Update README.md

README.md

@@ -1,71 +1,126 @@
 ---
 license: cc0-1.0
-task_categories:
-- reinforcement-learning
-- tabular-classification
-language:
-- en
-tags:
-- chess
-- opening-theory
-- lichess
-- elite-games
-- gambitflow
-size_categories:
-- 1M<n<10M
-pretty_name: 'GambitFlow: Elite Chess Opening Theory'
 ---
 
-# ♟️ GambitFlow: Elite Opening Theory (2000+ Elo)
+# GambitFlow Opening Database
 
-## 📊 Dataset Overview
-This dataset serves as the **"Opening Memory"** for the GambitFlow AI project (Synapse-Edge). It contains millions of chess positions extracted exclusively from **Elite Level Games (Elo 2000+)** played on Lichess.
+A curated, high-quality chess opening theory database in SQLite format, designed for chess engines and analysis tools. This dataset contains aggregated move statistics derived from thousands of recent, high-rated (2600+ ELO) grandmaster-level games.
 
-The goal is to provide the AI with Grandmaster-level intuition during the opening phase, preventing early positional disadvantages.
+The goal of this database is to provide a powerful, statistically-backed foundation for understanding modern opening theory, without relying on traditional, human-annotated opening books.
 
-- **Source:** Lichess Standard Rated Games (Jan 2017 - Apr 2024)
-- **Filter:** White & Black Elo ≥ 2000
-- **Depth:** First 35 plies (moves)
-- **Format:** SQLite Database (`.db`)
+## How to Use
 
-## 📂 Dataset Structure
+The database is a single SQLite file (`opening_theory.db`). You can download it directly from the "Files" tab or use the `huggingface_hub` library to access it programmatically.
 
-The data is stored in a `positions` table with the following schema:
-
-| Column | Type | Description |
-| :--- | :--- | :--- |
-| `fen` | TEXT (PK) | The board position in Forsyth–Edwards Notation (Cleaned: no move counters). |
-| `move_stats` | JSON | Aggregated statistics of moves played in this position by elite players. |
-
-### Example `move_stats` JSON:
-```json
-{
-  "e4": 15400,
-  "d4": 12300,
-  "Nf3": 5000
-}
-```
-
-## 🛠️ Usage
+Here is a Python example to query the database for the starting position:
 
 ```python
 import sqlite3
 import json
+from huggingface_hub import hf_hub_download
 
-conn = sqlite3.connect("opening_theory_v1.db")
+# 1. Download the database file
+db_path = hf_hub_download(
+    repo_id="GambitFlow/Opening-Database",
+    filename="opening_theory.db",
+    repo_type="dataset"
+)
+
+# 2. Connect to the database
+conn = sqlite3.connect(db_path)
 cursor = conn.cursor()
 
-# Get stats for the starting position
-start_fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPP1PPP/RNBQKBNR w KQkq - -"
-cursor.execute("SELECT move_stats FROM opening_book WHERE fen=?", (start_fen,))
-stats = json.loads(cursor.fetchone()[0])
+# 3. Query a position (FEN)
+# The FEN should be "canonical" (position, turn, castling, en passant)
+start_fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq -"
+
+cursor.execute("SELECT move_data FROM openings WHERE fen = ?", (start_fen,))
+row = cursor.fetchone()
+
+if row:
+    # 4. Parse the JSON data
+    moves_data = json.loads(row)
+    
+    # Sort moves by frequency
+    sorted_moves = sorted(
+        moves_data.items(), 
+        key=lambda item: item['frequency'], 
+        reverse=True
+    )
+    
+    print(f"Top moves for FEN: {start_fen}\n")
+    for move, data in sorted_moves[:5]:
+        print(f"- Move: {move}")
+        print(f"  Frequency: {data['frequency']:,}")
+        print(f"  Avg Score: {data.get('avg_score', 0.0):.3f}\n")
+
+conn.close()
+```
+
+## Data Collection and Processing
+
+This dataset was meticulously created through a multi-stage pipeline to ensure the highest quality and statistical relevance.
 
-print("Most popular elite move:", max(stats, key=stats.get))
+**1. Data Source:**
+The foundation of this dataset is the **Lichess Elite Database**, a filtered collection of games from high-rated players, originally curated by [Nikonoel](https://database.nikonoel.fr/). We used multiple monthly PGN files from the 2023-2024 period to capture modern theory.
+
+**2. Filtering Criteria:**
+Only games meeting the following strict criteria were processed:
+*   **Minimum ELO:** Both players had a rating of **2600 or higher**.
+*   **Maximum Depth:** Only the first **25 moves** of each game were analyzed to focus on opening theory.
+
+**3. Processing Pipeline:**
+*   **Distributed Processing:** The PGN files were processed in parallel across multiple sessions to handle the large volume of games efficiently.
+*   **Perspective-Aware Scoring:** Each move's quality was scored from the perspective of the player whose turn it was. A win scored `1.0`, a loss `0.0`, and a draw `0.5`.
+*   **Aggregation:** For each unique board position (FEN), statistics for every move played were aggregated, including frequency and the sum of scores.
+*   **Merging:** The databases from the distributed sessions were merged into a single master file. This process recalculated weighted averages for ELO and move scores, ensuring statistical accuracy.
+
+## Dataset Structure
+
+The database contains a single table named `openings`.
+
+**File:** `opening_theory.db`
+**Table:** `openings`
+
+| Column        | Type    | Description                                                 |
+|---------------|---------|-------------------------------------------------------------|
+| `fen`         | TEXT    | The canonical board position (FEN string, Primary Key).     |
+| `move_data`   | TEXT    | A JSON object containing statistics for each move played.   |
+| `total_games` | INTEGER | The total number of times this position was seen.           |
+| `avg_elo`     | INTEGER | The average ELO of players who reached this position.       |
+
+### `move_data` JSON Structure
+
+The `move_data` column contains a JSON string with the following structure:
+
+```json
+{
+  "e4": {
+    "frequency": 153944,
+    "score_sum": 76972.0,
+    "avg_score": 0.5
+  },
+  "d4": {
+    "frequency": 65604,
+    "score_sum": 32802.0,
+    "avg_score": 0.5
+  }
+}
 ```
+*   **`frequency`**: The number of times this move was played from the given position.
+*   **`score_sum`**: The sum of all perspective-aware scores for this move.
+*   **`avg_score`**: The average score, calculated as `score_sum / frequency`.
+
+## Citation
 
-## ⚖️ Credits & License
-- **Raw Data:** [Lichess Open Database](https://database.lichess.org/)
-- **Processed By:** GambitFlow Team
-- **License:** CC0 1.0 Universal (Public Domain)
+If you use this dataset in your work, please consider citing it:
 
-*We gratefully acknowledge Lichess.org for providing open access to millions of chess games.*
+```bibtex
+@dataset{gambitflow_opening_database_2024,
+  author       = {GambitFlow Project},
+  title        = {GambitFlow Opening Database},
+  year         = {2024},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/GambitFlow/Opening-Database}
+}
+```