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Browse files- README.md +10 -13
- app.py +278 -179
- requirements.txt +9 -5
README.md
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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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# Analizador de Partidas PGN (Doctor Linux)
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- Repara PGN y analiza la primera partida válida (no se altera tu texto original).
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- Si existe `models/blunder/model.joblib` + `features.txt`, muestra curva de **prob. de blunder**.
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- Gráfico de Ventaja aprox. y reporte básico.
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## Local
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```bash
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pip install -r requirements.txt
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python app.py
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app.py
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import
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import
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import chess
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import
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import
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import
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if
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""
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return
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def
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import os
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import gradio as gr
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import chess
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import chess.pgn
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import re
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import joblib
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import numpy as np
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import pandas as pd
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from io import StringIO
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# ====== Intento cargar modelo de blunders si existe ======
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BLUNDER_MODEL_PATH = "models/blunder/model.joblib"
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BLUNDER_FEATURES_PATH = "models/blunder/features.txt"
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blunder_model = None
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blunder_features = None
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if os.path.exists(BLUNDER_MODEL_PATH) and os.path.exists(BLUNDER_FEATURES_PATH):
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try:
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blunder_model = joblib.load(BLUNDER_MODEL_PATH)
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with open(BLUNDER_FEATURES_PATH, "r", encoding="utf-8") as f:
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blunder_features = [ln.strip() for ln in f if ln.strip()]
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print("✅ Modelo de blunders cargado.")
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except Exception as e:
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print("⚠️ No se pudo cargar el modelo de blunders:", e)
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blunder_model = None
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blunder_features = None
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# -------------------------------
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# Reparador de PGN
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# -------------------------------
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class ReparadorPGN:
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@staticmethod
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def reparar_pgn(pgn_text: str) -> str:
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if not isinstance(pgn_text, str):
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return pgn_text
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lineas = pgn_text.splitlines()
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out = []
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for linea in lineas:
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original = linea
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s = linea.strip()
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if s.startswith("[") and "]" in s:
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s = re.sub(r'\[([A-Za-z0-9_]+)\s+"([^"]*)["“”]?\]?$', r'[\1 "\2"]', s)
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s = re.sub(r'\[Ulnite', '[White', s)
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s = s.replace('[Result "I-0"]', '[Result "1-0"]')
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s = s.replace('[Result "O-I"]', '[Result "0-1"]')
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s = s.replace('[Result "I/2-I/2"]', '[Result "1/2-1/2"]')
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out.append(s); continue
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t = s
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correcciones = {
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r'\bnf([1-8a-h])': r'Nf\1',
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r'\bnc([1-8a-h])': r'Nc\1',
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r'\bng([1-8a-h])': r'Ng\1',
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r'\bhc([1-8])\b': 'Nc\\1',
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r'\bhf([1-8])\b': 'Nf\\1',
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r'\bnn1\b': 'Nf1',
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r'\bhe2\b': 'Ne2',
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r'\bnh7\b': 'Nh7',
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r'\bhc5\b': 'Nc5',
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r'\bqu2\b': 'Qd2',
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r'\bre1\b': 'Re1',
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r'\brn\b': 'Rf1',
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r'\bbe4:?': 'Be4',
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r'\bo-o-o\b': 'O-O-O',
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r'\bo-o\b': 'O-O',
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}
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for pat, rep in correcciones.items():
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t = re.sub(pat, rep, t, flags=re.IGNORECASE)
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out.append(t if t else original)
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texto = "\n".join(out)
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texto = texto.replace('Result "* *"', 'Result "*"')
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return texto
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# -------------------------------
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# Analizador simple
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# -------------------------------
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class AnalizadorAjedrez:
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def __init__(self):
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self.datos_jugadas = [] # lista de dicts por ply
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self._last_meta = {} # meta de partida analizada
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def analizar_primera_partida_valida(self, pgn_text: str):
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if not pgn_text or not pgn_text.strip():
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return "Anónimo", "Anónimo", "*", "PGN vacío.", None
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candidatos = []
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candidatos.append(("original", pgn_text))
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reparado = ReparadorPGN.reparar_pgn(pgn_text)
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candidatos.append(("reparado", reparado))
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candidatos.append(("minimo", self._crear_pgn_minimo(pgn_text)))
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for etiqueta, intento in candidatos:
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try:
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partidas = list(self._iterar_partidas(intento))
