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  1. README.md +7 -5
  2. index.html +223 -19
  3. prompts.txt +1 -0
README.md CHANGED
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  ---
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- title: T1
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- emoji: 🏃
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- colorFrom: red
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- colorTo: yellow
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  sdk: static
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  pinned: false
 
 
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  ---
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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  ---
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+ title: t1
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+ emoji: 🐳
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+ colorFrom: purple
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+ colorTo: gray
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  sdk: static
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  pinned: false
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+ tags:
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+ - deepsite
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  ---
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
index.html CHANGED
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- <!doctype html>
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- <html>
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- <head>
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- <meta charset="utf-8" />
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- <meta name="viewport" content="width=device-width" />
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- <title>My static Space</title>
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- <link rel="stylesheet" href="style.css" />
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- </head>
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- <body>
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- <div class="card">
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- <h1>Welcome to your static Space!</h1>
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- <p>You can modify this app directly by editing <i>index.html</i> in the Files and versions tab.</p>
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- <p>
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- Also don't forget to check the
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- <a href="https://huggingface.co/docs/hub/spaces" target="_blank">Spaces documentation</a>.
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- </p>
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- </div>
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- </body>
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- </html>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <!DOCTYPE html>
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+ <html lang="en">
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+ <head>
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+ <meta charset="UTF-8">
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+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+ <title>Tensor Calculations Showcase</title>
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+ <script src="https://cdn.tailwindcss.com"></script>
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+ <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
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+ <style>
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+ .code-block {
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+ background-color: #1e293b;
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+ color: #f8fafc;
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+ border-radius: 0.5rem;
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+ padding: 1rem;
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+ font-family: 'Courier New', monospace;
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+ position: relative;
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+ }
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+ .copy-btn {
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+ position: absolute;
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+ top: 0.5rem;
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+ right: 0.5rem;
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+ background-color: #334155;
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+ color: white;
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+ border: none;
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+ border-radius: 0.25rem;
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+ padding: 0.25rem 0.5rem;
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+ cursor: pointer;
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+ font-size: 0.75rem;
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+ }
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+ .copy-btn:hover {
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+ background-color: #475569;
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+ }
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+ .tensor-visualization {
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+ display: flex;
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+ flex-direction: column;
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+ align-items: center;
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+ margin: 1rem 0;
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+ }
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+ .tensor-cell {
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+ border: 1px solid #cbd5e1;
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+ padding: 0.5rem;
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+ text-align: center;
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+ background-color: #f8fafc;
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+ }
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+ .tensor-row {
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+ display: flex;
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+ margin-bottom: 0.25rem;
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+ }
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+ .tab-content {
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+ display: none;
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+ }
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+ .tab-content.active {
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+ display: block;
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+ }
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+ .tab-button {
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+ transition: all 0.3s ease;
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+ }
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+ .tab-button.active {
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+ background-color: #3b82f6;
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+ color: white;
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+ }
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+ </style>
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+ </head>
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+ <body class="bg-gray-50 min-h-screen">
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+ <div class="container mx-auto px-4 py-8 max-w-6xl">
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+ <header class="text-center mb-12">
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+ <h1 class="text-4xl font-bold text-blue-600 mb-2">
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+ <i class="fas fa-calculator mr-2"></i>Tensor Calculations in Python
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+ </h1>
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+ <p class="text-gray-600 text-lg">
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+ Interactive showcase of tensor operations with NumPy and PyTorch
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+ </p>
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+ </header>
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+
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+ <div class="bg-white rounded-xl shadow-md overflow-hidden mb-8">
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+ <div class="p-6">
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+ <h2 class="text-2xl font-semibold text-gray-800 mb-4">Introduction to Tensors</h2>
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+ <p class="text-gray-600 mb-4">
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+ Tensors are multi-dimensional arrays that generalize scalars, vectors, and matrices to higher dimensions.
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+ They are fundamental in machine learning and deep learning frameworks.
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+ </p>
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+
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+ <div class="flex flex-wrap gap-2 mb-6">
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+ <button class="tab-button active px-4 py-2 rounded-lg bg-blue-100 text-blue-700" onclick="openTab(event, 'numpy-tab')">
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+ <i class="fas fa-table mr-2"></i>NumPy
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+ </button>
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+ <button class="tab-button px-4 py-2 rounded-lg bg-gray-100 text-gray-700" onclick="openTab(event, 'pytorch-tab')">
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+ <i class="fas fa-brain mr-2"></i>PyTorch
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+ </button>
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+ <button class="tab-button px-4 py-2 rounded-lg bg-gray-100 text-gray-700" onclick="openTab(event, 'visualization-tab')">
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+ <i class="fas fa-eye mr-2"></i>Visualization
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+ </button>
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+ </div>
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+
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+ <div id="numpy-tab" class="tab-content active">
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+ <h3 class="text-xl font-semibold text-gray-800 mb-3">NumPy Tensors</h3>
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+ <p class="text-gray-600 mb-4">
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+ NumPy provides powerful N-dimensional array objects that we can use as tensors.
