import streamlit as st
import datetime
import pickle
import numpy as np
import rdflib
import torch
import os
import requests
from rdflib import Graph as RDFGraph, Namespace
from sentence_transformers import SentenceTransformer

# === STREAMLIT UI CONFIG ===
st.set_page_config(
    page_title="Atlas de Lenguas: Lenguas Indígenas Sudamericanas",
    page_icon="🌍",
    layout="wide",
    initial_sidebar_state="expanded",
    menu_items={
        'About': "## Análisis con IA de lenguas indígenas en peligro\n"
                 "Esta aplicación integra grafos de conocimiento de Glottolog, Wikipedia y Wikidata."
    }
)

# === CONFIGURATION ===
ENDPOINT_URL = "https://lxgkooi70bj7diiu.us-east-1.aws.endpoints.huggingface.cloud"
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
if not HF_API_TOKEN:
    st.error("⚠️ No se cargó el token HF_API_TOKEN desde los Secrets.")
else:
    st.success("✅ Token cargado correctamente.")
EMBEDDING_MODEL = "intfloat/multilingual-e5-base"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
EX = Namespace("http://example.org/lang/")

# === CUSTOM CSS ===
st.markdown("""
<style>
    .header {
        color: #2c3e50;
        border-bottom: 2px solid #4f46e5;
        padding-bottom: 0.5rem;
        margin-bottom: 1.5rem;
    }
    .feature-card {
        background-color: #f8fafc;
        border-radius: 8px;
        padding: 1rem;
        margin: 0.5rem 0;
        border-left: 3px solid #4f46e5;
    }
    .response-card {
    background-color: #fdfdfd;
    color: #1f2937;
    border-radius: 8px;
    padding: 1.5rem;
    box-shadow: 0 2px 6px rgba(0,0,0,0.08);
    margin: 1rem 0;
    font-size: 1rem;
    line-height: 1.5;
    }
    .language-card {
        background-color: #f9fafb;
        border-radius: 8px;
        padding: 1rem;
        margin: 0.5rem 0;
        border: 1px solid #e5e7eb;
    }
    .sidebar-section {
        margin-bottom: 1.5rem;
    }
    .sidebar-title {
        font-weight: 600;
        color: #4f46e5;
    }
    .suggested-question {
        padding: 0.5rem;
        margin: 0.25rem 0;
        border-radius: 4px;
        cursor: pointer;
        transition: all 0.2s;
    }
    .suggested-question:hover {
        background-color: #f1f5f9;
    }
    .metric-badge {
        display: inline-block;
        background-color: #e8f4fc;
        padding: 0.25rem 0.5rem;
        border-radius: 4px;
        font-size: 0.85rem;
        margin-right: 0.5rem;
    }
    .tech-badge {
        background-color: #ecfdf5;
        color: #065f46;
        padding: 0.25rem 0.5rem;
        border-radius: 4px;
        font-size: 0.75rem;
        font-weight: 500;
    }
</style>
""", unsafe_allow_html=True)

# === CORE FUNCTIONS ===
@st.cache_resource(show_spinner="Cargando modelos de IA y grafos de conocimiento...")
def load_all_components():
    embedder = SentenceTransformer(EMBEDDING_MODEL, device=DEVICE)
    methods = {}
    label, suffix, ttl, matrix_path = ("LinkGraph", "_hybrid_graphsage", "grafo_ttl_hibrido_graphsage.ttl", "embed_matrix_hybrid_graphsage.npy")
    with open(f"id_map{suffix}.pkl", "rb") as f:
        id_map = pickle.load(f)
    with open(f"grafo_embed{suffix}.pickle", "rb") as f:
        G = pickle.load(f)
    matrix = np.load(matrix_path)
    rdf = RDFGraph()
    rdf.parse(ttl, format="ttl")
    methods[label] = (matrix, id_map, G, rdf)
    return methods, embedder

def get_top_k(matrix, id_map, query, k, embedder):
    vec = embedder.encode(f"query: {query}", convert_to_tensor=True, device=DEVICE)
    vec = vec.cpu().numpy().astype("float32")
    sims = np.dot(matrix, vec) / (np.linalg.norm(matrix, axis=1) * np.linalg.norm(vec) + 1e-10)
    top_k_idx = np.argsort(sims)[-k:][::-1]
    return [id_map[i] for i in top_k_idx]

def get_context(G, lang_id):
    node = G.nodes.get(lang_id, {})
    lines = [f"**Lengua:** {node.get('label', lang_id)}"]
    if node.get("wikipedia_summary"):
        lines.append(f"**Wikipedia:** {node['wikipedia_summary']}")
    if node.get("wikidata_description"):
        lines.append(f"**Wikidata:** {node['wikidata_description']}")
    if node.get("wikidata_countries"):
        lines.append(f"**Países:** {node['wikidata_countries']}")
    return "\n\n".join(lines)

def query_rdf(rdf, lang_id):
    q = f"""
    PREFIX ex: <http://example.org/lang/>
    SELECT ?property ?value WHERE {{ ex:{lang_id} ?property ?value }}
    """
    try:
        return [(str(row[0]).split("/")[-1], str(row[1])) for row in rdf.query(q)]
    except Exception as e:
        return [("error", str(e))]

