Delete app.py

app.py

@@ -1,1240 +0,0 @@
-import streamlit as st
-import google.generativeai as genai
-from PyPDF2 import PdfReader
-import os
-import re
-import json
-import pickle
-import hashlib
-from datetime import datetime
-from pathlib import Path
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_huggingface.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.schema import Document
-import tempfile
-import warnings
-import numpy as np
-import shutil
-import time
-warnings.filterwarnings('ignore')
-
-# Configure page
-st.set_page_config(
-    page_title="Ashok 2.0 - AI Problem Solving Assistant",
-    page_icon="🧠",
-    layout="centered",
-    initial_sidebar_state="collapsed"
-)
-
-# World-class minimal UI styling
-st.markdown("""
-<style>
-    /* Import premium font */
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
-    
-    /* Global Reset & Base */
-    .stApp {
-        background: linear-gradient(135deg, #f8fafc 0%, #f1f5f9 100%);
-        font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
-    }
-    
-    .main .block-container {
-        padding-top: 2rem;
-        padding-bottom: 2rem;
-        max-width: 800px;
-    }
-    
-    /* Hide Streamlit UI elements */
-    #MainMenu {visibility: hidden;}
-    footer {visibility: hidden;}
-    header {visibility: hidden;}
-    .stDeployButton {display: none;}
-    
-    /* Main Heading */
-    .main-title {
-        font-size: 4rem;
-        font-weight: 700;
-        text-align: center;
-        background: linear-gradient(135deg, #1e40af 0%, #3b82f6 50%, #60a5fa 100%);
-        -webkit-background-clip: text;
-        -webkit-text-fill-color: transparent;
-        background-clip: text;
-        margin: 2rem 0 3rem 0;
-        letter-spacing: -0.02em;
-        line-height: 1.1;
-    }
-    
-    /* API Key Setup Container */
-    .api-setup-container {
-        background: rgba(255, 255, 255, 0.9);
-        backdrop-filter: blur(10px);
-        border: 1px solid rgba(229, 231, 235, 0.6);
-        border-radius: 24px;
-        padding: 3rem;
-        margin: 2rem auto;
-        max-width: 500px;
-        box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.05), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
-        text-align: center;
-    }
-    
-    .api-setup-title {
-        font-size: 1.5rem;
-        font-weight: 600;
-        color: #1f2937;
-        margin-bottom: 1rem;
-    }
-    
-    .api-setup-subtitle {
-        color: #6b7280;
-        margin-bottom: 2rem;
-        line-height: 1.6;
-    }
-    
-    /* API Key Input Styling */
-    .stTextInput > div > div > input {
-        background: rgba(255, 255, 255, 0.8);
-        border: 2px solid rgba(229, 231, 235, 0.6);
-        border-radius: 16px;
-        padding: 1rem 1.5rem;
-        font-size: 1rem;
-        transition: all 0.3s ease;
-        backdrop-filter: blur(5px);
-    }
-    
-    .stTextInput > div > div > input:focus {
-        border-color: #3b82f6;
-        box-shadow: 0 0 0 4px rgba(59, 130, 246, 0.1);
-        outline: none;
-        background: rgba(255, 255, 255, 0.95);
-    }
-    
-    .stTextInput label {
-        font-weight: 500;
-        color: #374151;
-        margin-bottom: 0.5rem;
-    }
-    
-    /* Chat Interface */
-    .chat-container {
-        background: rgba(255, 255, 255, 0.7);
-        backdrop-filter: blur(10px);
-        border: 1px solid rgba(229, 231, 235, 0.4);
-        border-radius: 24px;
-        padding: 2rem;
-        margin: 2rem 0;
-        box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.03), 0 4px 6px -2px rgba(0, 0, 0, 0.02);
-    }
-    
-    /* Chat Messages */
-    .stChatMessage {
-        background: rgba(255, 255, 255, 0.9) !important;
-        border: 1px solid rgba(0, 0, 0, 0.08) !important;
-        border-radius: 16px !important;
-        margin-bottom: 1rem !important;
-        padding: 1.5rem !important;
-        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.02) !important;
-        backdrop-filter: blur(5px) !important;
-    }
-    
-    .stChatMessage[data-testid="chat-message-user"] {
-        background: rgba(248, 250, 252, 0.9) !important;
-        border-left: 3px solid #3b82f6 !important;
-    }
-    
-    .stChatMessage[data-testid="chat-message-assistant"] {
-        background: rgba(255, 255, 255, 0.9) !important;
-        border-left: 3px solid #10b981 !important;
-    }
-    
-    .stChatMessage .stMarkdown {
-        color: #1f2937 !important;
-        line-height: 1.6;
-    }
-    
-    .stChatMessage .stMarkdown p {
-        color: #1f2937 !important;
