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| import spaces # Debe permanecer en la línea 1 para ZeroGPU | |
| import os | |
| import re | |
| import logging | |
| import gradio as ui | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
| from functools import lru_cache | |
| from typing import Optional | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Logging | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format="%(asctime)s [%(levelname)s] %(message)s", | |
| ) | |
| log = logging.getLogger(__name__) | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Constantes de configuración | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| MODELO = "DarksitoBest/DRK-Coder-V1.1" | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| MAX_HISTORIAL = 20 | |
| GPU_TIMEOUT_BUFFER = 10 | |
| FALLBACK_CONTEXT = 32_768 | |
| MAX_CONTEXT_SANE = 1_000_000 | |
| DEFAULT_REPETITION_PENALTY = 1.08 | |
| if not HF_TOKEN: | |
| raise RuntimeError( | |
| "No se encontró HF_TOKEN. Añádelo en Settings → Variables and secrets " | |
| "del Space y márcalo como Secret." | |
| ) | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Carga del modelo | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| log.info("Cargando tokenizer de %s …", MODELO) | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| MODELO, | |
| token=HF_TOKEN, | |
| trust_remote_code=True, | |
| ) | |
| log.info("Cargando modelo …") | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODELO, | |
| token=HF_TOKEN, | |
| trust_remote_code=True, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| low_cpu_mem_usage=True, | |
| ) | |
| model.eval() | |
| pipe = pipeline( | |
| "text-generation", | |
| model=model, | |
| tokenizer=tokenizer, | |
| ) | |
| def _calc_context_limit() -> int: | |
| for attr in ("max_position_embeddings",): | |
| val = getattr(model.config, attr, None) | |
| if val and val <= MAX_CONTEXT_SANE: | |
| return int(val) | |
| val = getattr(tokenizer, "model_max_length", None) | |
| if val and val <= MAX_CONTEXT_SANE: | |
| return int(val) | |
| return FALLBACK_CONTEXT | |
| CONTEXT_LIMIT: int = _calc_context_limit() | |
| log.info("Límite de contexto detectado: %d tokens", CONTEXT_LIMIT) | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Prompts del sistema | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| SYSTEM_PROMPTS: dict[str, str] = { | |
| "en": ( | |
| "You are DRK Code V1, a senior programming assistant.\n" | |
| "Always answer in English. Provide correct, secure, and executable solutions.\n" | |
| "When writing code, use Markdown code blocks with the language specified. Be clear and direct.\n" | |
| "When useful, add a brief reasoning summary inside <think>...</think> before the final answer. " | |
| "Do not expose hidden chain-of-thought.\n" | |
| "Put the main deliverable in one complete fenced code block so it can be opened in Canvas.\n" | |
| "If important information is missing, ask before making assumptions. Never invent APIs or results." | |
| ), | |
| "es": ( | |
| "Eres DRK Code V1, un asistente senior de programación.\n" | |
| "Responde siempre en español. Entrega soluciones correctas, seguras y ejecutables.\n" | |
| "Cuando escribas código, usa bloques Markdown con el lenguaje indicado. Sé claro y directo.\n" | |
| "Cuando sea útil, añade un resumen breve del razonamiento dentro de <think>...</think> antes " | |
| "de la respuesta final. No expongas razonamiento interno oculto.\n" | |
| "Coloca el entregable principal en un único bloque de código completo para abrirlo en Canvas.\n" | |
| "Si faltan datos importantes, pregunta antes de asumir. No inventes APIs ni resultados." | |
| ), | |
| } | |
| LANGUAGE_ALIASES: dict[str, Optional[str]] = { | |
| "py": "python", "python3": "python", "js": "javascript", "jsx": "javascript", | |
| "ts": "typescript", "tsx": "typescript", "htm": "html", "yml": "yaml", | |
| "bash": "shell", "sh": "shell", "zsh": "shell", "c++": "cpp", | |
