app.py

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
from pathlib import Path
from datetime import datetime, timedelta
import edge_tts
import asyncio
import requests
from collections import defaultdict
from audio_recorder_streamlit import audio_recorder
import streamlit.components.v1 as components
from urllib.parse import quote
from xml.etree import ElementTree as ET
from datasets import load_dataset

# 🧠 Initialize session state variables 
SESSION_VARS = {
    'search_history': [],          # Track search history
    'last_voice_input': "",        # Last voice input
    'transcript_history': [],      # Conversation history
    'should_rerun': False,         # Trigger for UI updates
    'search_columns': [],          # Available search columns
    'initial_search_done': False,  # First search flag
    'tts_voice': "en-US-AriaNeural", # Default voice
    'arxiv_last_query': "",        # Last ArXiv search
    'dataset_loaded': False,       # Dataset load status
    'current_page': 0,            # Current data page
    'data_cache': None,           # Data cache
    'dataset_info': None,         # Dataset metadata
    'nps_submitted': False,       # Track if user submitted NPS
    'nps_last_shown': None,       # When NPS was last shown
    'voice_recorder_key': str(datetime.now())  # Unique key for voice recorder
}

# 📊 Constants
ROWS_PER_PAGE = 100
MIN_SEARCH_SCORE = 0.3
EXACT_MATCH_BOOST = 2.0

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

class NPSTracker:
    """🎯 Net Promoter Score Tracker - Measuring happiness in numbers!"""
    
    def __init__(self, log_file="nps_logs.csv"):
        self.log_file = Path(log_file)
        self.initialize_log()
    
    def initialize_log(self):
        """📝 Create log file if it doesn't exist"""
        if not self.log_file.exists():
            df = pd.DataFrame(columns=['timestamp', 'score', 'feedback'])
            df.to_csv(self.log_file, index=False)
    
    def log_response(self, score, feedback=""):
        """🖊️ Log new NPS response"""
        new_entry = pd.DataFrame([{
            'timestamp': datetime.now().isoformat(),
            'score': score,
            'feedback': feedback
        }])
        
        if self.log_file.exists():
            df = pd.read_csv(self.log_file)
            df = pd.concat([df, new_entry], ignore_index=True)
        else:
            df = new_entry
        
        df.to_csv(self.log_file, index=False)
    
    def get_nps_stats(self, days=30):
        """📊 Calculate NPS stats for recent period"""
        if not self.log_file.exists():
            return {
                'nps_score': 0,
                'promoters': 0,
                'passives': 0,
                'detractors': 0,
                'total_responses': 0,
                'recent_feedback': []
            }
        
        df = pd.read_csv(self.log_file)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        
        cutoff = datetime.now() - timedelta(days=days)
        recent_df = df[df['timestamp'] > cutoff]
        
        if len(recent_df) == 0:
            return {
                'nps_score': 0,
                'promoters': 0,
                'passives': 0,
                'detractors': 0,
                'total_responses': 0,
                'recent_feedback': []
            }
        
        total = len(recent_df)
        promoters = len(recent_df[recent_df['score'] >= 9])
        passives = len(recent_df[recent_df['score'].between(7, 8)])
        detractors = len(recent_df[recent_df['score'] <= 6])
        
        nps = ((promoters/total) - (detractors/total)) * 100
        
        recent_feedback = recent_df[recent_df['feedback'].notna()].sort_values(
            'timestamp', ascending=False
        )['feedback'].head(5).tolist()
        
        return {
            'nps_score': round(nps, 1),
            'promoters': promoters,
            'passives': passives,
            'detractors': detractors,
            'total_responses': total,
            'recent_feedback': recent_feedback
        }

def setup_voice_recorder():
    """🎤 Create an in-browser voice recorder component"""
    return components.html(
        """
        <div style="display: flex; flex-direction: column; align-items: center; gap: 10px;">
            <button id="startButton" 
                    style="padding: 10px 20px; background: #ff4b4b; color: white; border: none; border-radius: 5px; cursor: pointer">
                Start Recording
            </button>
            <button id="stopButton" 
                    style="padding: 10px 20px; background: #4b4bff; color: white; border: none; border-radius: 5px; cursor: pointer"
                    disabled>
                Stop Recording
            </button>
            <audio id="audioPlayback" controls style="display: none;"></audio>
            <div id="statusText" style="color: #666;">Ready to record...</div>
        </div>
        <script>
        let mediaRecorder;
        let audioChunks = [];
        
        document.getElementById('startButton').onclick = async () => {
            try {
                const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
                mediaRecorder = new MediaRecorder(stream);
                
                mediaRecorder.ondataavailable = (e) => {
                    audioChunks.push(e.data);
                };
                
                mediaRecorder.onstop = () => {
                    const audioBlob = new Blob(audioChunks, { type: 'audio/wav' });
                    const audioUrl = URL.createObjectURL(audioBlob);
                    document.getElementById('audioPlayback').src = audioUrl;
                    document.getElementById('audioPlayback').style.display = 'block';
                    
                    // Send to Python
                    const reader = new FileReader();
                    reader.readAsDataURL(audioBlob);
                    reader.onloadend = () => {
                        window.parent.postMessage({
                            type: 'voiceData',
                            data: reader.result
                        }, '*');
                    };
                };
                
                mediaRecorder.start();
                document.getElementById('startButton').disabled = true;
                document.getElementById('stopButton').disabled = false;
                document.getElementById('statusText').textContent = 'Recording...';
                
