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.dockerignore

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+.git
+__pycache__/
+models/
+*.ipynb
+.env

Dockerfile

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+FROM python:3.10-slim
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PIP_NO_CACHE_DIR=1 \
+    HF_HUB_ENABLE_HF_TRANSFER=1 \
+    PORT=7860
+
+# Dependencias del sistema (mínimas)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential curl ca-certificates ffmpeg libgl1 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Python deps
+COPY requirements.txt .
+RUN pip install -U pip && pip install --no-cache-dir -r requirements.txt
+
+# Código
+COPY app.py ./app.py
+COPY README.md ./README.md
+
+EXPOSE 7860
+
+# (Opcional) Forzar un modelo más liviano desde Settings → Variables:
+# ENV MODEL_PATTERN=qwen2.5-7b-instruct-q3_k_m-*.gguf
+
+# Lanza Uvicorn en el puerto que Spaces define en $PORT
+CMD ["sh","-c","uvicorn app:app --host 0.0.0.0 --port ${PORT}"]

README.md

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----
-title: Astrohunters Llm
-emoji: 🌖
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Astrohunters LLM API (Docker)
+emoji: 🛰️
+colorFrom: indigo
+colorTo: purple
+license: apache-2.0
+---
+
+Endpoints:
+- `GET /healthz`
+- `POST /run_predict` { "prompt": "...", "system": "" }
+- `POST /run_predict_with_url` { "prompt": "...", "url": "https://...", "system": "" }

