import streamlit as st
import base64, cv2, glob, json, math, os, pytz, random, re, requests, textract, time, zipfile
import plotly.graph_objects as go
import streamlit.components.v1 as components
from datetime import datetime
from audio_recorder_streamlit import audio_recorder
from bs4 import BeautifulSoup
from collections import defaultdict
from dotenv import load_dotenv
from gradio_client import Client
from huggingface_hub import InferenceClient
from io import BytesIO
from PIL import Image
from PyPDF2 import PdfReader
from urllib.parse import quote
from xml.etree import ElementTree as ET
import extra_streamlit_components as stx
from streamlit.runtime.scriptrunner import get_script_run_ctx
import asyncio
import edge_tts

# -------------------- Configuration --------------------
st.set_page_config(
    page_title="🚲BikeAI🏆 ArXiv Voice Research",
    page_icon="🚲🏆",
    layout="wide",
    initial_sidebar_state="auto",
    menu_items={
        'Get Help': 'https://huggingface.co/awacke1',
        'Report a bug': 'https://huggingface.co/spaces/awacke1',
        'About': "🚲BikeAI🏆 ArXiv Voice Research"
    }
)
load_dotenv()

USER_NAMES = [
    "Aria", "Guy", "Sonia", "Tony", "Jenny", "Davis", "Libby", "Clara", "Liam", "Natasha", "William"
]

ENGLISH_VOICES = [
    "en-US-AriaNeural", "en-US-GuyNeural", "en-GB-SoniaNeural", "en-GB-TonyNeural",
    "en-US-JennyNeural", "en-US-DavisNeural", "en-GB-LibbyNeural", "en-CA-ClaraNeural",
    "en-CA-LiamNeural", "en-AU-NatashaNeural", "en-AU-WilliamNeural"
]

USER_VOICES = dict(zip(USER_NAMES, ENGLISH_VOICES))

if 'user_name' not in st.session_state:
    st.session_state['user_name'] = USER_NAMES[0]
if 'old_val' not in st.session_state:
    st.session_state['old_val'] = None
if 'viewing_prefix' not in st.session_state:
    st.session_state['viewing_prefix'] = None
if 'should_rerun' not in st.session_state:
    st.session_state['should_rerun'] = False

FILE_EMOJIS = {
    "md": "📝",
    "mp3": "🎵",
}

def get_high_info_terms(text: str) -> list:
    stop_words = set([
        'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with',
        'by', 'from', 'up', 'about', 'into', 'over', 'after', 'is', 'are', 'was', 'were',
        'be', 'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would',
        'should', 'could', 'might', 'must', 'shall', 'can', 'may', 'this', 'that', 'these',
        'those', 'i', 'you', 'he', 'she', 'it', 'we', 'they', 'what', 'which', 'who',
        'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most',
        'other', 'some', 'such', 'than', 'too', 'very', 'just', 'there'
    ])
    key_phrases = [
        'artificial intelligence', 'machine learning', 'deep learning', 'neural network',
        'personal assistant', 'natural language', 'computer vision', 'data science',
        'reinforcement learning', 'knowledge graph', 'semantic search', 'time series',
        'large language model', 'transformer model', 'attention mechanism',
        'autonomous system', 'edge computing', 'quantum computing', 'blockchain technology',
        'cognitive science', 'human computer', 'decision making', 'arxiv search',
        'research paper', 'scientific study', 'empirical analysis'
    ]

    preserved_phrases = []
    lower_text = text.lower()
    for phrase in key_phrases:
        if phrase in lower_text:
            preserved_phrases.append(phrase)
            text = text.replace(phrase, '')

    words = re.findall(r'\b\w+(?:-\w+)*\b', text)
    high_info_words = [
        word.lower() for word in words
        if len(word) > 3
        and word.lower() not in stop_words
        and not word.isdigit()
        and any(c.isalpha() for c in word)
    ]

    all_terms = preserved_phrases + high_info_words
    seen = set()
    unique_terms = []
    for term in all_terms:
        if term not in seen:
            seen.add(term)
            unique_terms.append(term)

    return unique_terms[:5]

def clean_text_for_filename(text: str) -> str:
    text = text.lower()
    text = re.sub(r'[^\w\s-]', '', text)
    words = text.split()
    stop_short = set(['the','and','for','with','this','that','from','just','very','then','been','only','also','about'])
    filtered = [w for w in words if len(w)>3 and w not in stop_short]
    return '_'.join(filtered)[:200]

