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
from dotenv import load_dotenv

# === CONFIGURATION ===
load_dotenv()
ENDPOINT_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
EMBEDDING_MODEL = "intfloat/multilingual-e5-base"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
EX = Namespace("http://example.org/lang/")

# === STREAMLIT UI CONFIG ===
st.set_page_config(
    page_title="Language Atlas: South American Indigenous Languages",
    page_icon="🌍",
    layout="wide",
    initial_sidebar_state="expanded",
    menu_items={
        'About': "## AI-powered analysis of endangered indigenous languages\n"
                "Developed by Departamento Académico de Humanidades"
    }
)

# === CUSTOM CSS ===
st.markdown("""
<style>
    /* Main styles */
    .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: white;
        border-radius: 8px;
        padding: 1.5rem;
        box-shadow: 0 1px 3px rgba(0,0,0,0.1);
        margin: 1rem 0;
    }
    .language-card {
        background-color: #f9fafb;
        border-radius: 8px;
        padding: 1rem;
        margin: 0.5rem 0;
        border: 1px solid #e5e7eb;
    }
    
    /* Sidebar styles */
    .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;
    }
    
    /* Metrics and badges */
    .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="Loading AI models and knowledge graphs...")
def load_all_components():
    embedder = SentenceTransformer(EMBEDDING_MODEL, device=DEVICE)
    methods = {}
    for label, suffix, ttl, matrix_path in [
        ("InfoMatch", "_hybrid", "grafo_ttl_hibrido.ttl", "embed_matrix_hybrid.npy"),
        ("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):
    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"**Language:** {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"**Countries:** {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 generate_response(matrix, id_map, G, rdf, user_question, k=3):
    ids = get_top_k(matrix, id_map, user_question, k)
    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 = f"""<s>[INST]
You are an expert in South American indigenous languages.
Use strictly and only the information below to answer the user question in **English**.
- Do not infer or assume facts that are not explicitly stated.
- If the answer is unknown or insufficient, say \"I cannot answer with the available data.\"
- Limit your answer to 100 words.
### CONTEXT:
{chr(10).join(context)}
### RDF RELATIONS:
{chr(10).join(rdf_facts)}
### QUESTION:
{user_question}
Answer:
[/INST]"""
    try:
        res = requests.post(
            ENDPOINT_URL,
            headers={"Authorization": f"Bearer {HF_API_TOKEN}", "Content-Type": "application/json"},
            json={"inputs": prompt}, timeout=60
        )
        out = res.json()
        if isinstance(out, list) and "generated_text" in out[0]:
            return out[0]["generated_text"].replace(prompt.strip(), "").strip(), ids, context, rdf_facts
        return str(out), ids, context, rdf_facts
    except Exception as e:
        return str(e), ids, context, rdf_facts

# === MAIN APP ===
def main():
    # Load components
    methods, embedder = load_all_components()

    # Main header
    st.markdown("""
    <div class="header">
        <h1>🌍 Language Atlas: South American Indigenous Languages</h1>
    </div>
    """, unsafe_allow_html=True)
    
    # Overview section
    with st.expander("📌 Overview", expanded=True):
        st.markdown("""
        This app provides **AI-powered analysis** of endangered indigenous languages in South America, 
        integrating knowledge graphs from **Glottolog, Wikipedia, and Wikidata**.
        """)
        
        cols = st.columns(2)
        with cols[0]:
            st.markdown("""
            <div class="feature-card">
                <h4>🔹 Two AI Methods Available:</h4>
                <ul>
                    <li><b>InfoMatch</b> (Node2Vec + Textual Data)</li>
                    <li><b>LinkGraph</b> (GraphSAGE + Structured Relations)</li>
                </ul>
                <p>🔹 <b>Powered by Mistral-7B</b> for contextual responses</p>
            </div>
            """, unsafe_allow_html=True)
        
        with cols[1]:
            st.markdown("""
            <div class="feature-card">
                <h4>🛠️ Features</h4>
                <ul>
                    <li>✅ Multisource Knowledge Graph</li>
                    <li>✅ Hybrid AI Analysis</li>
                    <li>✅ Comparative Results</li>
                    <li>✅ Structured & Unstructured Data</li>
                </ul>
            </div>
            """, unsafe_allow_html=True)
    