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+ Here are some basic operations:
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+ </p>
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+
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+ <div class="grid md:grid-cols-2 gap-6">
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+ <div>
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+ <h4 class="font-medium text-gray-700 mb-2">Creating Tensors</h4>
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+ <div class="code-block mb-4">
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+ <button class="copy-btn" onclick="copyCode(this)">Copy</button>
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+ <pre>import numpy as np
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+
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+ # Scalar (0D tensor)
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+ scalar = np.array(5)
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+
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+ # Vector (1D tensor)
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+ vector = np.array([1, 2, 3])
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+
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+ # Matrix (2D tensor)
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+ matrix = np.array([[1, 2], [3, 4]])
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+
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+ # 3D tensor
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+ tensor_3d = np.array([[[1, 2], [3, 4]],
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+ [[5, 6], [7, 8]]])</pre>
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+ </div>
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+ </div>
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+
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+ <div>
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+ <h4 class="font-medium text-gray-700 mb-2">Basic Operations</h4>
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+ <div class="code-block">
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+ <button class="copy-btn" onclick="copyCode(this)">Copy</button>
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+ <pre># Element-wise addition
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+ a = np.array([1, 2, 3])
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+ b = np.array([4, 5, 6])
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+ result = a + b # [5, 7, 9]
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+
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+ # Matrix multiplication
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+ mat_a = np.array([[1, 2], [3, 4]])
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+ mat_b = np.array([[5, 6], [7, 8]])
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+ result = np.dot(mat_a, mat_b)
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+
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+ # Reshaping
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+ tensor = np.arange(8) # [0,1,2,3,4,5,6,7]
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+ reshaped = tensor.reshape((2, 4))</pre>
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+ </div>
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+ </div>
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+ </div>
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+ </div>
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+
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+ <div id="pytorch-tab" class="tab-content">
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+ <h3 class="text-xl font-semibold text-gray-800 mb-3">PyTorch Tensors</h3>
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+ <p class="text-gray-600 mb-4">
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+ PyTorch tensors are similar to NumPy arrays but with GPU acceleration support and automatic differentiation capabilities.
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+ </p>
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+
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+ <div class="grid md:grid-cols-2 gap-6">
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+ <div>
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+ <h4 class="font-medium text-gray-700 mb-2">Creating PyTorch Tensors</h4>
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+ <div class="code-block mb-4">
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+ <button class="copy-btn" onclick="copyCode(this)">Copy</button>
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+ <pre>import torch
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+
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+ # Create tensors
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+ scalar = torch.tensor(5)
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+ vector = torch.tensor([1., 2., 3.])
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+ matrix = torch.tensor([[1, 2], [3, 4]])
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+
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+ # GPU tensor (if available)
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+ if torch.cuda.is_available():
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+ gpu_tensor = torch.tensor([1, 2, 3], device='cuda')
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+
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+ # With gradient tracking
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+ x = torch.tensor(2., requires_grad=True)</pre>
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+ </div>
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+ </div>
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+
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+ <div>
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+ <h4 class="font-medium text-gray-700 mb-2">Autograd Example</h4>
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+ <div class="code-block">
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+ <button class="copy-btn" onclick="copyCode(this)">Copy</button>
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+ <pre># Automatic differentiation example
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+ x = torch.tensor(2., requires_grad=True)
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+ y = x**2 + 3*x + 1
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+
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+ # Compute gradient
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+ y.backward()
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+
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+ print(x.grad) # dy/dx = 2x + 3 → 7</pre>
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+ </div>
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+ <button onclick="runAutogradDemo()" class="mt-4 px-4 py-2 bg-blue-600 text-white rounded-lg hover:bg-blue-700 transition">
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+ <i class="fas fa-play mr-2"></i>Run Autograd Demo
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+ </button>
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+ <div id="autograd-result" class="mt-2 p-3 bg-gray-100 rounded hidden"></div>
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+ </div>
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+ </div>
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+ </div>
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+
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+ <div id="visualization-tab" class="tab-content">
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+ <h3 class="text-xl font-semibold text-gray-800 mb-3">Tensor Visualization</h3>
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+ <p class="text-gray-600 mb-4">
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+ Understanding tensor shapes and dimensions is crucial. Here's how different rank tensors look:
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+ </p>
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+
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+ <div class="grid md:grid-cols-3 gap-4">
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+ <div class="border rounded-lg p-4">
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+ <h4 class="font-medium text-center mb-3">Scalar (0D)</h4>
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+ <div class="tensor-visualization">
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+ <div class="tensor-cell" style="width: 60px;">5</div>
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+ </div>
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+ <p class="text-sm text-gray-500 mt-2">Shape: ()</p>
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+ </div>
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+
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+ <div class="border rounded-lg p-4">
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+ <h4 class="font-medium text-center mb-3">Vector (1D)</h4>
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+ <div class="tensor-visualization">
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+ <div class="tensor-row">
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+ <div class="tensor-cell">1</div>
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+ <div class="tensor-cell">2</div>
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+ <div class="tensor-cell">3</div>
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+ </div>
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+ </div>
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+ <p class="text-sm text-gray-500 mt-2">Shape: (3,)</p>
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+ </div>
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+
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+ <div class="border rounded-lg p-4">
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+ <h4 class="font-medium text-center mb-
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+ </html>
prompts.txt ADDED
@@ -0,0 +1 @@
 
 
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+ Create an python project to show case various tensor calculations, with simple examples and explanation.