Update app.py

app.py

@@ -1,136 +1,154 @@
-#!/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)
+#!/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
+
+# FastAPI + CORS + mount de Gradio
+from fastapi import FastAPI, Body
+from fastapi.middleware.cors import CORSMiddleware
+from gradio.routes import mount_gradio_app
+
+# ===== Config del modelo (puedes cambiar por variables de entorno) =====
+MODEL_REPO    = os.getenv("MODEL_REPO", "Qwen/Qwen2.5-7B-Instruct-GGUF")
+# En CPU Basic, si te falta RAM/tiempo de inferencia: qwen2.5-7b-instruct-q3_k_m-*.gguf
+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}")
+
+# Hilos seguros para CPU Basic
+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()
+
+# ====== Lógica 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 (Gradio) ======
+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])
+
+# ====== FastAPI + CORS + endpoints REST ======
+api = FastAPI()
+# CORS (ajusta ALLOWED_ORIGINS a tu dominio en Settings > Variables si quieres)
+ALLOWED_ORIGINS = os.getenv("ALLOWED_ORIGINS", "*").split(",")
+api.add_middleware(
+    CORSMiddleware,
+    allow_origins=ALLOWED_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@api.get("/healthz")
+def healthz():
+    return {"ok": True}
+
+@api.post("/run_predict")
+def run_predict(body: dict = Body(...)):
+    prompt = body.get("prompt", "")
+    system = body.get("system", "")
+    return {"reply": api_run_predict(prompt, system)}
+
+@api.post("/run_predict_with_url")
+def run_predict_with_url(body: dict = Body(...)):
+    prompt = body.get("prompt", "")
+    url    = body.get("url", "")
+    system = body.get("system", "")
+    return {"reply": api_run_predict_with_url(prompt, url, system)}
+
+# Montamos la UI de Gradio bajo la raíz
+app = mount_gradio_app(api, chat_ui, path="/")
+
+if __name__ == "__main__":
+    # Para ejecución local (en Docker usamos Uvicorn del CMD)
+    chat_ui.queue(max_size=16).launch(server_name="0.0.0.0", server_port=7860)