Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+mnist_model.keras filter=lfs diff=lfs merge=lfs -text

hello.py

@@ -0,0 +1,16 @@
+import matplotlib.pyplot as plt
+
+# Sample data
+x = [1, 2, 3, 4, 5]
+y = [2, 4, 6, 8, 10]
+
+# Create a line plot
+plt.plot(x, y)
+
+# Add title and labels
+plt.title('Simple Line Plot')
+plt.xlabel('X-axis')
+plt.ylabel('Y-axis')
+
+# Show the plot
+plt.show()

main.py

@@ -0,0 +1,13 @@
+import json
+
+
+data ={'age':21,'name':'tmna'}
+
+json_str = json.dumps(data)
+print(json_str)
+print(type(json_str))
+
+parsed_str = json.loads(json_str)
+print(parsed_str)
+print(type(parsed_str))
+

mnist_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b047305752416c87e00cf0cf6eb684ab43b5ddd66538331ed537a85149f6d89
+size 1244359

module/h.ipynb

@@ -0,0 +1,55 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "66977768",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ec40ac2",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8082fa30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"hello\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

module/hello.py

@@ -0,0 +1,4 @@
+from package.main import *
+print(add(2,9)) 
+
+print(area(3))

module/package/__init__.py

@@ -0,0 +1 @@
+#sample

module/package/main.py

@@ -0,0 +1,5 @@
+def add(a,b):
+    return a+b
+
+def area(r):
+    return 3.14*r*r

new.ipynb

@@ -0,0 +1,85 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c1f714e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "(x_train,y_train),(x_test,y_test) = tf.keras.datasets.mnist.load_data()\n",
+    "\n",
+    "x_train,x_test = x_train/255.0,x_test/255.0\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "model = keras.models.Sequential([\n",
+    "    keras.layers.Flatten(input_shape=(28,28)),\n",
+    "    keras.layers.Dense(128,activation='relu'),\n",
+    "    keras.layers.Dense(10,activation='softmax')\n",
+    "    \n",
+    "    \n",
+    "])\n",
+    "\n",
+    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
+    "model.fit(x_train,y_train,epochs=5)\n",
+    "\n",
+    "model.save(\"mnist_model.keras\")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af40fe46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import HfApi\n",
+    "repo_id=\"turab31/mnist-model\"\n",
+    "api = HfApi()\n",
+    "api.create_repo(repo_id=repo_id,exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bec5662",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import upload_folder\n",
+    "upload_folder(folder_path=\"\",repo_id=repo_id,repo_type=\"model\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

numpy/one.py

@@ -0,0 +1,17 @@
+import numpy as np
+a=np.array([[1,2,3],
+            [3,4,5]])
+# print(a.shape)
+
+a=np.array([1,2,3,4,15,89])
+# # slicing from start
+# print(a[:3])
+# # alicing from end
+# print(a[3:])
+
+# print(np.zeros((6,3,4)))
+
+
+arr = ([2,3,78,68])
+# print(np.sort(arr))
+print(np.argsort(arr))

o.ipynb

@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "283a8867",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
+      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "       [[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "       [[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "       [[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "       [[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras \n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "(x_train,y_train),(x_test,y_test) = tf.keras.datasets.mnist.load_data()\n",
+    "\n",
+    "x_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c4566813",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x263602585f0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(x_train[2],cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a2893d80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
+       "\n",
+       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        ...,\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.],\n",
+       "        [0., 0., 0., ..., 0., 0., 0.]]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train,x_test=x_train/255.0,x_test/255.0\n",
+    "x_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9d3b090a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\najaf\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'x_train' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m      3\u001b[39m model = keras.models.Sequential([\n\u001b[32m      4\u001b[39m     keras.layers.Flatten(input_shape=(\u001b[32m28\u001b[39m,\u001b[32m28\u001b[39m)),\n\u001b[32m      5\u001b[39m     keras.layers.Dense(\u001b[32m128\u001b[39m,activation=\u001b[33m'\u001b[39m\u001b[33mrelu\u001b[39m\u001b[33m'\u001b[39m),\n\u001b[32m   (...)\u001b[39m\u001b[32m      8\u001b[39m \n\u001b[32m      9\u001b[39m ])\n\u001b[32m     10\u001b[39m model.compile(optimizer=\u001b[33m'\u001b[39m\u001b[33madam\u001b[39m\u001b[33m'\u001b[39m,loss=\u001b[33m'\u001b[39m\u001b[33msparse_categorical_crossentropy\u001b[39m\u001b[33m'\u001b[39m,metrics=[\u001b[33m'\u001b[39m\u001b[33maccuracy\u001b[39m\u001b[33m'\u001b[39m])\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m model.fit(\u001b[43mx_train\u001b[49m,y_train,epochs=\u001b[32m5\u001b[39m)\n",
+      "\u001b[31mNameError\u001b[39m: name 'x_train' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "model = keras.models.Sequential([\n",
+    "    keras.layers.Flatten(input_shape=(28,28)),\n",
+    "    keras.layers.Dense(128,activation='relu'),\n",
+    "    keras.layers.Dense(10,activation='softmax')\n",
+    "    \n",
+    "    \n",
+    "])\n",
+    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
+    "model.fit(x_train,y_train,epochs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd5b05dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'model' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m.fit(x_train,y_train,epochs=\u001b[32m5\u001b[39m)\n",
+      "\u001b[31mNameError\u001b[39m: name 'model' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(x_train,y_train,epochs=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a8d6734",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9685 - loss: 0.1016\n",
+      "{test_accuracy*100:.2f}%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_loss,test_accuracy = model.evaluate(x_test,y_test)\n",
+    "print('{test_accuracy*100:.2f}%')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a7179f4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'model' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mmodel\u001b[49m.fit(x_train,y_train,epochs=\u001b[33m'\u001b[39m\u001b[33m5\u001b[39m\u001b[33m'\u001b[39m)\n",
+      "\u001b[31mNameError\u001b[39m: name 'model' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(x_train,y_train,epochs='5')"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

one.py

@@ -0,0 +1,27 @@
+import tensorflow as tf
+from tensorflow import keras
+
+import numpy as np
+import matplotlib.pyplot as plt
+from huggingface_hub import login
+
+(x_train,y_train),(x_test,y_test) = tf.keras.datasets.mnist.load_data()
+
+x_train,x_test = x_train/255.0,x_test/255.0
+
+import tensorflow as tf
+from tensorflow import keras
+model = keras.models.Sequential([
+    keras.layers.Flatten(input_shape=(28,28)),
+    keras.layers.Dense(128,activation='relu'),
+    keras.layers.Dense(10,activation='softmax')
+    
+    
+])
+
+model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
+model.fit(x_train,y_train,epochs=5)
+
+model.save("mnist_model.keras")
+
+login()