20226-05-22 minor update

.gitignore

@@ -3,4 +3,5 @@ models/*
 images/*
 notebooks/*_executed.ipynb
 src/climbingboardgpt/__pycache__
-outputs/
+outputs/
+webapp/__pycache__

README.md

@@ -160,6 +160,10 @@ ClimbingBoardGPT/
 │   ├── joint_transformer_grade_predictor.pth
 │   └── joint_route_gpt_generator.pth
 ├── notebooks/
+│   ├── 01_unified_route_tokenization.ipynb
+│   ├── 02_joint_transformer_grade_prediction.ipynb
+│   ├── 03_joint_route_generator.ipynb
+│   └── 04_generated_route_evaluation.ipynb
 ├── scripts/
 │   ├── 01_tokenize_routes.py
 │   ├── 02_train_grade_predictor.py
@@ -172,6 +176,14 @@ ClimbingBoardGPT/
 │   ├── demo_predict_tb2.py
 │   └── demo_predict_kilter.py
 ├── src/climbingboardgpt/
+├── webapp/
+│   ├── app.py
+│   ├── app.css
+│   ├── app.js
+│   ├── index.html
+│   └── Dockerfile
+├── docker-compose.webapp.yml
+├── LICENSE
 ├── README.md
 ├── requirements.txt
 └── pyproject.toml

notebooks/01_unified_route_tokenization.ipynb

@@ -9,7 +9,7 @@
     "\n",
     "## What is tokenization and why does it matter?\n",
     "\n",
-    "In natural language processing, **tokenization** is the process of converting raw text into a sequence of discrete symbols (tokens) that a model can process. For example, the sentence \"I love climbing\" might be tokenized as `[\"I\", \" love\", \" climbing\"]` using a subword tokenizer like BPE.\n",
+    "In natural language processing, **tokenization** is the process of converting raw text into a sequence of discrete symbols (tokens) that a model can process. For example, the sentence \"I climb rocks\" might be tokenized as `[\"I\", \" climb\", \" rocks\"]` using a subword tokenizer like BPE.\n",
     "\n",
     "For climbing board routes, we face an analogous problem: how do we convert a climb — which is fundamentally a *set of holds at specific positions with specific roles* — into a sequence of tokens that a transformer can learn from?\n",
     "\n",

pyproject.toml

@@ -7,7 +7,7 @@ name = "climbingboardgpt"
 version = "0.2.1"
 description = "Unified TB2/Kilter transformer route modeling, grade prediction, and GPT-style route generation."
 readme = "README.md"
-requires-python = ""
+requires-python = ">=3.12"
 dependencies = [
     "numpy",
     "pandas",

webapp/Dockerfile

@@ -21,4 +21,4 @@ RUN pip install --no-cache-dir --upgrade pip \
 
 EXPOSE 8055
 
-CMD ["uvicorn", "webapp.app:app", "--host", "0.0.0.0", "--port", "8055"]
+CMD ["uvicorn", "webapp.app:app", "--host", "0.0.0.0", "--port", "8055"]

webapp/static/index.html

@@ -109,10 +109,12 @@
       <div class="card note">
         <h2>Links</h2>
         <ul class="link-list">
+          <li><a href="https://pawelsarkowicz.xyz" target="_blank" rel="noreferrer">pawelsarkowicz.xyz</a></li>
           <li><a href="https://github.com/psark007/ClimbingBoardGPT" target="_blank" rel="noreferrer">ClimbingBoardGPT repo</a></li>
           <li><a href="https://github.com/psark007/Tension-Board-2-Analysis" target="_blank" rel="noreferrer">Tension Board 2 Analysis repo</a></li>
           <li><a href="https://github.com/psark007/Kilter-Board-Analysis" target="_blank" rel="noreferrer">Kilter Board Analysis repo</a></li>
-          <li><a href="https://pawelsarkowicz.xyz" target="_blank" rel="noreferrer">pawelsarkowicz.xyz</a></li>
+          <li><a href="https://tensionclimbing.com/products/tension-board-2" target="_blank" rel="noreferrer">Tension Board 2</a></li>
+          <li><a href="https://settercloset.com" target="_blank" rel="noreferrer">Kilter Board</a></li>
         </ul>
       </div>