| "md": "markdown", "text": None, "txt": None, | |
| } | |
| SUPPORTED_CANVAS_LANGUAGES: frozenset[str] = frozenset({ | |
| "python", "c", "cpp", "markdown", "latex", "json", "html", "css", | |
| "javascript", "jinja2", "typescript", "yaml", "dockerfile", "shell", "r", "sql", | |
| }) | |
| _RE_CODE_BLOCK = re.compile(r"```([^\n`]*)\n(.*?)```", re.DOTALL) | |
| _RE_USER_TURN = re.compile(r"\n(?:User|Usuario):\s*", re.IGNORECASE) | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Helpers | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| def texto_del_contenido(content) -> str: | |
| if isinstance(content, str): return content | |
| if isinstance(content, list): | |
| partes = [ | |
| str(b.get("text", "")) if isinstance(b, dict) and b.get("type") == "text" | |
| else str(b) | |
| for b in content if isinstance(b, (str, dict)) | |
| ] | |
| return "\n".join(partes) | |
| return str(content or "") | |
| def construir_prompt(mensaje: str, historial: list, idioma: str) -> str: | |
| system_prompt = SYSTEM_PROMPTS.get(idioma, SYSTEM_PROMPTS["en"]) | |
| mensajes = [{"role": "system", "content": system_prompt}] | |
| # Soporte robusto de historial (maneja formato de diccionarios y tuplas) | |
| for turno in historial[-MAX_HISTORIAL:]: | |
| if isinstance(turno, dict): | |
| rol = turno.get("role") | |
| if rol in {"user", "assistant"}: | |
| mensajes.append({"role": rol, "content": texto_del_contenido(turno.get("content", ""))}) | |
| elif isinstance(turno, (list, tuple)) and len(turno) == 2: | |
| user_msg, bot_msg = turno | |
| if user_msg: | |
| mensajes.append({"role": "user", "content": texto_del_contenido(user_msg)}) | |
| if bot_msg: | |
| mensajes.append({"role": "assistant", "content": texto_del_contenido(bot_msg)}) | |
| mensajes.append({"role": "user", "content": mensaje}) | |
| if getattr(tokenizer, "chat_template", None): | |
| return tokenizer.apply_chat_template(mensajes, tokenize=False, add_generation_prompt=True) | |
| lineas = [f"System: {system_prompt}"] | |
| for msg in mensajes[1:]: | |
| nombre = "User" if msg["role"] == "user" else "Assistant" | |
| lineas.append(f"{nombre}: {msg['content']}") | |
| lineas.append("Assistant:") | |
| return "\n\n".join(lineas) | |
| def extraer_canvas(respuesta: str) -> tuple[Optional[str], Optional[str]]: | |
| bloques = _RE_CODE_BLOCK.findall(respuesta) | |
| if not bloques: return None, None | |
| lenguaje_raw, codigo = max(bloques, key=lambda b: len(b[1].strip())) | |
| codigo = codigo.strip() | |
| if not codigo: return None, None | |
| lenguaje = lenguaje_raw.strip().lower().split()[0] if lenguaje_raw.strip() else None | |
| lenguaje = LANGUAGE_ALIASES.get(lenguaje, lenguaje) | |
| if lenguaje not in SUPPORTED_CANVAS_LANGUAGES: lenguaje = None | |
| return codigo, lenguaje | |
| def _calcular_tokens_salida(prompt: str, max_tokens: int, longitud_automatica: bool) -> int: | |
| if not longitud_automatica: return int(max_tokens) | |
| tokens_entrada = len(tokenizer(prompt, add_special_tokens=False, truncation=False).input_ids) | |
| return max(1, CONTEXT_LIMIT - tokens_entrada - 16) | |
| def _construir_kwargs_generacion(tokens_salida: int, temperatura: float, top_p: float, longitud_automatica: bool) -> dict: | |
| kwargs: dict = { | |
| "max_new_tokens": tokens_salida, "return_full_text": False, | |
| "repetition_penalty": DEFAULT_REPETITION_PENALTY, "pad_token_id": tokenizer.eos_token_id, | |
| } | |
| if longitud_automatica: kwargs["max_time"] = 120 - GPU_TIMEOUT_BUFFER | |
| if float(temperatura) > 0: | |
| kwargs.update(do_sample=True, temperature=float(temperatura), top_p=float(top_p)) | |
| else: kwargs["do_sample"] = False | |
| return kwargs | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Chatbot Principal | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| def chat_bot(mensaje_usuario: str, historial: list, idioma: str, temperatura: float, top_p: float, max_tokens: int, longitud_automatica: bool): | |