-        margin-bottom: 0.5rem;
-    }
-    
-    /* Chat Input */
-    .stChatInput > div {
-        background: rgba(255, 255, 255, 0.9);
-        border: 1px solid rgba(229, 231, 235, 0.6);
-        border-radius: 20px;
-        backdrop-filter: blur(10px);
-    }
-    
-    .stChatInput input {
-        color: #1f2937 !important;
-        background: transparent !important;
-        border: none !important;
-        padding: 1rem 1.5rem !important;
-    }
-    
-    .stChatInput input::placeholder {
-        color: #9ca3af !important;
-    }
-    
-    /* Quick Actions */
-    .quick-actions-container {
-        background: rgba(255, 255, 255, 0.6);
-        backdrop-filter: blur(10px);
-        border: 1px solid rgba(229, 231, 235, 0.4);
-        border-radius: 20px;
-        padding: 2rem;
-        margin: 2rem 0;
-    }
-    
-    .quick-actions-title {
-        font-size: 1.25rem;
-        font-weight: 600;
-        color: #1f2937;
-        text-align: center;
-        margin-bottom: 1.5rem;
-    }
-    
-    .quick-action-grid {
-        display: grid;
-        grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
-        gap: 1rem;
-        margin-top: 1rem;
-    }
-    
-    .quick-action-item {
-        background: rgba(255, 255, 255, 0.8);
-        border: 1px solid rgba(229, 231, 235, 0.5);
-        border-radius: 16px;
-        padding: 1.5rem;
-        cursor: pointer;
-        transition: all 0.3s ease;
-        text-align: left;
-        backdrop-filter: blur(5px);
-    }
-    
-    .quick-action-item:hover {
-        background: rgba(248, 250, 252, 0.9);
-        border-color: #3b82f6;
-        transform: translateY(-2px);
-        box-shadow: 0 8px 25px rgba(59, 130, 246, 0.1);
-    }
-    
-    .quick-action-icon {
-        font-size: 1.5rem;
-        margin-bottom: 0.5rem;
-    }
-    
-    .quick-action-title {
-        font-weight: 600;
-        color: #1f2937;
-        margin-bottom: 0.5rem;
-        font-size: 1rem;
-    }
-    
-    .quick-action-desc {
-        color: #6b7280;
-        font-size: 0.9rem;
-        line-height: 1.5;
-    }
-    
-    /* Control Panel */
-    .control-panel {
-        display: flex;
-        justify-content: center;
-        gap: 1rem;
-        margin: 2rem 0;
-    }
-    
-    .control-button {
-        background: rgba(255, 255, 255, 0.8);
-        border: 1px solid rgba(229, 231, 235, 0.6);
-        border-radius: 12px;
-        padding: 0.75rem 1.5rem;
-        color: #374151;
-        font-weight: 500;
-        cursor: pointer;
-        transition: all 0.3s ease;
-        backdrop-filter: blur(5px);
-    }
-    
-    .control-button:hover {
-        background: rgba(248, 250, 252, 0.9);
-        border-color: #3b82f6;
-        color: #1f2937;
-        transform: translateY(-1px);
-    }
-    
-    /* Status Indicators */
-    .status-success {
-        background: linear-gradient(135deg, rgba(16, 185, 129, 0.1) 0%, rgba(5, 150, 105, 0.1) 100%);
-        border: 1px solid rgba(16, 185, 129, 0.2);
-        border-radius: 16px;
-        padding: 1rem 1.5rem;
-        margin: 1rem 0;
-        color: #065f46;
-        font-weight: 500;
-        text-align: center;
-        backdrop-filter: blur(5px);
-    }
-    
-    .status-warning {
-        background: linear-gradient(135deg, rgba(245, 158, 11, 0.1) 0%, rgba(217, 119, 6, 0.1) 100%);
-        border: 1px solid rgba(245, 158, 11, 0.2);
-        border-radius: 16px;
-        padding: 1rem 1.5rem;
-        margin: 1rem 0;
-        color: #92400e;
-        font-weight: 500;
-        text-align: center;
-        backdrop-filter: blur(5px);
-    }
-    
-    /* Learning Indicator */
-    .learning-indicator {
-        background: linear-gradient(135deg, rgba(16, 185, 129, 0.9) 0%, rgba(5, 150, 105, 0.9) 100%);
-        color: white;
-        padding: 1rem 1.5rem;
-        border-radius: 16px;
-        margin: 1rem 0;
-        font-weight: 500;
-        text-align: center;
-        box-shadow: 0 4px 6px rgba(16, 185, 129, 0.2);
-        backdrop-filter: blur(10px);
-    }
-    
-    /* Typing Indicator */
-    .typing-indicator {
-        background: rgba(248, 250, 252, 0.9);
-        border: 1px solid rgba(229, 231, 235, 0.6);
-        border-radius: 16px;
-        padding: 1rem 1.5rem;
-        margin: 1rem 0;
-        display: flex;
-        align-items: center;
-        gap: 0.75rem;
-        color: #6b7280;
-        backdrop-filter: blur(5px);
-    }
-    
-    .typing-dots {
-        display: flex;
-        gap: 4px;
-    }
-    
-    .typing-dot {
-        width: 6px;
-        height: 6px;