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for game in partidas:
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if game is None: continue
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plies = sum(1 for _ in game.mainline_moves())
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if plies == 0: continue
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blancas = game.headers.get("White", "Anónimo") or "Anónimo"
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negras = game.headers.get("Black", "Anónimo") or "Anónimo"
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resultado = game.headers.get("Result", "*") or "*"
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self._analizar_jugadas(game, blancas, negras)
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info = f"Análisis exitoso ({etiqueta}). Plies: {plies}"
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return blancas, negras, resultado, info, intento
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except Exception:
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continue
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return "Anónimo", "Anónimo", "*", "No se pudo analizar ninguna partida válida en el PGN.", None
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def _iterar_partidas(self, pgn_text: str):
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f = StringIO(pgn_text)
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while True:
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game = chess.pgn.read_game(f)
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if game is None: break
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yield game
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def _analizar_jugadas(self, game, white, black):
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board = game.board()
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self.datos_jugadas = []
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self._last_meta = {"white": white, "black": black}
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jugada_num = 0
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for mv in game.mainline_moves():
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jugada_num += 1
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board.push(mv)
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self.datos_jugadas.append(self._evaluar_posicion(board, jugada_num, white, black))
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def _crear_pgn_minimo(self, pgn_text: str) -> str:
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pares = re.findall(r'\b(\d+)\.\s*([^\s]+)\s+([^\s]+)', pgn_text)
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movimientos = []
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if pares:
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for _, m1, m2 in pares[:50]:
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movimientos.append((m1, m2))
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else:
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san = re.findall(r'\b([NBRQK]?[a-h]?[1-8]?x?[a-h][1-8](?:=[NBRQK])?[+#]?)\b', pgn_text)
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if san:
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it = iter(san[:100]); tmp = []
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for m in it:
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n1 = m; n2 = next(it, None)
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if n2 is None: tmp.append((n1, "")); break
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tmp.append((n1, n2))
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movimientos = tmp
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header = (
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'[Event "Partida Reparada"]\n'
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'[White "Blancas"]\n'
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'[Black "Negras"]\n'
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'[Result "*"]\n\n'
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)
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cuerpo = []
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for i, par in enumerate(movimientos, 1):
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b, n = par
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cuerpo.append(f"{i}. {b} {n}".strip())
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return header + " ".join(cuerpo).strip() + ("\n" if cuerpo else "")
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def _evaluar_posicion(self, board: chess.Board, jugada_num: int, white: str, black: str):
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valores = {'p': 1, 'n': 3, 'b': 3, 'r': 5, 'q': 9, 'k': 0}
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pieces = board.piece_map().values()
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mat_w = sum(valores.get(p.symbol().lower(), 0) for p in pieces if p.color == chess.WHITE)
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mat_b = sum(valores.get(p.symbol().lower(), 0) for p in pieces if p.color == chess.BLACK)
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ventaja = mat_w - mat_b
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movilidad = board.legal_moves.count()
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return {
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"jugada": jugada_num,
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"evaluacion": ventaja + movilidad * 0.1,
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"material_w": mat_w,
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"material_b": mat_b,
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"material_rel": ventaja,
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"mobility": movilidad,
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"stm": 1 if board.turn == chess.WHITE else 0,
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"phase": self._fase_por_material(mat_w + mat_b),
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"white": white,
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"black": black,
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"elo_white": -1,
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"elo_black": -1,
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"elo_gap": 0,
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# campos binarios aproximados (sin SAN aquí)
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"is_capture": 0,
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"is_check_move": 0,
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"is_castling": 0,
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"is_promotion": 0,
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"in_check": int(board.is_check()),
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"ply": jugada_num - 1,
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}
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@staticmethod
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def _fase_por_material(total):
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if total > 5000: return 0
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if total > 2000: return 1
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return 2
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+
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def _df_features_for_model(self):
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if not self.datos_jugadas:
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return None
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return pd.DataFrame(self.datos_jugadas)
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| 192 |
+
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| 193 |
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def generar_grafico(self, blancas: str, negras: str):
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if not self.datos_jugadas:
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fig, ax = plt.subplots(figsize=(10, 6))
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| 196 |
+
ax.text(0.5, 0.5, "PGN no analizable", ha="center", va="center", fontsize=14, transform=ax.transAxes,
|
| 197 |
+
bbox=dict(boxstyle="round,pad=0.3", facecolor="lightcoral", alpha=0.7))
|
| 198 |
+
ax.set_axis_off()
|
| 199 |
+
return fig
|
| 200 |
+
|
| 201 |
+
jugadas = [d["jugada"] for d in self.datos_jugadas]
|
| 202 |
+
evals = [d["evaluacion"] for d in self.datos_jugadas]
|
| 203 |
+
material = [d["material_rel"] for d in self.datos_jugadas]
|
| 204 |
+
|
| 205 |
+
fig, ax = plt.subplots(figsize=(12, 6))
|
| 206 |
+
ax.plot(jugadas, evals, linewidth=2, label="Ventaja aprox.")
