update

Author: noahho

docs/docs.md

@@ -1,179 +1,40 @@
-#  
-
 <style>
 .prior-labs-hero {
-  position: relative;
-  width: 100%;
-  height: 00px;
-  margin: 2rem 0;
-  overflow: hidden;
   margin-top: -4rem;
 }
 
-.grid-container {
-  display: grid;
-  grid-template-columns: repeat(60, 1fr);
-  grid-template-rows: repeat(26, 1fr);
-  width: 100%;
-  height: 100%;
-  gap: 0.25%;
-}
-
-.square {
-  background-color: rgba(96, 191, 129, 0.7);
-  transition: background-color 0.5s ease;
-  border-radius: 0px;
-}
-
-.square.active {
-  background-color: rgba(96, 191, 129, 1);
-}
-
-.square.missing {
-  background-color: transparent;
-}
-
-.hero-content {
-  position: absolute;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%);
-  text-align: center;
-  z-index: 10;
-  padding: 2rem;
-  border-radius: 8px;
-}
-
-.hero-title {
-  font-size: 2.5rem !important;
-  font-weight: 700 !important;
-  color: rgb(96, 191, 129) !important;
-  margin-bottom: 1rem !important;
-  line-height: 1.2 !important;
-}
-
-.hero-subtitle {
-  font-size: 1.2rem !important;
-  color: rgb(96, 191, 129) !important;
-  max-width: 600px;
-  margin: 0 auto !important;
-  line-height: 1.4 !important;
+/* Style for the top boxes */
+.grid.cards.up > ul > li  {
+  background-color: rgba(25, 36, 74, 0.03); /* Primary color with slight opacity */
 }
 </style>
+# TabPFN Integrations
 
-<div class="prior-labs-hero">
-  <div class="grid-container">
-    <!-- Squares will be generated by JavaScript -->
-  </div>
-</div>
-
-<script>
-document.addEventListener('DOMContentLoaded', () => {
-  const gridContainer = document.querySelector('.grid-container');
-  const columns = 60;
-  const rows = 25;
-  const totalCells = columns * rows;
-
-  // PriorLabs pattern (1 represents missing cell)
-  const pattern = [
-    "11111 11111 1 11111 11111    1       1    11111 11111",
-    "1   1 1   1 1 1   1 1   1    1      111   1   1 1    ",
-    "11111 11111 1 1   1 11111    1     11 11  11111 11111",
-    "1     1 1   1 1   1 1 1      1    1111111 1   1     1",
-    "1     1  11 1 11111 1  11    1111 1     1 11111 11111"
-  ];
-
-  // Create squares
-  for (let i = 0; i < totalCells; i++) {
-    const square = document.createElement('div');
-    square.classList.add('square');
-    gridContainer.appendChild(square);
-  }
-
-  // Apply PriorLabs pattern
-  const squares = document.querySelectorAll('.square');
-  const patternStartRow = 10;
-  const patternStartCol = 3;
-  
-  pattern.forEach((row, rowIndex) => {
-    for (let colIndex = 0; colIndex < row.length; colIndex++) {
-      if (row[colIndex] === '1') {
-        const index = (rowIndex + patternStartRow) * columns + (colIndex + patternStartCol);
-        if (index < squares.length) {
-          squares[index].classList.add('missing');
-        }
-      }
-    }
-  });
-
-  // Animate squares
-  setInterval(() => {
-    squares.forEach(square => {
-      if (!square.classList.contains('missing')) {
-        if (Math.random() < 0.2) {
-          square.classList.add('active');
-        } else {
-          square.classList.remove('active');
-        }
-      }
-    });
-  }, 1000);
-});
-</script>
-
-PriorLabs is building breakthrough foundation models that understand spreadsheets and databases. While foundation models have transformed text and images, tabular data has remained largely untouched. We're tackling this opportunity with technology that could revolutionize how we approach scientific discovery, medical research, financial modeling, and business intelligence.
-
-<!---
-This page contains usage examples and installation instructions of TabPFN. Please find additional instructions on our Classifiers and Regressors on the respective subpages. An in-depth technical documentation of our software interfaces can be found in the [API Reference](api/tabpfn_classifier/)
-
-## User Interface
-
-<script type="module" src="https://gradio.s3-us-west-2.amazonaws.com/4.43.0/gradio.js"></script>
-
-<gradio-app src="https://noahho-tabpfn-client-gui.hf.space" theme_mode="light" eager="true" container="false">
-</gradio-app>
--->
-
-<!---
-## Expected Output
-Our models follow the interfaces provided by sklearn, so you can expect the same output as you would from sklearn models.
-TabPFNClassifier will return a numpy array of shape `(n_samples, n_classes)` with the probabilities of each class, while
-TabPFNRegressor will return a numpy array of shape `(n_samples,)` with the predicted values. For more detailed documentation
-please check the technical documentation of [scripts.estimator.TabPFNClassifier.predict_proba](https://priorlabs.github.io/api/tabpfn_classifier/#scripts.estimator.TabPFNClassifier.predict_proba).
-
-## Expected Runtime
-The runtime of the model is dependent on the number of estimators and the size of the dataset. For a dataset of 1000
-samples and 4 features, the runtime on GPU is typically less than 1 second. For a dataset of 10000 samples and 4 features, the
-runtime on GPU is typically less than 10 seconds.
--->
-
-## TabPFN Integrations
-
-<div class="grid cards" markdown>
+<div class="grid cards up" markdown>
 