| mensaje_usuario = (mensaje_usuario or "").strip() | |
| if not mensaje_usuario: | |
| msg = "Type a question or paste a code snippet to get started." if idioma == "en" else "Escribe una pregunta o pega un fragmento de código para comenzar." | |
| return msg, ui.skip() | |
| try: | |
| prompt = construir_prompt(mensaje_usuario, historial, idioma) | |
| tokens_salida = _calcular_tokens_salida(prompt, max_tokens, longitud_automatica) | |
| kwargs = _construir_kwargs_generacion(tokens_salida, temperatura, top_p, longitud_automatica) | |
| log.info("Generando respuesta | idioma=%s temp=%.2f tokens=%d", idioma, temperatura, tokens_salida) | |
| resultado = pipe(prompt, **kwargs) | |
| respuesta = resultado[0]["generated_text"].strip() | |
| respuesta = _RE_USER_TURN.split(respuesta, maxsplit=1)[0].strip() | |
| if respuesta: | |
| codigo, lenguaje = extraer_canvas(respuesta) | |
| canvas_update = ui.update(value=codigo, language=lenguaje) if codigo else ui.skip() | |
| return respuesta, canvas_update | |
| except torch.cuda.OutOfMemoryError: | |
| log.exception("OOM durante la generación") | |
| msg = "⚠️ Out of GPU memory. Try reducing token length." if idioma == "en" else "⚠️ Memoria de GPU insuficiente. Reduce la longitud." | |
| return msg, ui.skip() | |
| except Exception: | |
| log.exception("Error inesperado en chat_bot") | |
| msg = "⚠️ I couldn't generate a response. Please try again." if idioma == "en" else "⚠️ No pude generar una respuesta. Inténtalo de nuevo." | |
| return msg, ui.skip() | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Textos UI | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| UI_TEXT: dict[str, dict[str, str]] = { | |
| "en": { | |
| "subtitle": "AI coding workspace · Design, debug, and build faster", "online": "Model connected", | |
| "settings": "Configuration", "settings_sub": "Adjust the generation style", | |
| "creativity": "Creativity", "creativity_info": "0 = precise · 1.5 = creative", "top_p_info": "Token diversity", | |
| "length": "Maximum length", "length_info": "Response tokens", "auto_length": "Auto length", | |
| "auto_length_info": "Stops at EOS, context limit, or the 120s GPU timeout", "tip_title": "Pro Tip", | |
| "tip": "Include the language, goal, constraints, and full error message for better code.", | |
| "welcome": "What are we building?", "welcome_sub": "Ask a question, paste code, or describe a bug.", | |
| "placeholder": "Describe your task or paste code…", "send": "Send", "stop": "Stop", | |
| "canvas": "Canvas", "canvas_sub": "The main generated code appears here automatically", | |
| }, | |
| "es": { | |
| "subtitle": "Espacio de programación con IA · Diseña, depura y construye más rápido", "online": "Modelo conectado", | |
| "settings": "Configuración", "settings_sub": "Ajusta el estilo de generación", | |
| "creativity": "Creatividad", "creativity_info": "0 = preciso · 1.5 = creativo", "top_p_info": "Diversidad de tokens", | |
| "length": "Longitud máxima", "length_info": "Tokens de respuesta", "auto_length": "Longitud automática", | |
| "auto_length_info": "Se detiene por EOS, límite de contexto o timeout de 120s", "tip_title": "Consejo Pro", | |
| "tip": "Incluye lenguaje, objetivo, restricciones y el error completo para obtener mejor código.", | |
| "welcome": "¿Qué vamos a construir?", "welcome_sub": "Pregunta, pega código o describe un bug.", | |
| "placeholder": "Describe tu tarea o pega código…", "send": "Enviar", "stop": "Detener", | |
| "canvas": "Canvas", "canvas_sub": "El código principal generado aparece aquí automáticamente", | |
| }, | |
| } | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # HTML Generators | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| def hero_html(lang: str = "en") -> str: | |
| t = UI_TEXT.get(lang, UI_TEXT["en"]) | |
| return f""" | |
| <div id="top-bar"> | |
| <div class="brand"> | |
| <div class="logo">DRK</div> | |