def generate_filename(prompt, response, file_type="md"):
    prefix = datetime.now().strftime("%y%m_%H%M") + "_"
    combined = (prompt + " " + response).strip()
    info_terms = get_high_info_terms(combined)

    snippet = (prompt[:100] + " " + response[:100]).strip()
    snippet_cleaned = clean_text_for_filename(snippet)
    name_parts = info_terms + [snippet_cleaned]
    full_name = '_'.join(name_parts)

    if len(full_name) > 150:
        full_name = full_name[:150]

    filename = f"{prefix}{full_name}.{file_type}"
    return filename

def create_file(prompt, response, file_type="md"):
    filename = generate_filename(prompt.strip(), response.strip(), file_type)
    with open(filename, 'w', encoding='utf-8') as f:
        f.write(prompt + "\n\n" + response)
    return filename

def get_download_link(file):
    with open(file, "rb") as f:
        b64 = base64.b64encode(f.read()).decode()
    return f'<a href="data:file/zip;base64,{b64}" download="{os.path.basename(file)}">📂 Download {os.path.basename(file)}</a>'

def clean_for_speech(text: str) -> str:
    text = text.replace("\n", " ")
    text = text.replace("</s>", " ")
    text = text.replace("#", "")
    text = re.sub(r"\(https?:\/\/[^\)]+\)", "", text)
    text = re.sub(r"\s+", " ", text).strip()
    return text

async def edge_tts_generate_audio(text, voice="en-US-AriaNeural", rate=0, pitch=0):
    text = clean_for_speech(text)
    if not text.strip():
        return None
    rate_str = f"{rate:+d}%"
    pitch_str = f"{pitch:+d}Hz"
    communicate = edge_tts.Communicate(text, voice, rate=rate_str, pitch=pitch_str)
    out_fn = generate_filename(text, text, "mp3")
    try:
        await communicate.save(out_fn)
    except edge_tts.exceptions.NoAudioReceived:
        st.error("No audio was received from TTS service.")
        return None
    return out_fn

def speak_with_edge_tts(text, voice="en-US-AriaNeural", rate=0, pitch=0):
    return asyncio.run(edge_tts_generate_audio(text, voice, rate, pitch))

def play_and_download_audio(file_path):
    if file_path and os.path.exists(file_path):
        st.audio(file_path)
        dl_link = f'<a href="data:audio/mpeg;base64,{base64.b64encode(open(file_path,"rb").read()).decode()}" download="{os.path.basename(file_path)}">Download {os.path.basename(file_path)}</a>'
        st.markdown(dl_link, unsafe_allow_html=True)

def load_files_for_sidebar():
    md_files = glob.glob("*.md")
    mp3_files = glob.glob("*.mp3")

    md_files = [f for f in md_files if os.path.basename(f).lower() != 'readme.md']
    all_files = md_files + mp3_files

    groups = defaultdict(list)
    for f in all_files:
        fname = os.path.basename(f)
        prefix = fname[:10]
        groups[prefix].append(f)

    for prefix in groups:
        groups[prefix].sort(key=lambda x: os.path.getmtime(x), reverse=True)

    sorted_prefixes = sorted(groups.keys(),
                             key=lambda pre: max(os.path.getmtime(x) for x in groups[pre]),
                             reverse=True)
    return groups, sorted_prefixes

def extract_keywords_from_md(files):
    text = ""
    for f in files:
        if f.endswith(".md"):
            c = open(f,'r',encoding='utf-8').read()
            text += " " + c
    return get_high_info_terms(text)

def display_file_manager_sidebar(groups, sorted_prefixes):
    st.sidebar.title("🎵 Audio & Docs Manager")

    all_md = []
    all_mp3 = []
    for prefix in groups:
        for f in groups[prefix]:
            if f.endswith(".md"):
                all_md.append(f)
            elif f.endswith(".mp3"):
                all_mp3.append(f)

    top_bar = st.sidebar.columns(3)
    with top_bar[0]:
        if st.button("🗑 DelAllMD"):
            for f in all_md:
                os.remove(f)
            st.session_state.should_rerun = True
    with top_bar[1]:
        if st.button("🗑 DelAllMP3"):
            for f in all_mp3:
                os.remove(f)
            st.session_state.should_rerun = True
    with top_bar[2]:
        if st.button("⬇️ ZipAll"):
            z = create_zip_of_files(all_md, all_mp3)
            if z:
                st.sidebar.markdown(get_download_link(z),unsafe_allow_html=True)

