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Ahmed","userId":"04367273378954502118"}}},"outputs":[],"source":["import tensorflow as tf\n","\n","#load the datasets\n","(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n","\n","#pre-process the data \n","x_train = tf.keras.utils.normalize(x_train, axis=1) \n","x_test = tf.keras.utils.normalize(x_test, axis=1)"]},{"cell_type":"code","source":["#define the model input and set the layers\n","model = tf.keras.models.Sequential()\n","model.add(tf.keras.layers.Flatten())\n","model.add(tf.keras.layers.Dense(128, activation=tf.nn.leaky_relu))\n","model.add(tf.keras.layers.Dense(128, activation=tf.nn.leaky_relu))\n","model.add(tf.keras.layers.Dense(10, activation=tf.nn.sigmoid))"],"metadata":{"id":"VhcjQDlMTfZK","executionInfo":{"status":"ok","timestamp":1672933298726,"user_tz":-300,"elapsed":663,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["#compile the model\n","model.compile(optimizer='adam',\n","              loss='sparse_categorical_crossentropy',\n","              metrics=['accuracy'])\n","\n","#train the model\n","model.fit(x_train, y_train, epochs=100)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yGdgJOF1Tlrb","executionInfo":{"status":"ok","timestamp":1672934154735,"user_tz":-300,"elapsed":846820,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}},"outputId":"0aba0e9f-92b8-4ffb-ab6e-31588a0cfab4"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","1563/1563 [==============================] - 9s 5ms/step - loss: 2.0518 - accuracy: 0.2489\n","Epoch 2/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.8665 - accuracy: 0.3308\n","Epoch 3/100\n","1563/1563 [==============================] - 9s 5ms/step - loss: 1.7763 - accuracy: 0.3670\n","Epoch 4/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.7220 - accuracy: 0.3851\n","Epoch 5/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.6738 - accuracy: 0.4045\n","Epoch 6/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.6351 - accuracy: 0.4158\n","Epoch 7/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.6004 - accuracy: 0.4314\n","Epoch 8/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.5682 - accuracy: 0.4414\n","Epoch 9/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.5413 - accuracy: 0.4533\n","Epoch 10/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.5130 - accuracy: 0.4594\n","Epoch 11/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.4869 - accuracy: 0.4686\n","Epoch 12/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.4654 - accuracy: 0.4766\n","Epoch 13/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.4448 - accuracy: 0.4854\n","Epoch 14/100\n","1563/1563 [==============================] - 9s 5ms/step - loss: 1.4263 - accuracy: 0.4907\n","Epoch 15/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.4053 - accuracy: 0.4983\n","Epoch 16/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.3901 - accuracy: 0.5037\n","Epoch 17/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.3656 - accuracy: 0.5123\n","Epoch 18/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.3544 - accuracy: 0.5150\n","Epoch 19/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.3386 - accuracy: 0.5194\n","Epoch 20/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.3210 - accuracy: 0.5285\n","Epoch 21/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.3063 - accuracy: 0.5359\n","Epoch 22/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.2877 - accuracy: 0.5405\n","Epoch 23/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.2772 - accuracy: 0.5405\n","Epoch 24/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.2640 - accuracy: 0.5451\n","Epoch 25/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.2488 - accuracy: 0.5525\n","Epoch 26/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.2418 - accuracy: 0.5567\n","Epoch 27/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.2190 - accuracy: 0.5651\n","Epoch 28/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.2133 - accuracy: 0.5673\n","Epoch 29/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.1933 - accuracy: 0.5713\n","Epoch 30/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.1834 - accuracy: 0.5742\n","Epoch 31/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.1739 - accuracy: 0.5782\n","Epoch 32/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.1581 - accuracy: 0.5848\n","Epoch 33/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.1482 - accuracy: 0.5877\n","Epoch 34/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.1385 - accuracy: 0.5919\n","Epoch 35/100\n","1563/1563 [==============================] - 9s 5ms/step - loss: 1.1241 - accuracy: 0.5948\n","Epoch 36/100\n","1563/1563 [==============================] - 9s 5ms/step - loss: 1.1159 - accuracy: 0.5981\n","Epoch 37/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.1023 - accuracy: 0.6040\n","Epoch 38/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0930 - accuracy: 0.6060\n","Epoch 