| <div class="brand-text"> | |
| <h1>DRK Code V1</h1> | |
| <p>{t['subtitle']}</p> | |
| </div> | |
| </div> | |
| <div class="online"><span class="dot"></span> {t['online']}</div> | |
| </div> | |
| """ | |
| def panel_html(lang: str = "en") -> str: | |
| t = UI_TEXT.get(lang, UI_TEXT["en"]) | |
| return f'<div class="section-header">{t["settings"]}</div><div class="section-sub">{t["settings_sub"]}</div>' | |
| def tip_html(lang: str = "en") -> str: | |
| t = UI_TEXT.get(lang, UI_TEXT["en"]) | |
| return f'<div class="quick-tip"><b>{t["tip_title"]}</b>{t["tip"]}</div>' | |
| def canvas_html(lang: str = "en") -> str: | |
| t = UI_TEXT.get(lang, UI_TEXT["en"]) | |
| return f'<div class="canvas-header"><div class="canvas-title"><span>✦</span> {t["canvas"]}</div><div class="section-sub">{t["canvas_sub"]}</div></div>' | |
| def placeholder_md(lang: str = "en") -> str: | |
| """Devuelve Markdown para que Gradio lo renderice correctamente en el placeholder.""" | |
| t = UI_TEXT.get(lang, UI_TEXT["en"]) | |
| return ( | |
| "### ⌁\n\n" | |
| f"**{t['welcome']}**\n\n" | |
| f"{t['welcome_sub']}" | |
| ) | |
| def cambiar_idioma(lang: str): | |
| t = UI_TEXT.get(lang, UI_TEXT["en"]) | |
| return ( | |
| hero_html(lang), panel_html(lang), | |
| ui.update(label=t["creativity"], info=t["creativity_info"]), | |
| ui.update(info=t["top_p_info"]), | |
| ui.update(label=t["length"], info=t["length_info"]), | |
| ui.update(label=t["auto_length"], info=t["auto_length_info"]), | |
| tip_html(lang), canvas_html(lang), | |
| ui.update(placeholder=placeholder_md(lang)), | |
| ui.update(placeholder=t["placeholder"], submit_btn=t["send"], stop_btn=t["stop"]), | |
| ) | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # CSS & Theme | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| CSS = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap'); | |
| :root { | |
| --bg-base: #09090b; | |
| --bg-panel: #0f0f12; | |
| --bg-input: #18181b; | |
| --border-subtle: #27272a; | |
| --border-default: #3f3f46; | |
| --text-primary: #fafafa; | |
| --text-secondary: #a1a1aa; | |
| --text-tertiary: #71717a; | |
| --accent: #6366f1; | |
| --accent-hover: #4f46e5; | |
| --accent-soft: rgba(99, 102, 241, 0.1); | |
| } | |
| html, body, .gradio-container { | |
| background-color: var(--bg-base) !important; | |
| font-family: 'Inter', system-ui, -apple-system, sans-serif !important; | |
| color: var(--text-primary) !important; | |
| } | |
| .gradio-container { | |
| max-width: 1600px !important; | |
| padding: 0 24px 24px !important; | |
| } | |
| /* Dot Matrix Background */ | |
| .gradio-container::before { | |
| content: ""; | |
| position: fixed; | |
| inset: 0; | |
| pointer-events: none; | |
| background-image: radial-gradient(rgba(255, 255, 255, 0.025) 1px, transparent 1px); | |
| background-size: 24px 24px; | |
| z-index: 0; | |
| } | |
| .gradio-container > * { position: relative; z-index: 1; } | |
| footer { display: none !important; } | |
| /* Top Bar */ | |
| #top-bar { | |
| display: flex; align-items: center; justify-content: space-between; | |
| padding: 20px 0; border-bottom: 1px solid var(--border-subtle); | |
| margin-bottom: 24px; | |
| } | |
| .brand { display: flex; align-items: center; gap: 16px; } | |
| .logo { | |
| width: 40px; height: 40px; border-radius: 8px; | |
| display: grid; place-items: center; | |
| font: 700 15px/1 'Inter', sans-serif; color: white; | |
| background: var(--bg-input); border: 1px solid var(--border-default); | |
| box-shadow: 0 1px 2px rgba(0,0,0,0.3); | |
| } | |
| .brand-text h1 { margin: 0; font-size: 18px; font-weight: 600; letter-spacing: -0.01em; color: var(--text-primary); } | |
| .brand-text p { margin: 2px 0 0; font-size: 13px; color: var(--text-tertiary); } | |
| .online { | |
| display: flex; align-items: center; gap: 8px; | |
| padding: 6px 12px; border-radius: 6px; | |
| background: var(--bg-panel); border: 1px solid var(--border-subtle); | |