    for prefix in sorted_prefixes:
        files = groups[prefix]
        kw = extract_keywords_from_md(files)
        keywords_str = " ".join(kw) if kw else "No Keywords"
        with st.sidebar.expander(f"{prefix} Files ({len(files)}) - KW: {keywords_str}", expanded=True):
            c1,c2 = st.columns(2)
            with c1:
                if st.button("👀ViewGrp", key="view_group_"+prefix):
                    st.session_state.viewing_prefix = prefix
            with c2:
                if st.button("🗑DelGrp", key="del_group_"+prefix):
                    for f in files:
                        os.remove(f)
                    st.success(f"Deleted group {prefix}!")
                    st.session_state.should_rerun = True

            for f in files:
                fname = os.path.basename(f)
                ctime = datetime.fromtimestamp(os.path.getmtime(f)).strftime("%Y-%m-%d %H:%M:%S")
                st.write(f"**{fname}** - {ctime}")

def create_zip_of_files(md_files, mp3_files):
    md_files = [f for f in md_files if os.path.basename(f).lower() != 'readme.md']
    all_files = md_files + mp3_files
    if not all_files:
        return None

    all_content = []
    for f in all_files:
        if f.endswith('.md'):
            with open(f,'r',encoding='utf-8') as file:
                all_content.append(file.read())
        elif f.endswith('.mp3'):
            all_content.append(os.path.basename(f))

    combined_content = " ".join(all_content)
    info_terms = get_high_info_terms(combined_content)

    timestamp = datetime.now().strftime("%y%m_%H%M")
    name_text = '_'.join(term.replace(' ', '-') for term in info_terms[:3])
    zip_name = f"{timestamp}_{name_text}.zip"

    with zipfile.ZipFile(zip_name,'w') as z:
        for f in all_files:
            z.write(f)

    return zip_name

def perform_ai_lookup(q, vocal_summary=True, extended_refs=False, titles_summary=True, full_audio=False):
    """Perform Arxiv search (via your RAG pattern) and generate audio summaries."""
    start = time.time()
    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    # The next lines call your RAG pipeline
    refs = client.predict(q,20,"Semantic Search","mistralai/Mixtral-8x7B-Instruct-v0.1",api_name="/update_with_rag_md")[0]
    r2 = client.predict(q,"mistralai/Mixtral-8x7B-Instruct-v0.1",True,api_name="/ask_llm")

    result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
    st.markdown(result)

    # Audio outputs
    if full_audio:
        complete_text = f"Complete response for query: {q}. {clean_for_speech(r2)} {clean_for_speech(refs)}"
        audio_file_full = speak_with_edge_tts(complete_text)
        if audio_file_full:
            st.write("### 📚 Full Audio")
            play_and_download_audio(audio_file_full)

    if vocal_summary:
        main_text = clean_for_speech(r2)
        if main_text.strip():
            audio_file_main = speak_with_edge_tts(main_text)
            if audio_file_main:
                st.write("### 🎙 Short Audio")
                play_and_download_audio(audio_file_main)

    if extended_refs:
        summaries_text = "Extended references: " + refs.replace('"','')
        summaries_text = clean_for_speech(summaries_text)
        if summaries_text.strip():
            audio_file_refs = speak_with_edge_tts(summaries_text)
            if audio_file_refs:
                st.write("### 📜 Long Refs")
                play_and_download_audio(audio_file_refs)

    if titles_summary:
        titles = []
        for line in refs.split('\n'):
            m = re.search(r"\[([^\]]+)\]", line)
            if m:
                titles.append(m.group(1))
        if titles:
            titles_text = "Titles: " + ", ".join(titles)
            titles_text = clean_for_speech(titles_text)
            if titles_text.strip():
                audio_file_titles = speak_with_edge_tts(titles_text)
                if audio_file_titles:
                    st.write("### 🔖 Titles")
                    play_and_download_audio(audio_file_titles)

    elapsed = time.time()-start
    st.write(f"**Total Elapsed:** {elapsed:.2f} s")

    create_file(q, result, "md")

    return result

def main():
    st.sidebar.markdown("### 🚲BikeAI🏆 ArXiv Voice Research")
    st.session_state['user_name'] = st.selectbox("Current User:", USER_NAMES, index=0)