 -   :material-cloud-check:{ .lg .middle } **API Client**
 
     ---
 
-    The fastest way to get started with TabPFN. Access our models through the cloud without requiring local GPU resources.
+    **Best models, No GPU needed.** The fastest way to get started with TabPFN. Access our models through the cloud without requiring local GPU resources.
 
     [:octicons-arrow-right-24: TabPFN Client](https://github.com/PriorLabs/tabpfn-client)
 
--   :material-application:{ .lg .middle } **User Interface**
+-   :material-language-python:{ .lg .middle } **Python Package**
 
     ---
 
-    Visual interface for no-code interaction with TabPFN. Perfect for quick experimentation and visualization.
+    **Most popular.** Local installation for research and privacy sensitive use cases with GPU support and scikit-learn compatible interface.
 
-    [:octicons-arrow-right-24: Access GUI](https://ux.priorlabs.ai/)
+    [:octicons-arrow-right-24: TabPFN Local](https://github.com/PriorLabs/tabpfn)
 
--   :material-language-python:{ .lg .middle } **Python Package**
+-   :material-application:{ .lg .middle } **User Interface**
 
     ---
 
-    Local installation for research and privacy sesitive use cases with GPU support and scikit-learn compatible interface.
+    **No Code.** Visual interface for no-code interaction with TabPFN. Perfect for quick experimentation and visualization.
 
-    [:octicons-arrow-right-24: TabPFN Local](https://github.com/PriorLabs/tabpfn)
+    [:octicons-arrow-right-24: Access GUI](https://ux.priorlabs.ai/)
 
 -   :material-language-r:{ .lg .middle } **R Integration**
 
@@ -183,10 +44,13 @@ runtime on GPU is typically less than 10 seconds.
 
 </div>
 
+&nbsp;</br>
+&nbsp;</br>
+&nbsp;</br>
 
 ## Why TabPFN
 
-<div class="grid cards" markdown>
+<div class="grid cards second" markdown>
 
 -   :material-speedometer:{ .lg .middle } **Rapid Training**
 
@@ -226,4 +90,4 @@ runtime on GPU is typically less than 10 seconds.
 
     The model handles various types of raw data, including missing values and categorical variables, with minimal preprocessing. This reduces the burden on users to perform extensive data preparation.
 
-</div>
+</div>