    # Sidebar
    with st.sidebar:
        # Logo and academic info
        st.markdown("### Departamento Académico de Humanidades")
        st.markdown("---")
        
        # Quick start guide
        st.markdown("### 🚀 Quick Start")
        st.markdown("""
        1. **Type a question** in the input box  
        2. **Click 'Analyze'** to compare methods  
        3. **Explore results** with expandable details  
        """)
        
        st.markdown("---")
        
        # Suggested questions
        st.markdown("### 🔍 Example Queries")
        questions = [
            "What languages are endangered in Brazil?",
            "How many speakers does Aymara have?",
            "Which languages are related to Quechua?",
            "Where is Mapudungun spoken?"
        ]
        
        for q in questions:
            if st.markdown(f"<div class='suggested-question'>{q}</div>", unsafe_allow_html=True):
                st.session_state.query = q
        
        st.markdown("---")
        
        # Technical details
        st.markdown("### ⚙️ Technical Details")
        st.markdown("""
        - <span class="tech-badge">Embeddings</span> Node2Vec vs. GraphSAGE  
        - <span class="tech-badge">Language Model</span> Mistral-7B-Instruct  
        - <span class="tech-badge">Knowledge Graph</span> RDF-based integration  
        """, unsafe_allow_html=True)
        
        st.markdown("---")
        
        # Data sources
        st.markdown("### 📂 Data Sources")
        st.markdown("""
        - **Glottolog** (Language classification)  
        - **Wikipedia** (Textual summaries)  
        - **Wikidata** (Structured facts)  
        """)
        
        st.markdown("---")
        
        # Analysis parameters
        st.markdown("### 📊 Analysis Parameters")
        k = st.slider("Number of languages to analyze", 1, 10, 3)
        st.markdown("---")
        
        # Debug options
        st.markdown("### 🔧 Advanced Options")
        show_ctx = st.checkbox("Show context information", False)
        show_rdf = st.checkbox("Show structured facts", False)

    # Main query interface
    st.markdown("### 📝 Ask About Indigenous Languages")
    query = st.text_input(
        "Enter your question:",
        value=st.session_state.get("query", ""),
        label_visibility="collapsed",
        placeholder="e.g. What languages are spoken in Peru?"
    )

    if st.button("Analyze", type="primary", use_container_width=True):
        if not query:
            st.warning("Please enter a question")
            return
            
        col1, col2 = st.columns(2)
        
        for col, (label, method) in zip([col1, col2], methods.items()):
            with col:
                st.markdown(f"#### {label} Method")
                st.caption({
                    "InfoMatch": "Node2Vec embeddings combining text and graph structure",
                    "LinkGraph": "GraphSAGE embeddings capturing network patterns"
                }[label])
                
                start = datetime.datetime.now()
                response, lang_ids, context, rdf_data = generate_response(*method, query, k)
                duration = (datetime.datetime.now() - start).total_seconds()
                
                # Response display
                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)} languages</span>
                    </div>
                </div>
                """, unsafe_allow_html=True)
                
                # Additional information
                if show_ctx:
                    with st.expander(f"📖 Context from {len(lang_ids)} languages"):
                        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("🔗 Structured facts (RDF)"):
                        st.code("\n".join(rdf_data))
        
        # Footer note
        st.markdown("---")
        st.markdown("""
        <div style="font-size: 0.8rem; color: #64748b; text-align: center;">
        <b>📌 Note:</b> This tool is designed for researchers, linguists, and cultural preservationists.  
        For best results, use specific questions about languages, families, or regions.
        </div>
        """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()