    # Display saved files in sidebar
    groups, sorted_prefixes = load_files_for_sidebar()
    display_file_manager_sidebar(groups, sorted_prefixes)
    if st.session_state.viewing_prefix and st.session_state.viewing_prefix in groups:
        st.write("---")
        st.write(f"**Viewing Group:** {st.session_state.viewing_prefix}")
        for f in groups[st.session_state.viewing_prefix]:
            fname = os.path.basename(f)
            ext = os.path.splitext(fname)[1].lower().strip('.')
            st.write(f"### {fname}")
            if ext == "md":
                content = open(f,'r',encoding='utf-8').read()
                st.markdown(content)
            elif ext == "mp3":
                st.audio(f)
            else:
                st.markdown(get_download_link(f), unsafe_allow_html=True)
        if st.button("❌ Close"):
            st.session_state.viewing_prefix = None

    if st.button("🗑️ Clear All History in Sidebar"):
        md_files = glob.glob("*.md")
        mp3_files = glob.glob("*.mp3")
        for f in md_files+mp3_files:
            os.remove(f)
        st.success("All history cleared!")
        st.rerun()

    st.title("🎙️ ArXiv Voice Search")

    # Voice component
    mycomponent = components.declare_component("mycomponent", path="mycomponent")
    voice_val = mycomponent(my_input_value="Start speaking...")

    tabs = st.tabs(["🎤 Voice Chat", "💾 History", "⚙️ Settings"])

    with tabs[0]:
        st.subheader("🎤 Voice Chat")
        if voice_val:
            voice_text = voice_val.strip()
            input_changed = (voice_text != st.session_state.get('old_val'))
            if input_changed and voice_text:
                # Save user input
                create_file(st.session_state['user_name'], voice_text, "md")

                # Perform ArXiv search automatically
                with st.spinner("Searching ArXiv..."):
                    # Always do vocal_summary = True, extended_refs=False, titles_summary=True, full_audio=False
                    result = perform_ai_lookup(voice_text, vocal_summary=True, extended_refs=False, titles_summary=True, full_audio=False)

                # Update old_val
                st.session_state['old_val'] = voice_text
                # Clear the text by rerunning
                st.rerun()

        st.write("Speak a query to run an ArXiv search and hear the results.")

    with tabs[1]:
        st.subheader("💾 History")
        # Show all MD files and allow reading them aloud
        md_files = sorted(glob.glob("*.md"), key=os.path.getmtime, reverse=True)
        for i, fpath in enumerate(md_files, start=1):
            fname = os.path.basename(fpath)
            with open(fpath,'r',encoding='utf-8') as ff:
                content = ff.read()
            with st.expander(fname, expanded=False):
                st.write(content)
                if st.button(f"🔊 Read Aloud {fname}", key=f"read_{i}_{fname}"):
                    voice = USER_VOICES.get(st.session_state['user_name'], "en-US-AriaNeural")
                    audio_file = speak_with_edge_tts(content, voice=voice)
                    if audio_file:
                        play_and_download_audio(audio_file)

        if st.button("📜 Read Entire History"):
            all_content = []
            for fpath in sorted(md_files, key=os.path.getmtime):
                with open(fpath,'r',encoding='utf-8') as ff:
                    c = ff.read().strip()
                    if c:
                        all_content.append((fpath, c))
            mp3_files = []
            for (fpath, text) in all_content:
                voice = USER_VOICES.get(st.session_state['user_name'], "en-US-AriaNeural")
                audio_file = speak_with_edge_tts(text, voice=voice)
                if audio_file:
                    mp3_files.append(audio_file)
                    st.write(f"**{os.path.basename(fpath)}:**")
                    play_and_download_audio(audio_file)

            if mp3_files:
                combined_file = f"full_conversation_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp3"
                with open(combined_file, 'wb') as outfile:
                    for f in mp3_files:
                        with open(f, 'rb') as infile:
                            outfile.write(infile.read())
                st.write("**Full Conversation Audio:**")
                play_and_download_audio(combined_file)

    with tabs[2]:
        st.subheader("⚙️ Settings")
        st.write("Currently no additional settings.")

    if st.session_state.should_rerun:
        st.session_state.should_rerun = False
        st.rerun()

if __name__=="__main__":
    main()