|
| 207 |
+
ax.fill_between(jugadas, evals, alpha=0.20)
|
| 208 |
+
ax.axhline(0, linestyle="--")
|
| 209 |
+
ax.set_title(f"Análisis: {blancas} vs {negras}")
|
| 210 |
+
ax.set_xlabel("Jugada"); ax.set_ylabel("Valor")
|
| 211 |
+
ax.grid(True, alpha=0.3)
|
| 212 |
+
|
| 213 |
+
# Si hay modelo, dibujamos curva de probabilidad de blunder
|
| 214 |
+
if blunder_model is not None and blunder_features is not None:
|
| 215 |
+
df = self._df_features_for_model()
|
| 216 |
+
missing = [c for c in blunder_features if c not in df.columns]
|
| 217 |
+
for m in missing:
|
| 218 |
+
df[m] = 0
|
| 219 |
+
X = df[blunder_features].astype(float).values
|
| 220 |
+
try:
|
| 221 |
+
prob = blunder_model.predict_proba(X)[:, 1]
|
| 222 |
+
ax.plot(jugadas, prob, linewidth=2, label="Prob. blunder (modelo)")
|
| 223 |
+
except Exception as e:
|
| 224 |
+
print("⚠️ No se pudo inferir prob. de blunder:", e)
|
| 225 |
+
|
| 226 |
+
ax.legend(loc="best")
|
| 227 |
+
fig.tight_layout()
|
| 228 |
+
return fig
|
| 229 |
+
|
| 230 |
+
def generar_reporte(self, blancas: str, negras: str, resultado: str, info_extra: str):
|
| 231 |
+
if not self.datos_jugadas:
|
| 232 |
+
return "## PGN no analizable\nCarga otro PGN o revisa el formato."
|
| 233 |
+
jugadas = [d["jugada"] for d in self.datos_jugadas]
|
| 234 |
+
evals = [d["evaluacion"] for d in self.datos_jugadas]
|
| 235 |
+
material = [d["material_rel"] for d in self.datos_jugadas]
|
| 236 |
+
tiene_modelo = (blunder_model is not None and blunder_features is not None)
|
| 237 |
+
nota = "Modelo de blunders activo." if tiene_modelo else "Modelo de blunders no disponible."
|
| 238 |
+
return f"""
|
| 239 |
+
## Reporte de partida
|
| 240 |
+
**Blancas:** {blancas}
|
| 241 |
+
**Negras:** {negras}
|
| 242 |
+
**Resultado:** {resultado}
|
| 243 |
+
|
| 244 |
+
**Plies analizados:** {len(jugadas)}
|
| 245 |
+
**Ventaja final aprox.:** {evals[-1]:.2f}
|
| 246 |
+
**Diferencia de material final (W-B):** {material[-1]}
|
| 247 |
+
|
| 248 |
+
{info_extra or ""}
|
| 249 |
+
|
| 250 |
+
_{nota}_
|
| 251 |
+
"""
|
| 252 |
+
|
| 253 |
+
# ====== UI ======
|
| 254 |
+
analizador = AnalizadorAjedrez()
|
| 255 |
+
|
| 256 |
+
def ui_analizar(pgn_text: str):
|
| 257 |
+
blancas, negras, resultado, info, _ = analizador.analizar_primera_partida_valida(pgn_text)
|
| 258 |
+
fig = analizador.generar_grafico(blancas, negras)
|
| 259 |
+
reporte = analizador.generar_reporte(blancas, negras, resultado, info)
|
| 260 |
+
pgn_reparado = ReparadorPGN.reparar_pgn(pgn_text)
|
| 261 |
+
return fig, reporte, pgn_reparado
|
| 262 |
+
|
| 263 |
+
with gr.Blocks(title="Analizador PGN (ML-ready) — Doctor Linux") as demo:
|
| 264 |
+
gr.Markdown("# Analizador de Partidas PGN\nCarga un PGN (el input no se modifica).")
|
| 265 |
+
with gr.Row():
|
| 266 |
+
pgn_in = gr.Textbox(label="PGN original", lines=18, placeholder="Pega aquí un PGN. Puede contener múltiples partidas.")
|
| 267 |
+
with gr.Row():
|
| 268 |
+
btn = gr.Button("Analizar")
|
| 269 |
+
with gr.Row():
|
| 270 |
+
graf = gr.Plot(label="Gráfico")
|
| 271 |
+
with gr.Row():
|
| 272 |
+
rep = gr.Markdown(label="Reporte")
|
| 273 |
+
with gr.Row():
|
| 274 |
+
reparado_out = gr.Textbox(label="PGN reparado (para revisión; tu texto original no se toca)", lines=12)
|
| 275 |
+
btn.click(fn=ui_analizar, inputs=[pgn_in], outputs=[graf, rep, reparado_out])
|
| 276 |
+
|
| 277 |
+
if __name__ == "__main__":
|
| 278 |
+
demo.launch()
|
requirements.txt
CHANGED
|
@@ -1,5 +1,9 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
gradio>=4.44.0
|
| 2 |
+
python-chess>=1.999
|
| 3 |
+
matplotlib>=3.8
|
| 4 |
+
numpy>=1.26
|
| 5 |
+
pandas>=2.2
|
| 6 |
+
scikit-learn>=1.3
|
| 7 |
+
tqdm>=4.66
|
| 8 |
+
joblib>=1.4
|
| 9 |
+
pyarrow>=16.0
|