            } catch (err) {
                console.error('Error:', err);
                document.getElementById('statusText').textContent = 'Error: ' + err.message;
            }
        };
        
        document.getElementById('stopButton').onclick = () => {
            mediaRecorder.stop();
            document.getElementById('startButton').disabled = false;
            document.getElementById('stopButton').disabled = true;
            document.getElementById('statusText').textContent = 'Recording complete!';
        };
        </script>
        """,
        height=200,
    )

def render_nps_sidebar():
    """🎨 Show NPS metrics in sidebar"""
    tracker = NPSTracker()
    stats = tracker.get_nps_stats()
    
    st.sidebar.markdown("### 📊 User Satisfaction Metrics")
    
    score_color = (
        "🟢" if stats['nps_score'] >= 50 else 
        "🟡" if stats['nps_score'] >= 0 else 
        "🔴"
    )
    st.sidebar.metric(
        "Net Promoter Score",
        f"{score_color} {stats['nps_score']}"
    )
    
    st.sidebar.markdown("#### Response Breakdown")
    col1, col2, col3 = st.sidebar.columns(3)
    with col1:
        st.metric("😃", stats['promoters'])
    with col2:
        st.metric("😐", stats['passives'])
    with col3:
        st.metric("😕", stats['detractors'])
    
    if stats['recent_feedback']:
        st.sidebar.markdown("#### Recent Feedback")
        for feedback in stats['recent_feedback']:
            st.sidebar.info(feedback[:100] + "..." if len(feedback) > 100 else feedback)

def render_nps_survey():
    """🎯 Show NPS survey form"""
    tracker = NPSTracker()
    
    st.markdown("### 📝 Your Feedback Matters!")
    score = st.slider(
        "How likely are you to recommend this search tool to others?",
        0, 10, 
        help="0 = Not likely at all, 10 = Extremely likely"
    )
    
    feedback = st.text_area("Additional feedback (optional)")
    
    if st.button("Submit Feedback", key="nps_submit"):
        tracker.log_response(score, feedback)
        st.session_state['nps_submitted'] = True
        st.success("Thank you for your feedback! 🙏")
        st.experimental_rerun()

[... Rest of your existing code for search functionality ...]

def main():
    st.title("🎥 Smart Video Search with Voice & Feedback")
    
    # Initialize search
    search = VideoSearch()
    
    # Add NPS metrics to sidebar
    with st.sidebar:
        render_nps_sidebar()
        
        # Show survey periodically
        current_time = datetime.now()
        if (not st.session_state.get('nps_submitted') and 
            (not st.session_state.get('nps_last_shown') or 
             current_time - st.session_state['nps_last_shown'] > timedelta(hours=24))):
            with st.expander("📝 Quick Feedback", expanded=True):
                render_nps_survey()
                st.session_state['nps_last_shown'] = current_time
    
    # Create main tabs
    tab1, tab2, tab3, tab4 = st.tabs([
        "🔍 Search", "🎙️ Voice Input", "📚 ArXiv", "📂 Files"
    ])
    
    # Search Tab
    with tab1:
        st.subheader("Search Videos")
        col1, col2 = st.columns([3, 1])
        with col1:
            query = st.text_input("Enter search query:", 
                                value="" if st.session_state['initial_search_done'] else "aliens")
        with col2:
            search_column = st.selectbox("Search in:", 
                                       ["All Fields"] + st.session_state['search_columns'])
        
        col3, col4 = st.columns(2)
        with col3:
            num_results = st.slider("Max results:", 1, 100, 20)
        with col4:
            search_button = st.button("🔍 Search")
        
        if (search_button or not st.session_state['initial_search_done']) and query:
            st.session_state['initial_search_done'] = True
            selected_column = None if search_column == "All Fields" else search_column
            
            with st.spinner("Searching..."):
                results = search.search(query, selected_column, num_results)
            
            if results:
                st.session_state['search_history'].append({
                    'query': query,
                    'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                    'results': results[:5]
                })
                
                st.write(f"Found {len(results)} results:")
                for i, result in enumerate(results, 1):
                    with st.expander(f"Result {i}", expanded=(i==1)):
                        render_result(result)
            else:
                st.warning("No matching results found.")
    
    # Voice Input Tab
    with tab2:
        st.subheader("Voice Search")
        st.write("🎙️ Record your query:")
        
        voice_recorder = setup_voice_recorder()
        
        if 'voice_data' in st.session_state:
            with st.spinner("Processing voice..."):
                voice_query = transcribe_audio(st.session_state['voice_data'])
                st.markdown("**Transcribed Text:**")
                st.write(voice_query)
                
                if st.button("🔍 Search with Voice"):
                    results = search.search(voice_query, None, 20)
                    for i, result in enumerate(results, 1):
                        with st.expander(f"Result {i}", expanded=(i==1)):
                            render_result(result)
    
    # ArXiv Tab
    with tab3:
        st.subheader("ArXiv Search")
        arxiv_query = st.text_input("Search ArXiv:", value=st.session_state['arxiv_last_query'])
        vocal_summary = st.checkbox("🎙 Quick Audio Summary", value=True)
        titles_summary = st.checkbox("🔖 Titles Only", value=True)
        full_audio = st.checkbox("📚 Full Audio Summary", value=False)
        
        if st.button("🔍 Search ArXiv"):
            st.session_state['arxiv_last_query'] = arxiv_query
            perform_arxiv_lookup(arxiv_query, vocal_summary, titles_summary, full_audio)
    
    # File Manager Tab
    with tab4:
        show_file_manager()

if __name__ == "__main__":
    main()