import os
import subprocess
import pandas as pd
import folium
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from textblob import TextBlob
from gtts import gTTS
import speech_recognition as sr
from deep_translator import GoogleTranslator
from collections import Counter
import re
import json

# We will import _index from rag_pipeline to get documents
import rag_pipeline

# ── Map Generation ──────────────────────────────────────────────
LOCATIONS = [
    {"name": "Jerusalem (Al-Quds)", "lat": 31.7683, "lon": 35.2137,
     "query": "Jerusalem Al-Quds occupation history destruction",
     "desc": "The capital of Palestine, central to its history, culture, and religious identity."},
    {"name": "Gaza", "lat": 31.5017, "lon": 34.4668,
     "query": "Gaza destruction casualties humanitarian crisis displaced",
     "desc": "One of the oldest cities; subject of military operations and humanitarian siege."},
    {"name": "Ramallah", "lat": 31.9038, "lon": 35.2034,
     "query": "Ramallah West Bank Palestinian Authority",
     "desc": "A major Palestinian cultural and political center in the West Bank."},
    {"name": "Hebron (Al-Khalil)", "lat": 31.5326, "lon": 35.0998,
     "query": "Hebron Al-Khalil settlements occupation",
     "desc": "A historic city known for the Ibrahimi Mosque and traditional crafts."},
    {"name": "Nablus", "lat": 32.2211, "lon": 35.2544,
     "query": "Nablus West Bank raids settlements",
     "desc": "Famous for its traditional soap, knafeh, and historic old city."},
    {"name": "Haifa", "lat": 32.7940, "lon": 34.9896,
     "query": "Haifa Nakba 1948 Palestinian expelled",
     "desc": "A historic coastal city, largely depopulated during the 1948 Nakba."},
    {"name": "Jaffa (Yafa)", "lat": 32.0504, "lon": 34.7522,
     "query": "Jaffa Yafa Nakba 1948 destruction port expelled",
     "desc": "Historically one of Palestine's most important port cities, depopulated in 1948."},
    {"name": "Rafah", "lat": 31.2956, "lon": 34.2527,
     "query": "Rafah crossing humanitarian aid evacuation bombardment",
     "desc": "A border city in southern Gaza; key crossing for humanitarian aid."},
    {"name": "Khan Yunis", "lat": 31.3436, "lon": 34.3061,
     "query": "Khan Yunis destruction bombardment casualties",
     "desc": "One of Gaza's largest cities, heavily affected by military operations."},
    {"name": "Jenin", "lat": 32.4641, "lon": 35.2961,
     "query": "Jenin refugee camp military operation incursion",
     "desc": "Home to one of the West Bank's largest refugee camps."},
]


def _get_location_facts(query: str) -> str:
    """Retrieve document excerpts relevant to a location. Returns formatted HTML."""
    if rag_pipeline._retriever is None:
        return ""
    try:
        nodes = rag_pipeline._retriever.retrieve(query)
        if not nodes:
            return ""
        snippets = []
        seen = set()
        for node in nodes[:3]:
            text   = node.node.get_content()[:280].strip().replace("\n", " ")
            source = node.node.metadata.get("source", "")
            page   = node.node.metadata.get("page_number", "?")
            key    = (source, page)
            if key in seen:
                continue
            seen.add(key)
            src_label = (source[:45] + "...") if len(source) > 45 else source
            snippets.append(
                f'<blockquote style="font-size:11px;margin:4px 0;border-left:3px solid #c00;'
                f'padding-left:6px;color:#222;">'
                f'"{text}..."<br>'
                f'<i style="color:#666;">&#8212; {src_label}, p.{page}</i>'
                f'</blockquote>'
            )
        return "".join(snippets)
    except Exception:
        return ""


def generate_map():
    m = folium.Map(location=[31.5, 34.8], zoom_start=8, tiles="CartoDB positron")

    for loc in LOCATIONS:
        doc_facts = _get_location_facts(loc["query"])

        popup_html = (
            f'<div style="font-family:Arial,sans-serif;max-width:340px;direction:auto;">'
            f'<h4 style="margin:0 0 6px;color:#1a1a1a;">{loc["name"]}</h4>'
            f'<p style="font-size:12px;color:#333;margin:0 0 8px;">{loc["desc"]}</p>'
        )
        if doc_facts:
            popup_html += (
                f'<hr style="border:none;border-top:1px solid #ddd;margin:6px 0;">'
                f'<p style="font-size:11px;font-weight:bold;color:#c00;margin:0 0 4px;">'
                f'&#128196; From the Documents:</p>'
                f'{doc_facts}'
            )
        popup_html += "</div>"

        folium.Marker(
            location=[loc["lat"], loc["lon"]],
            popup=folium.Popup(popup_html, max_width=360),
            tooltip=folium.Tooltip(loc["name"], sticky=True),
            icon=folium.Icon(color="red", icon="info-sign"),
        ).add_to(m)