39/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 1.0820 - accuracy: 0.6090\n","Epoch 40/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0742 - accuracy: 0.6145\n","Epoch 41/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0730 - accuracy: 0.6146\n","Epoch 42/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0502 - accuracy: 0.6210\n","Epoch 43/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0413 - accuracy: 0.6274\n","Epoch 44/100\n","1563/1563 [==============================] - 10s 7ms/step - loss: 1.0320 - accuracy: 0.6297\n","Epoch 45/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0226 - accuracy: 0.6329\n","Epoch 46/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0145 - accuracy: 0.6350\n","Epoch 47/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0119 - accuracy: 0.6351\n","Epoch 48/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.0004 - accuracy: 0.6398\n","Epoch 49/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9915 - accuracy: 0.6395\n","Epoch 50/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9771 - accuracy: 0.6492\n","Epoch 51/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9725 - accuracy: 0.6495\n","Epoch 52/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9659 - accuracy: 0.6507\n","Epoch 53/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9604 - accuracy: 0.6515\n","Epoch 54/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9474 - accuracy: 0.6580\n","Epoch 55/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9333 - accuracy: 0.6616\n","Epoch 56/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9343 - accuracy: 0.6630\n","Epoch 57/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9306 - accuracy: 0.6641\n","Epoch 58/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9178 - accuracy: 0.6684\n","Epoch 59/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9077 - accuracy: 0.6726\n","Epoch 60/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.9059 - accuracy: 0.6722\n","Epoch 61/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8924 - accuracy: 0.6766\n","Epoch 62/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8858 - accuracy: 0.6789\n","Epoch 63/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8821 - accuracy: 0.6830\n","Epoch 64/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8751 - accuracy: 0.6817\n","Epoch 65/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8654 - accuracy: 0.6862\n","Epoch 66/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8590 - accuracy: 0.6902\n","Epoch 67/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8537 - accuracy: 0.6917\n","Epoch 68/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8520 - accuracy: 0.6905\n","Epoch 69/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8399 - accuracy: 0.6955\n","Epoch 70/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8334 - accuracy: 0.6979\n","Epoch 71/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8299 - accuracy: 0.6987\n","Epoch 72/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8270 - accuracy: 0.7010\n","Epoch 73/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8177 - accuracy: 0.7033\n","Epoch 74/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8099 - accuracy: 0.7037\n","Epoch 75/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.8100 - accuracy: 0.7044\n","Epoch 76/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.7998 - accuracy: 0.7090\n","Epoch 77/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.7935 - accuracy: 0.7118\n","Epoch 78/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.7877 - accuracy: 0.7121\n","Epoch 79/100\n","1563/1563 [==============================] - 9s 5ms/step - loss: 0.7863 - accuracy: 0.7145\n","Epoch 80/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7796 - accuracy: 0.7167\n","Epoch 81/100\n","1563/1563 [==============================] - 10s 6ms/step - loss: 0.7786 - accuracy: 0.7171\n","Epoch 82/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7726 - accuracy: 0.7191\n","Epoch 83/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7586 - accuracy: 0.7224\n","Epoch 84/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7556 - accuracy: 0.7279\n","Epoch 85/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7504 - accuracy: 0.7289\n","Epoch 86/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7533 - accuracy: 0.7241\n","Epoch 87/100\n","1563/1563 [==============================] - 10s 6ms/step - loss: 0.7377 - accuracy: 0.7308\n","Epoch 88/100\n","1563/1563 [==============================] - 10s 6ms/step - loss: 0.7449 - accuracy: 0.7276\n","Epoch 89/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7326 - accuracy: 0.7330\n","Epoch 90/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.7242 - accuracy: 0.7364\n","Epoch 91/100\n","1563/1563 [==============================] - 8s 5ms/step - loss: 0.7183 - accuracy: 0.7398\n","Epoch 92/100\n","1563/1563 [==============================] - 9s 5ms/step - loss: 