def query_llm(prompt):
    try:
        res = requests.post(
            ENDPOINT_URL,
            headers={"Authorization": f"Bearer {HF_API_TOKEN}", "Content-Type": "application/json"},
            json={"inputs": prompt}, timeout=30
        )
        res.raise_for_status()
        out = res.json()
        if isinstance(out, list):
            if len(out) > 0 and isinstance(out[0], dict) and "generated_text" in out[0]:
                return out[0]["generated_text"].strip()
        elif isinstance(out, dict) and "generated_text" in out:
            return out["generated_text"].strip()
        elif isinstance(out, dict) and "text" in out:
            return out["text"].strip()
        return "Sin respuesta del modelo."
    except Exception as e:
        return f"Error al consultar el modelo: {str(e)}"

def generate_response(matrix, id_map, G, rdf, user_question, k, embedder):
    ids = get_top_k(matrix, id_map, user_question, k, embedder)
    context = [get_context(G, i) for i in ids]
    rdf_facts = []
    for i in ids:
        rdf_facts.extend([f"{p}: {v}" for p, v in query_rdf(rdf, i)])

    prompt_es = (
        "Contexto: " + " ".join(context) + "\n" +
        "Hechos RDF: " + ", ".join(rdf_facts) + "\n" +
        f"Pregunta: {user_question} Responde en español:"
    )

    prompt_en = (
        "Context: " + " ".join(context) + "\n" +
        "RDF facts: " + ", ".join(rdf_facts) + "\n" +
        f"Question: {user_question} Answer in English:"
    )

    response_es = query_llm(prompt_es)
    response_en = query_llm(prompt_en)

    full_response = (
        f"<b>Respuesta en español:</b><br>{response_es}<br><br>"
        f"<b>Answer in English:</b><br>{response_en}"
    )
    return full_response, ids, context, rdf_facts

# === UI MAIN ===
def main():
    methods, embedder = load_all_components()
    st.markdown("""
    <div class="header">
        <h1>🌍 Atlas de Lenguas: Lenguas Indígenas Sudamericanas</h1>
    </div>
    """, unsafe_allow_html=True)

    with st.sidebar:
        st.markdown("### 📚 Información de Contacto")
        st.markdown("""
        - <span class="tech-badge">Correo: jxvera@gmail.com</span>
        """, unsafe_allow_html=True)

        st.markdown("---")
        st.markdown("### 🚀 Inicio Rápido")
        st.markdown("""
        1. **Escribe una pregunta** en el cuadro de entrada
        2. **Haz clic en 'Analizar'** para obtener la respuesta
        3. **Explora los resultados** con los detalles expandibles
        """)

        st.markdown("---")
        st.markdown("### 🔍 Preguntas de Ejemplo")
        questions = [
            "¿Qué idiomas están en peligro en Brasil?",
            "¿Qué idiomas se hablan en Perú?",
            "¿Cuáles idiomas están relacionados con el Quechua?",
            "¿Dónde se habla el Mapudungun?"
        ]
        for q in questions:
            if st.button(q, key=f"suggested_{q}", use_container_width=True):
                st.session_state.query = q

        st.markdown("---")
        st.markdown("### 📊 Parámetros de Análisis")
        k = st.slider("Número de idiomas a analizar", 1, 10, 3)

        st.markdown("---")
        st.markdown("### 🔧 Opciones Avanzadas")
        show_ctx = st.checkbox("Mostrar información de contexto", False)
        show_rdf = st.checkbox("Mostrar hechos estructurados", False)

    st.markdown("### 📝 Haz una pregunta sobre lenguas indígenas")
    query = st.text_input(
        "Ingresa tu pregunta:",
        value=st.session_state.get("query", ""),
        label_visibility="collapsed",
        placeholder="Ej. ¿Qué lenguas se hablan en Perú?"
    )

    if st.button("Analizar", type="primary", use_container_width=True):
        if not query:
            st.warning("Por favor, ingresa una pregunta")
            return
        label = "LinkGraph"
        method = methods[label]
        start = datetime.datetime.now()
        response, lang_ids, context, rdf_data = generate_response(*method, query, k, embedder)
        duration = (datetime.datetime.now() - start).total_seconds()
        st.markdown(f"""
        <div class="response-card">
            {response}
            <div style="margin-top: 1rem;">
                <span class="metric-badge">⏱️ {duration:.2f}s</span>
                <span class="metric-badge">🌐 {len(lang_ids)} idiomas</span>
            </div>
        </div>
        """, unsafe_allow_html=True)

        if show_ctx:
            with st.expander(f"📖 Contexto de {len(lang_ids)} idiomas"):
                for lang_id, ctx in zip(lang_ids, context):
                    st.markdown(f"<div class='language-card'>{ctx}</div>", unsafe_allow_html=True)

        if show_rdf:
            with st.expander("🔗 Hechos estructurados (RDF)"):
                st.code("\n".join(rdf_data))

    st.markdown("---")
    st.markdown("""
    <div style="font-size: 0.8rem; color: #64748b; text-align: center;">
    <b>📌 Nota:</b> Esta herramienta está diseñada para investigadores, lingüistas y preservacionistas culturales.
      Para mejores resultados, usa preguntas específicas sobre idiomas, familias o regiones.
    </div>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()