    map_path = "palestine_map.html"
    m.save(map_path)
    return map_path

# ── Timeline Generation ──────────────────────────────────────────────
def generate_timeline():
    timeline_html = """
    <div style="font-family: Arial, sans-serif; padding: 20px;">
        <h3>Historical Timeline of the Palestinian Cause</h3>
        <ul style="border-left: 2px solid #333; padding-left: 20px;">
            <li style="margin-bottom: 10px;"><b>1917:</b> Balfour Declaration issued by the British government.</li>
            <li style="margin-bottom: 10px;"><b>1947:</b> UN General Assembly adopts Resolution 181 (Partition Plan).</li>
            <li style="margin-bottom: 10px;"><b>1948:</b> The Nakba (Catastrophe); hundreds of thousands of Palestinians displaced.</li>
            <li style="margin-bottom: 10px;"><b>1967:</b> The Naksa (Setback); occupation of the West Bank, Gaza, and East Jerusalem.</li>
            <li style="margin-bottom: 10px;"><b>1987:</b> The First Intifada begins.</li>
            <li style="margin-bottom: 10px;"><b>1993:</b> Oslo Accords signed.</li>
            <li style="margin-bottom: 10px;"><b>2000:</b> The Second Intifada begins.</li>
            <li style="margin-bottom: 10px;"><b>Present:</b> Ongoing struggle for self-determination and human rights.</li>
        </ul>
    </div>
    """
    return timeline_html

# ── Word Cloud Generation ──────────────────────────────────────────────
def generate_wordcloud(doc_name="All"):
    if rag_pipeline._index is None:
        return None
    
    docstore = rag_pipeline._index.docstore
    nodes = list(docstore.docs.values())
    
    text = ""
    for node in nodes:
        if doc_name is None or doc_name == "All" or node.metadata.get("source") == doc_name:
            text += node.get_content() + " "
            
    if not text.strip():
        # Fallback if no text
        text = "Palestine History Culture Rights Peace Justice Freedom"
        
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    img_path = "wordcloud.png"
    plt.savefig(img_path, bbox_inches='tight')
    plt.close()
    return img_path

# ── Statistics Generation ──────────────────────────────────────────────
def get_statistics():
    if rag_pipeline._index is None:
        return pd.DataFrame(), pd.DataFrame()
        
    docstore = rag_pipeline._index.docstore
    nodes = list(docstore.docs.values())
    
    data = []
    for node in nodes:
        source = node.metadata.get("source", "Unknown")
        page = node.metadata.get("page_number", 0)
        length = len(node.get_content())
        data.append({"Source": source, "Page": page, "Length": length})
        
    df = pd.DataFrame(data)
    
    if df.empty:
        return pd.DataFrame(), pd.DataFrame()
        
    stats_df = df.groupby('Source').agg(
        Chunks=('Source', 'count'),
        Total_Length=('Length', 'sum'),
        Avg_Length=('Length', 'mean')
    ).reset_index()
    
    return stats_df, df

# ── Advanced Analytics ──────────────────────────────────────────────
def advanced_analytics(text):
    if not text or not text.strip():
        return "No text provided for analysis."
        
    blob = TextBlob(text)
    sentiment = blob.sentiment
    sentiment_str = f"Polarity: {sentiment.polarity:.2f} (Negative < 0 < Positive), Subjectivity: {sentiment.subjectivity:.2f} (Objective < 0.5 < Subjective)"
    
    words = re.findall(r'\b[A-Z][a-z]+\b', text)
    freq = Counter(words)
    common_entities = freq.most_common(10)
    
    analytics_report = f"### Sentiment Analysis\n{sentiment_str}\n\n"
    analytics_report += "### Frequent Capitalized Entities (Heuristic)\n"
    for ent, count in common_entities:
        analytics_report += f"- **{ent}**: {count}\n"
        
    return analytics_report

# ── Audio Features ──────────────────────────────────────────────
def text_to_speech(text, lang='en'):
    try:
        tts = gTTS(text=text, lang=lang)
        output_path = "output_audio.mp3"
        tts.save(output_path)
        return output_path
    except Exception as e:
        print(f"TTS Error: {e}")
        return None

def speech_to_text(audio_path):
    if not audio_path:
        return ""
        
    wav_path = audio_path
    if not audio_path.lower().endswith(".wav"):
        wav_path = "temp_stt.wav"
        try:
            subprocess.run(["ffmpeg", "-y", "-i", audio_path, wav_path], check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
        except Exception as e:
            error_msg = f"[Voice Input Error: Audio conversion failed: {str(e)}]"
            print(error_msg)
            return error_msg
            
    r = sr.Recognizer()
    try:
        with sr.AudioFile(wav_path) as source:
            audio_data = r.record(source)
            text = r.recognize_google(audio_data)
            return text
    except Exception as e:
        error_msg = f"[Voice Input Error: {str(e)}]"
        print(error_msg)
        return error_msg

# ── Translation ──────────────────────────────────────────────
def translate_text(text, target_lang='en'):
    try:
        translator = GoogleTranslator(source='auto', target=target_lang)
        return translator.translate(text)
    except Exception as e:
        print(f"Translation Error: {e}")
        return text

# ── Export Chat ──────────────────────────────────────────────
def export_chat_history(history):
    if not history:
        return None
    
    # Convert objects to dicts if necessary for JSON serialization
    cleaned_history = []
    for msg in history:
        if isinstance(msg, dict):
            cleaned_history.append(msg)
        else:
            cleaned_history.append({"role": getattr(msg, "role", "unknown"), "content": getattr(msg, "content", "")})
            
    file_path = "chat_history.json"
    with open(file_path, "w", encoding="utf-8") as f:
        json.dump(cleaned_history, f, ensure_ascii=False, indent=4)
    return file_path