app.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os, glob, textwrap
+from pathlib import Path
+
+import gradio as gr
+from huggingface_hub import snapshot_download
+from llama_cpp import Llama
+import requests
+from bs4 import BeautifulSoup
+
+# ===== Modelo (GGUF) =====
+MODEL_REPO    = os.getenv("MODEL_REPO", "Qwen/Qwen2.5-7B-Instruct-GGUF")
+# Para CPU Basic puedes bajar a q3_k_m si te falta RAM
+MODEL_PATTERN = os.getenv("MODEL_PATTERN", "qwen2.5-7b-instruct-q4_k_m-*.gguf")
+
+LOCAL_DIR = Path("models"); LOCAL_DIR.mkdir(parents=True, exist_ok=True)
+print(f"[Boot] Descargando {MODEL_REPO} patrón {MODEL_PATTERN} ...")
+snapshot_dir = snapshot_download(repo_id=MODEL_REPO, local_dir=str(LOCAL_DIR),
+                                 allow_patterns=[MODEL_PATTERN])
+candidates = sorted(glob.glob(str(Path(snapshot_dir) / MODEL_PATTERN)))
+if not candidates:
+    raise FileNotFoundError(f"No hay shards para {MODEL_PATTERN} en {snapshot_dir}")
+MODEL_PATH = candidates[0]
+print(f"[Boot] Usando shard: {MODEL_PATH}")
+
+N_THREADS = max(1, (os.cpu_count() or 2) - 1)
+
+llm = Llama(
+    model_path=MODEL_PATH,
+    n_ctx=4096,
+    n_threads=N_THREADS,
+    n_batch=256,
+    n_gpu_layers=0,
+    verbose=False,
+)
+
+SYSTEM_DEFAULT = textwrap.dedent("""\
+Eres Astrohunters-Guide, un asistente en español.
+- Respondes con precisión y sin inventar datos.
+- Sabes explicar resultados de exoplanetas (período, duración, profundidad, SNR, radio).
+- Si te paso una URL, lees su contenido y lo usas como contexto.
+""")
+
+def fetch_url_text(url: str, max_chars: int = 6000) -> str:
+    try:
+        r = requests.get(url, timeout=15)
+        r.raise_for_status()
+        soup = BeautifulSoup(r.text, "html.parser")
+        for t in soup(["script", "style", "noscript"]): t.decompose()
+        txt = " ".join(soup.get_text(separator=" ").split())
+        return txt[:max_chars]
+    except Exception as e:
+        return f"[No se pudo cargar {url}: {e}]"
+
+def run_llm(messages, temperature=0.6, top_p=0.95, max_tokens=768):
+    out = llm.create_chat_completion(
+        messages=messages,
+        temperature=temperature,
+        top_p=top_p,
+        max_tokens=max_tokens,
+        stream=False,
+    )
+    return out["choices"][0]["message"]["content"].strip()
+
+# ====== Funciones API ======
+def api_run_predict(prompt: str, system: str = "") -> str:
+    messages = [
+        {"role": "system", "content": system or SYSTEM_DEFAULT},
+        {"role": "user", "content": prompt},
+    ]
+    return run_llm(messages, max_tokens=512)
+
+def api_run_predict_with_url(prompt: str, url: str = "", system: str = "") -> str:
+    web_ctx = fetch_url_text(url) if url else ""
+    user_msg = prompt if not web_ctx else f"{prompt}\n\n[CONTEXTO_WEB]\n{web_ctx}"
+    messages = [
+        {"role": "system", "content": system or SYSTEM_DEFAULT},
+        {"role": "user", "content": user_msg},
+    ]
+    return run_llm(messages, max_tokens=700)
+
+# ===== UI de chat =====
+with gr.Blocks(title="Astrohunters LLM (Qwen2.5 7B)") as chat_ui:
+    gr.Markdown("## 🛰️ Astrohunters LLM (Qwen2.5 7B Instruct, GGUF — CPU Basic)")
+    with gr.Row():
+        with gr.Column(scale=3):
+            chat = gr.Chatbot(height=420, type="tuples")
+            with gr.Row():
+                txt = gr.Textbox(placeholder="Escribe tu pregunta...", scale=4)
+                btn = gr.Button("Enviar", scale=1, variant="primary")
+        with gr.Column(scale=2):
+            system_tb = gr.Textbox(label="System prompt", value=SYSTEM_DEFAULT, lines=10)
+            url_tb = gr.Textbox(label="URL (opcional): Cargar contenido web", placeholder="https://...")
+
+    def chat_infer(history, system_prompt, user, url_to_load):
+        web_ctx = fetch_url_text(url_to_load.strip()) if url_to_load and url_to_load.strip() else ""
+        messages = [{"role": "system", "content": system_prompt or SYSTEM_DEFAULT}]
+        for u, a in history:
+            if u: messages.append({"role": "user", "content": u})
+            if a: messages.append({"role": "assistant", "content": a})
+        user_msg = user or ""
+        if web_ctx:
+            user_msg = f"{user_msg}\n\n[CONTEXTO_WEB]\n{web_ctx}"
+        messages.append({"role": "user", "content": user_msg})
+        reply = run_llm(messages, max_tokens=700)
+        history.append((user, reply))
+        return history, ""
+
+    btn.click(chat_infer, inputs=[chat, system_tb, txt, url_tb], outputs=[chat, txt])
+    txt.submit(chat_infer, inputs=[chat, system_tb, txt, url_tb], outputs=[chat, txt])
+
+# ====== APIs nombradas (fuera del Blocks) ======
+api1 = gr.Interface(
+    fn=api_run_predict,
+    inputs=[gr.Textbox(label="prompt"), gr.Textbox(label="system")],
+    outputs=gr.Textbox(label="reply"),
+    api_name="run_predict",
+)
+
+api2 = gr.Interface(
+    fn=api_run_predict_with_url,
+    inputs=[gr.Textbox(label="prompt"), gr.Textbox(label="url"), gr.Textbox(label="system")],
+    outputs=gr.Textbox(label="reply"),
+    api_name="run_predict_with_url",
+)
+
+# Unimos todo en un solo demo para que Gradio registre las rutas
+demo = gr.TabbedInterface(
+    [chat_ui, api1, api2],
+    tab_names=["Chat", "API: run_predict", "API: run_predict_with_url"],
+)
+
+if __name__ == "__main__":
+    demo.queue(max_size=16).launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+gradio==4.44.0
+huggingface_hub>=0.23.0
+llama-cpp-python==0.2.90
+fastapi>=0.115.0
+uvicorn>=0.24.0
+requests
+beautifulsoup4