import streamlit as st
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import torch
import json
import os
import glob
import random
from pathlib import Path
from datetime import datetime, timedelta
import edge_tts
import asyncio
import requests
from collections import defaultdict
import streamlit.components.v1 as components
from urllib.parse import quote
from xml.etree import ElementTree as ET
from datasets import load_dataset
import base64
import re

# -------------------- Configuration & Constants --------------------
USER_NAMES = [
    "Alex", "Jordan", "Taylor", "Morgan", "Rowan", "Avery", "Riley", "Quinn",
    "Casey", "Jesse", "Reese", "Skyler", "Ellis", "Devon", "Aubrey", "Kendall",
    "Parker", "Dakota", "Sage", "Finley"
]

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"
]

ROWS_PER_PAGE = 100
MIN_SEARCH_SCORE = 0.3
EXACT_MATCH_BOOST = 2.0
SAVED_INPUTS_DIR = "saved_inputs"
os.makedirs(SAVED_INPUTS_DIR, exist_ok=True)

SESSION_VARS = {
    'search_history': [],
    'last_voice_input': "",
    'transcript_history': [],
    'should_rerun': False,
    'search_columns': [],
    'initial_search_done': False,
    'tts_voice': "en-US-AriaNeural",
    'arxiv_last_query': "",
    'dataset_loaded': False,
    'current_page': 0,
    'data_cache': None,
    'dataset_info': None,
    'nps_submitted': False,
    'nps_last_shown': None,
    'old_val': None,
    'voice_text': None,
    'user_name': random.choice(USER_NAMES),
    'max_items': 100,
    'global_voice': "en-US-AriaNeural"  # Default global voice
}

for var, default in SESSION_VARS.items():
    if var not in st.session_state:
        st.session_state[var] = default

@st.cache_resource
def get_model():
    return SentenceTransformer('all-MiniLM-L6-v2')

def create_voice_component():
    mycomponent = components.declare_component(
        "mycomponent",
        path="mycomponent"
    )
    return mycomponent

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 = f"speech_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp3"
    await communicate.save(out_fn)
    return out_fn

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

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 generate_filename(prefix, text):
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    safe_text = re.sub(r'[^\w\s-]', '', text[:50]).strip().lower()
    safe_text = re.sub(r'[-\s]+', '-', safe_text)
    return f"{prefix}_{timestamp}_{safe_text}.md"

def save_input_as_md(user_name, text, prefix="input"):
    if not text.strip():
        return
    fn = generate_filename(prefix, text)
    full_path = os.path.join(SAVED_INPUTS_DIR, fn)
    with open(full_path, 'w', encoding='utf-8') as f:
        f.write(f"# User: {user_name}\n")
        f.write(f"**Timestamp:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
        f.write(text)
    return full_path

def save_response_as_md(user_name, text, prefix="response"):
    if not text.strip():
        return
    fn = generate_filename(prefix, text)
    full_path = os.path.join(SAVED_INPUTS_DIR, fn)
    with open(full_path, 'w', encoding='utf-8') as f:
        f.write(f"# User: {user_name}\n")
        f.write(f"**Timestamp:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
        f.write(text)
    return full_path

def list_saved_inputs():
    files = sorted(glob.glob(os.path.join(SAVED_INPUTS_DIR, "*.md")))
    return files

def parse_md_file(fpath):
    # Extract user and text from md
    user_line = ""
    ts_line = ""
    content_lines = []
    with open(fpath, 'r', encoding='utf-8') as f:
        lines = f.readlines()
    for line in lines:
        if line.startswith("# User:"):
            user_line = line.replace("# User:", "").strip()
        elif line.startswith("**Timestamp:**"):
            ts_line = line.replace("**Timestamp:**", "").strip()
        else:
            content_lines.append(line.strip())
    content = "\n".join(content_lines).strip()
    return user_line, ts_line, content

def fetch_dataset_info(dataset_id, token):
    info_url = f"https://huggingface.co/api/datasets/{dataset_id}"
    try:
        response = requests.get(info_url, timeout=30)
        if response.status_code == 200:
            return response.json()
    except Exception:
        pass
    return None

@st.cache_data
def get_dataset_info(dataset_id, token):
    try:
        dataset = load_dataset(dataset_id, token=token, streaming=True)
        return dataset['train'].info
    except:
        return None