| color: var(--text-secondary); font-size: 12px; font-weight: 500; | |
| } | |
| .dot { width: 8px; height: 8px; border-radius: 50%; background: #10b981; box-shadow: 0 0 8px rgba(16, 185, 129, 0.6); } | |
| /* Section Headers */ | |
| .section-header { font-size: 11px; font-weight: 600; text-transform: uppercase; letter-spacing: 0.08em; color: var(--text-secondary); margin-bottom: 4px; } | |
| .section-sub { font-size: 13px; color: var(--text-tertiary); margin-bottom: 20px; } | |
| /* Panels */ | |
| #settings-panel, #main-chat, #canvas-panel { | |
| background: var(--bg-panel) !important; | |
| border: 1px solid var(--border-subtle) !important; | |
| border-radius: 8px !important; | |
| box-shadow: 0 1px 3px rgba(0,0,0,0.2) !important; | |
| } | |
| #settings-panel { padding: 24px !important; } | |
| #canvas-panel { padding: 20px !important; display: flex; flex-direction: column; } | |
| .canvas-header { display: flex; flex-direction: column; margin-bottom: 12px; } | |
| .canvas-title { font-size: 11px; font-weight: 600; text-transform: uppercase; letter-spacing: 0.08em; color: var(--text-secondary); display: flex; align-items: center; gap: 6px; } | |
| /* Chat styles */ | |
| #main-chat { padding: 8px !important; overflow: hidden; } | |
| #chatbot { background: transparent !important; border: 0 !important; height: 100% !important; } | |
| #chatbot .message { | |
| border-radius: 8px !important; | |
| border: 1px solid var(--border-subtle) !important; | |
| padding: 16px !important; | |
| margin-bottom: 16px !important; | |
| font-size: 14px !important; | |
| line-height: 1.6 !important; | |
| } | |
| #chatbot .message.user { | |
| background: var(--accent-soft) !important; | |
| border-color: rgba(99, 102, 241, 0.3) !important; | |
| } | |
| #chatbot .message.bot { | |
| background: var(--bg-base) !important; | |
| } | |
| #chatbot pre { | |
| background: var(--bg-base) !important; | |
| border: 1px solid var(--border-subtle) !important; | |
| border-radius: 6px !important; | |
| font-family: 'JetBrains Mono', monospace !important; | |
| font-size: 13px !important; | |
| } | |
| /* Code Editor (Canvas) */ | |
| #canvas-code { | |
| min-height: 540px !important; | |
| flex-grow: 1; | |
| background: var(--bg-base) !important; | |
| border-radius: 6px !important; | |
| border: 1px solid var(--border-subtle) !important; | |
| font-family: 'JetBrains Mono', monospace !important; | |
| } | |
| /* Form Elements */ | |
| input[type="text"], textarea, select { | |
| background-color: var(--bg-input) !important; | |
| border: 1px solid var(--border-subtle) !important; | |
| border-radius: 6px !important; | |
| color: var(--text-primary) !important; | |
| font-size: 14px !important; | |
| transition: border-color 0.2s ease, box-shadow 0.2s ease !important; | |
| } | |
| input[type="text"]:focus, textarea:focus { | |
| border-color: var(--accent) !important; | |
| box-shadow: 0 0 0 3px var(--accent-soft) !important; | |
| outline: none !important; | |
| } | |
| /* Gradio Form Overrides */ | |
| .gradio-container .form { background: transparent !important; border: 0 !important; } | |
| label { color: var(--text-secondary) !important; font-size: 13px !important; font-weight: 500 !important; } | |
| input[type="range"] { accent-color: var(--accent); } | |
| /* Buttons */ | |
| button { | |
| border-radius: 6px !important; | |
| transition: all 0.2s ease !important; | |
| font-weight: 500 !important; | |
| border: 1px solid var(--border-subtle) !important; | |
| background: var(--bg-input) !important; | |
| color: var(--text-primary) !important; | |
| font-size: 14px !important; | |
| } | |
| button:hover { background: var(--border-subtle) !important; } | |
| button.primary { | |
| background: var(--accent) !important; | |
| border-color: var(--accent) !important; | |
| color: white !important; | |
| } | |
| button.primary:hover { | |
| background: var(--accent-hover) !important; | |
