
+# Obtenir le token d'authentification
+token = HfFolder.get_token()
+
+# Cloner le dépôt Hugging Face
+repo_url = api.create_repo(repo_name, token=token, exist_ok=True)
+repo = Repository(local_dir=repo_name, clone_from=repo_url)
+
+# Copier le fichier du modèle dans le dépôt local
+import shutil
+shutil.copy(model_path, repo_name)
+
+# Pousser le modèle sur le hub
+repo.push_to_hub(commit_message=commit_message)