0.7179 - accuracy: 0.7375\n","Epoch 93/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7112 - accuracy: 0.7394\n","Epoch 94/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7092 - accuracy: 0.7413\n","Epoch 95/100\n","1563/1563 [==============================] - 9s 5ms/step - loss: 0.7106 - accuracy: 0.7423\n","Epoch 96/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.7051 - accuracy: 0.7420\n","Epoch 97/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.6970 - accuracy: 0.7458\n","Epoch 98/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.6919 - accuracy: 0.7470\n","Epoch 99/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.6856 - accuracy: 0.7502\n","Epoch 100/100\n","1563/1563 [==============================] - 9s 6ms/step - loss: 0.6826 - accuracy: 0.7491\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f4a48cf0820>"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["#evaluate the model\n","val_loss, val_acc = model.evaluate(x_test, y_test)\n","print(val_loss)\n","print(val_acc)\n","\n","#make predictions\n","predictions = model.predict(x_test)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZhGsPuxXTmK3","executionInfo":{"status":"ok","timestamp":1672934421625,"user_tz":-300,"elapsed":6841,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}},"outputId":"f2bfe037-3ab7-4844-efd9-026f9fb55532"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["313/313 [==============================] - 2s 5ms/step - loss: 2.2531 - accuracy: 0.4640\n","2.2531487941741943\n","0.46399998664855957\n","313/313 [==============================] - 1s 4ms/step\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Select 5 random images from the test set\n","indices = np.random.randint(0, len(x_test), size=1)\n","images = x_test[indices]\n","\n","# Make predictions for the selected images\n","predictions = model.predict(images)\n","\n","# Iterate over the images and predictions\n","for i, (image, prediction) in enumerate(zip(images, predictions)):\n","  # Convert the image to uint8 and reshape it to (32, 32, 3)\n","  image = np.uint8(image * 255).reshape(32, 32, 3)\n","\n","  # Get the class label and probability\n","  label = np.argmax(prediction)\n","  probability = prediction[label]\n","\n","  # Plot the image and the prediction\n","  plt.subplot(1, 5, i + 1)\n","  plt.imshow(image)\n","\n","  \n","# The labels of the CIFAR-10 dataset are represented as integers in the range 0 to 9. Each integer corresponds to a class of image:\n","\n","# 0: airplane\n","# 1: automobile\n","# 2: bird\n","# 3: cat\n","# 4: deer\n","# 5: dog\n","# 6: frog\n","# 7: horse\n","# 8: ship\n","# 9: truck\n","\n","  plt.title(\"Prediction: {} ({:.2f})\".format(label, probability))\n","\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":140},"id":"Sz8Xxj4yqT7w","executionInfo":{"status":"ok","timestamp":1672935210071,"user_tz":-300,"elapsed":613,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}},"outputId":"2e526988-cfd5-4e75-c924-f8fb9785c551"},"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 19ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["! pip install huggingface_hub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-BOR20s0tnwK","executionInfo":{"status":"ok","timestamp":1672937044533,"user_tz":-300,"elapsed":4653,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}},"outputId":"577173e1-078b-4e71-da07-8b1bf0b4ae22"},"execution_count":59,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.8/dist-packages (0.11.1)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.4.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (6.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (2.25.1)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (21.3)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (3.8.2)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.64.1)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (1.24.3)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2.10)\n","Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (4.0.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2022.12.7)\n"]}]},{"cell_type":"code","source":["! huggingface-cli login"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SCxq-BU20S_K","executionInfo":{"status":"ok","timestamp":1672937165299,"user_tz":-300,"elapsed":36898,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}},"outputId":"abaf65fb-dd6a-413c-84a0-ca91af0073b9"},"execution_count":61,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n","    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n","    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n","    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n","    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n","\n","    To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n","    \n","Token: \n","Add token as git credential? (Y/n) n\n","Token is valid.