| border-color: var(--accent-hover) !important; | |
| } | |
| /* Quick Tip */ | |
| .quick-tip { | |
| margin-top: 24px; padding: 12px 16px; | |
| border-radius: 6px; font-size: 13px; line-height: 1.5; | |
| color: var(--text-secondary); | |
| border: 1px solid var(--border-subtle); | |
| background: var(--bg-input); | |
| } | |
| .quick-tip b { color: var(--text-primary); display: block; margin-bottom: 4px; font-size: 12px; text-transform: uppercase; letter-spacing: 0.05em; } | |
| /* Responsive */ | |
| @media (max-width: 1024px) { | |
| .gradio-container { padding: 0 16px 16px !important; } | |
| #top-bar { flex-direction: column; align-items: flex-start; gap: 16px; } | |
| } | |
| """ | |
| THEME = ui.themes.Base( | |
| primary_hue="indigo", | |
| secondary_hue="slate", | |
| neutral_hue="zinc", | |
| ).set( | |
| body_background_fill="#09090b", | |
| body_text_color="#fafafa", | |
| block_background_fill="#0f0f12", | |
| block_border_color="#27272a", | |
| input_background_fill="#18181b", | |
| input_border_color="#27272a", | |
| button_primary_background_fill="#6366f1", | |
| button_primary_background_fill_hover="#4f46e5", | |
| button_primary_text_color="#ffffff", | |
| ) | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| # Interfaz Gradio | |
| # ───────────────────────────────────────────────────────────────────────────── | |
| with ui.Blocks(fill_height=True, title="DRK Code V1") as demo: | |
| hero = ui.HTML(hero_html("en")) | |
| canvas = ui.Code( | |
| value="", language=None, lines=28, max_lines=40, | |
| show_label=False, interactive=True, wrap_lines=True, | |
| show_line_numbers=True, buttons=["copy", "download"], | |
| elem_id="canvas-code", render=False, | |
| ) | |
| with ui.Row(equal_height=True): | |
| # Panel de Configuración | |
| with ui.Column(scale=1, min_width=260, elem_id="settings-panel"): | |
| idioma = ui.Dropdown( | |
| choices=[("English", "en"), ("Español", "es")], value="en", | |
| label="Language / Idioma", interactive=True, | |
| ) | |
| panel_header = ui.HTML(panel_html("en")) | |
| temperatura = ui.Slider(0, 1.5, value=0.35, step=0.05, label="Creativity", info="0 = precise · 1.5 = creative") | |
| top_p = ui.Slider(0.1, 1, value=0.9, step=0.05, label="Top P", info="Token diversity") | |
| max_tokens = ui.Slider(128, 4096, value=1024, step=128, label="Maximum length", info="Response tokens") | |
| longitud_automatica = ui.Checkbox(value=False, label="Auto length", info="Stops at EOS, context limit, or 120s timeout") | |
| tip = ui.HTML(tip_html("en")) | |
| # Chat Principal | |
| with ui.Column(scale=4, min_width=400, elem_id="main-chat"): | |
| chatbot = ui.Chatbot( | |
| elem_id="chatbot", height=620, show_label=False, | |
| buttons=["copy", "copy_all"], reasoning_tags=[("<think>", "</think>")], | |
| placeholder=placeholder_md("en"), render=False, | |
| ) | |
| caja = ui.Textbox( | |
| placeholder="Describe your task or paste code…", lines=2, max_lines=10, | |
| show_label=False, container=False, submit_btn="Send", stop_btn="Stop", render=False, | |
| ) | |
| ui.ChatInterface( | |
| fn=chat_bot, chatbot=chatbot, textbox=caja, | |
| additional_inputs=[idioma, temperatura, top_p, max_tokens, longitud_automatica], | |
| additional_outputs=[canvas], editable=True, fill_height=True, | |
| ) | |
| # Panel Canvas | |
| with ui.Column(scale=2, min_width=360, elem_id="canvas-panel"): | |
| canvas_header = ui.HTML(canvas_html("en")) | |
| canvas.render() | |
| # Eventos | |
| longitud_automatica.change( | |
| fn=lambda activada: ui.update(interactive=not activada), | |
| inputs=longitud_automatica, outputs=max_tokens, queue=False, | |
| ) | |
| idioma.change( | |
| fn=cambiar_idioma, inputs=idioma, | |
| outputs=[hero, panel_header, temperatura, top_p, max_tokens, longitud_automatica, tip, canvas_header, chatbot, caja], | |
| queue=False, | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(default_concurrency_limit=1, max_size=20).launch(theme=THEME, css=CSS) |