@st.cache_data
def load_dataset_page(dataset_id, token, page, rows_per_page):
    try:
        start_idx = page * rows_per_page
        end_idx = start_idx + rows_per_page
        dataset = load_dataset(
            dataset_id,
            token=token,
            streaming=False,
            split=f'train[{start_idx}:{end_idx}]'
        )
        return pd.DataFrame(dataset)
    except:
        return pd.DataFrame()

class FastDatasetSearcher:
    def __init__(self, dataset_id="tomg-group-umd/cinepile"):
        self.dataset_id = dataset_id
        self.text_model = get_model()
        self.token = os.environ.get('DATASET_KEY')

    def load_page(self, page=0):
        return load_dataset_page(self.dataset_id, self.token, page, ROWS_PER_PAGE)

    def quick_search(self, query, df):
        if df.empty or not query.strip():
            return df
        
        try:
            searchable_cols = []
            if len(df) > 0:
                for col in df.columns:
                    sample_val = df[col].iloc[0] 
                    if not isinstance(sample_val, (np.ndarray, bytes)):
                        searchable_cols.append(col)
            
            query_lower = query.lower()
            query_terms = set(query_lower.split())
            query_embedding = self.text_model.encode([query], show_progress_bar=False)[0]
            
            scores = []
            matched_any = []
            
            for _, row in df.iterrows():
                text_parts = []
                row_matched = False
                exact_match = False
                priority_fields = ['description', 'matched_text']
                other_fields = [col for col in searchable_cols if col not in priority_fields]
                
                for col in priority_fields:
                    if col in row:
                        val = row[col]
                        if val is not None:
                            val_str = str(val).lower()
                            if query_lower in val_str.split():
                                exact_match = True
                            if any(term in val_str.split() for term in query_terms):
                                row_matched = True
                            text_parts.append(str(val))
                
                for col in other_fields:
                    val = row[col]
                    if val is not None:
                        val_str = str(val).lower()
                        if query_lower in val_str.split():
                            exact_match = True
                        if any(term in val_str.split() for term in query_terms):
                            row_matched = True
                        text_parts.append(str(val))
                
                text = ' '.join(text_parts)
                if text.strip():
                    text_tokens = set(text.lower().split())
                    matching_terms = query_terms.intersection(text_tokens)
                    keyword_score = len(matching_terms) / len(query_terms) if len(query_terms) > 0 else 0.0
                    
                    text_embedding = self.text_model.encode([text], show_progress_bar=False)[0]
                    semantic_score = float(cosine_similarity([query_embedding], [text_embedding])[0][0])
                    
                    combined_score = 0.7 * keyword_score + 0.3 * semantic_score
                    
                    if exact_match:
                        combined_score *= EXACT_MATCH_BOOST
                    elif row_matched:
                        combined_score *= 1.2
                else:
                    combined_score = 0.0
                    row_matched = False
                
                scores.append(combined_score)
                matched_any.append(row_matched)
            
            results_df = df.copy()
            results_df['score'] = scores
            results_df['matched'] = matched_any
            
            filtered_df = results_df[
                (results_df['matched']) |
                (results_df['score'] > MIN_SEARCH_SCORE)
            ]
            
            return filtered_df.sort_values('score', ascending=False)
        except:
            return df

def play_text(text):
    voice = st.session_state.get('global_voice', "en-US-AriaNeural")
    audio_file = speak_with_edge_tts(text, voice=voice)
    if audio_file:
        play_and_download_audio(audio_file)

def arxiv_search(query, max_results=3):
    # Simple arXiv search using RSS (for demonstration)
    # In production, use official arXiv API or a library.
    base_url = "http://export.arxiv.org/api/query"
    params = {
        'search_query': query.replace(' ', '+'),
        'start': 0,
        'max_results': max_results
    }
    response = requests.get(base_url, params=params, timeout=30)
    if response.status_code == 200:
        root = ET.fromstring(response.text)
        ns = {"a": "http://www.w3.org/2005/Atom"}
        entries = root.findall('a:entry', ns)
        results = []
        for entry in entries:
            title = entry.find('a:title', ns).text.strip()
            summary = entry.find('a:summary', ns).text.strip()
            # Just truncating summary for demo
            summary_short = summary[:300] + "..."
            results.append((title, summary_short))
        return results
    return []

def summarize_arxiv_results(results):
    # Just combine titles and short summaries
    lines = []
    for i, (title, summary) in enumerate(results, 1):
        lines.append(f"Result {i}: {title}\n{summary}\n")
    return "\n\n".join(lines)

def main():
    st.title("🎙️ Voice Chat & Search")