\n","Your token has been saved to /root/.huggingface/token\n","Login successful\n"]}]},{"cell_type":"code","source":["from huggingface_hub import notebook_login\n","notebook_login()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":330,"referenced_widgets":["5b5a47176ab04c32804af92e17e871b6","4d5f62389ff7492da35455d800c04438","a69bcb8726424b35bb19f60b82f26d2c","fc6e3dd0c0b44d6589e1cfec0469436d","4c4c19d28a1f4fafa1b9701a2f8d14d8","9908294eabfc430ebef39f3355dd3303","280e66498a664c68a4ea7983438332e2","ba3c27ad92834d8997a1dce38ac88c6f","22d653d942444fa89b5bd5e51794dc95","aeb2656c82094c60a0b7928fe8d3a8a6","2d5536dfb1fe40b79a51069da0b852d4","0670eae69d094fa49bb966ae10848bc1","6771b8f01420408abfdab9a2f410a027","8260ce9e624945c9bfb9f36e6570c74c","189bff7f908d40948b9d10b3e1c917fc","cedec1c62d5144779bc1ea4d4a1caebf","c7f372eb705649afb0e1737ecca1e8e3"]},"id":"psjlMKet02J_","executionInfo":{"status":"ok","timestamp":1672937201862,"user_tz":-300,"elapsed":640,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}},"outputId":"da00a680-81d4-49b7-abb4-cc74913cf0a5"},"execution_count":63,"outputs":[{"output_type":"stream","name":"stdout","text":["Token is valid.\n","Your token has been saved in your configured git credential helpers (store).\n","Your token has been saved to /root/.huggingface/token\n","Login successful\n"]}]},{"cell_type":"code","source":["from huggingface_hub import create_repo\n","create_repo(repo_id=\"test-model\")\n","'https://huggingface.co/zegoop/myModel1'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"W2aqtyCL1CQ_","executionInfo":{"status":"ok","timestamp":1672937305508,"user_tz":-300,"elapsed":1353,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}},"outputId":"ee7862e2-1e17-4b3c-8b59-13492c43a86a"},"execution_count":64,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'https://huggingface.co/zegoop/myModel1'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":64}]},{"cell_type":"code","source":["from huggingface_hub import upload_file\n","upload_file(\n","   path_or_fileobj=\"Cat&Dogs.ipynb\", \n","   path_in_repo=\"cat&dog.ipynb\", \n","   repo_id=\"zegoop/myModel1\"\n",")\n","'https://huggingface.co/zegoop/myModel1/blob/main/cat&dog.ipynb'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":399},"id":"bFBPBy5r2b9G","executionInfo":{"status":"error","timestamp":1672937735927,"user_tz":-300,"elapsed":2904,"user":{"displayName":"Waleed Ahmed","userId":"04367273378954502118"}},"outputId":"6ab8e84b-dd51-43ba-bc8a-72e481fc51dc"},"execution_count":65,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-65-71de4a2fd3cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mhuggingface_hub\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mupload_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m upload_file(\n\u001b[0m\u001b[1;32m      3\u001b[0m    \u001b[0mpath_or_fileobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Cat&Dogs.ipynb\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m    \u001b[0mpath_in_repo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"cat&dog.ipynb\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m    \u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"zegoop/myModel1\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    122\u001b[0m             )\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mupload_file\u001b[0;34m(self, path_or_fileobj, path_in_repo, repo_id, token, repo_type, revision, commit_message, commit_description, create_pr, parent_commit)\u001b[0m\n\u001b[1;32m   2066\u001b[0m             \u001b[0;32melse\u001b[0m \u001b[0;34mf\"Upload {path_in_repo} with huggingface_hub\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2067\u001b[0m         )\n\u001b[0;32m-> 2068\u001b[0;31m         operation = CommitOperationAdd(\n\u001b[0m\u001b[1;32m   2069\u001b[0m             \u001b[0mpath_or_fileobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_or_fileobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2070\u001b[0m             \u001b[0mpath_in_repo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_in_repo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/huggingface_hub/_commit_api.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path_in_repo, path_or_fileobj)\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/huggingface_hub/_commit_api.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    100\u001b[0m             \u001b[0mpath_or_fileobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath_or_fileobj\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[1;32m    101\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_fileobj\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[0;32m--> 102\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    103\u001b[0m                     \u001b[0;34mf\"Provided path: '{path_or_fileobj}' is not a file on the local\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m                     \u001b[0;34m\" file system\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Provided path: 'Cat&Dogs.ipynb' is not a file on the local file system"]}]}]}