    # Sidebar
    with st.sidebar:
        # Editable user name
        st.session_state['user_name'] = st.text_input("Current User:", value=st.session_state['user_name'])
        
        # Global voice selection
        st.session_state['global_voice'] = st.selectbox("Select Global Voice:", ENGLISH_VOICES, index=0)
        
        st.session_state['max_items'] = st.number_input("Max Items per search iteration:", min_value=1, max_value=1000, value=st.session_state['max_items'])
        
        st.subheader("📝 Saved Inputs & Responses")
        saved_files = list_saved_inputs()
        for fpath in saved_files:
            user, ts, content = parse_md_file(fpath)
            fname = os.path.basename(fpath)
            st.write(f"- {fname} (User: {user})")

    # Create voice component for input
    voice_component = create_voice_component()
    voice_val = voice_component(my_input_value="Start speaking...")

    # Tabs: Voice Chat History, Arxiv Search, Dataset Search, Settings
    tab1, tab2, tab3, tab4 = st.tabs(["🗣️ Voice Chat History", "📚 ArXiv Search", "📊 Dataset Search", "⚙️ Settings"])

    # ------------------ Voice Chat History -------------------------
    with tab1:
        st.subheader("Voice Chat History")
        # List saved inputs and responses and allow playing them
        files = list_saved_inputs()
        for fpath in reversed(files):
            user, ts, content = parse_md_file(fpath)
            with st.expander(f"{ts} - {user}", expanded=False):
                st.write(content)
                if st.button("🔊 Read Aloud", key=f"read_{fpath}"):
                    play_text(content)

    # ------------------ ArXiv Search -------------------------
    with tab2:
        st.subheader("ArXiv Search")
        # If we have a voice_val and autorun with ArXiv chosen:
        edited_input = st.text_area("Enter or Edit Search Query:", value=(voice_val.strip() if voice_val else ""), height=100)
        autorun = st.checkbox("⚡ Auto-Run", value=True)
        run_arxiv = st.button("🔍 ArXiv Search")

        input_changed = (edited_input != st.session_state.get('old_val'))
        if autorun and input_changed and edited_input.strip():
            st.session_state['old_val'] = edited_input
            # Save user input
            save_input_as_md(st.session_state['user_name'], edited_input, prefix="input")
            with st.spinner("Searching ArXiv..."):
                results = arxiv_search(edited_input)
                if results:
                    summary = summarize_arxiv_results(results)
                    # Save response
                    save_response_as_md(st.session_state['user_name'], summary, prefix="response")
                    st.write(summary)
                    # Autoplay TTS
                    play_text(summary)
                else:
                    st.warning("No results found on ArXiv.")

        if run_arxiv and edited_input.strip():
            # Manual trigger
            save_input_as_md(st.session_state['user_name'], edited_input, prefix="input")
            with st.spinner("Searching ArXiv..."):
                results = arxiv_search(edited_input)
                if results:
                    summary = summarize_arxiv_results(results)
                    save_response_as_md(st.session_state['user_name'], summary, prefix="response")
                    st.write(summary)
                    play_text(summary)
                else:
                    st.warning("No results found on ArXiv.")

    # ------------------ Dataset Search -------------------------
    with tab3:
        st.subheader("Dataset Search")
        search = FastDatasetSearcher()
        query = st.text_input("Enter dataset search query:")
        run_ds_search = st.button("Search Dataset")
        num_results = st.slider("Max results:", 1, 100, 20)
        
        if run_ds_search and query.strip():
            with st.spinner("Searching dataset..."):
                df = search.load_page()
                results = search.quick_search(query, df)
                if len(results) > 0:
                    st.write(f"Found {len(results)} results:")
                    shown = 0
                    for i, (_, result) in enumerate(results.iterrows(), 1):
                        if shown >= num_results:
                            break
                        with st.expander(f"Result {i}", expanded=(i==1)):
                            # Just print result keys/values here
                            for k, v in result.items():
                                if k not in ['score', 'matched']:
                                    st.write(f"**{k}:** {v}")
                        shown += 1
                else:
                    st.warning("No matching results found.")

    # ------------------ Settings Tab -------------------------
    with tab4:
        st.subheader("Settings")
        st.write("Adjust voice and search parameters in the sidebar.")
        if st.button("🗑️ Clear Search History"):
            st.session_state['search_history'] = []
            # Optionally delete files:
            # for fpath in list_saved_inputs():
            #     os.remove(fpath)
            st.success("Search history cleared!")

if __name__ == "__main__":
    main()