-        border-radius: 50%;
-        background-color: #9ca3af;
-        animation: typing 1.4s infinite ease-in-out;
-    }
-    
-    .typing-dot:nth-child(1) { animation-delay: -0.32s; }
-    .typing-dot:nth-child(2) { animation-delay: -0.16s; }
-    
-    @keyframes typing {
-        0%, 80%, 100% { transform: scale(0.8); opacity: 0.4; }
-        40% { transform: scale(1); opacity: 1; }
-    }
-    
-    /* Button Styling */
-    .stButton > button {
-        background: linear-gradient(135deg, rgba(59, 130, 246, 0.9) 0%, rgba(37, 99, 235, 0.9) 100%);
-        color: white !important;
-        border: none;
-        border-radius: 12px;
-        padding: 0.75rem 2rem;
-        font-weight: 500;
-        transition: all 0.3s ease;
-        backdrop-filter: blur(10px);
-        box-shadow: 0 4px 6px rgba(59, 130, 246, 0.2);
-    }
-    
-    .stButton > button:hover {
-        background: linear-gradient(135deg, rgba(37, 99, 235, 0.9) 0%, rgba(29, 78, 216, 0.9) 100%);
-        transform: translateY(-1px);
-        box-shadow: 0 6px 12px rgba(59, 130, 246, 0.25);
-    }
-    
-    /* API Key Guide */
-    .api-guide {
-        background: rgba(255, 255, 255, 0.6);
-        border: 1px solid rgba(229, 231, 235, 0.4);
-        border-radius: 16px;
-        padding: 1.5rem;
-        margin: 1.5rem 0;
-        backdrop-filter: blur(5px);
-    }
-    
-    .api-guide-title {
-        font-weight: 600;
-        color: #1f2937;
-        margin-bottom: 1rem;
-    }
-    
-    .api-guide-text {
-        color: #6b7280;
-        line-height: 1.6;
-        margin-bottom: 0.5rem;
-    }
-    
-    .api-link {
-        color: #3b82f6;
-        text-decoration: none;
-        font-weight: 500;
-        transition: color 0.2s ease;
-    }
-    
-    .api-link:hover {
-        color: #1d4ed8;
-    }
-    
-    /* Responsive Design */
-    @media (max-width: 768px) {
-        .main-title {
-            font-size: 3rem;
-        }
-        
-        .api-setup-container {
-            padding: 2rem;
-            margin: 1rem;
-        }
-        
-        .quick-action-grid {
-            grid-template-columns: 1fr;
-        }
-        
-        .control-panel {
-            flex-direction: column;
-            align-items: center;
-        }
-    }
-</style>
-""", unsafe_allow_html=True)
-
-class PersistentHFKnowledgeBase:
-    """Persistent Knowledge Base with silent initialization"""
-    
-    def __init__(self):
-        # Create persistent directories
-        self.data_dir = Path("./persistent_data")
-        self.data_dir.mkdir(exist_ok=True)
-        
-        # File paths for persistence
-        self.vectorstore_path = self.data_dir / "vectorstore"
-        self.metadata_path = self.data_dir / "metadata.json"
-        self.conversations_path = self.data_dir / "conversations.json"
-        self.stats_path = self.data_dir / "stats.json"
-        self.init_flag_path = self.data_dir / "initialized.flag"
-        
-        # Initialize components
-        self.embeddings = None
-        self.vectorstore = None
-        self.metadata = {}
-        self.conversations = []
-        self.stats = {}
-        
-        # Initialize system silently
-        self.initialize_system()
-    
-    def initialize_system(self):
-        """Initialize the complete system with silent book processing"""
-        try:
-            # Initialize embeddings first
-            self.init_embeddings()
-            
-            # Check if system was already initialized
-            if self.init_flag_path.exists():
-                # Load existing knowledge base silently
-                self.load_existing_knowledge()
-            else:
-                # First time initialization - do it silently
-                self.first_time_initialization()
-                    
-        except Exception as e:
-            # Silent fallback initialization
-            self.fallback_initialization()
-    
-    def init_embeddings(self):
-        """Initialize embeddings model with silent caching"""
-        if self.embeddings is None:
-            try:
-                cache_dir = self.data_dir / "embeddings_cache"
-                cache_dir.mkdir(exist_ok=True)
-                
-                self.embeddings = HuggingFaceEmbeddings(
-                    model_name="sentence-transformers/all-MiniLM-L6-v2",
-                    cache_folder=str(cache_dir)
-                )
-            except Exception as e:
-                return False
-        return True
-    
-    def first_time_initialization(self):
-        """Complete first-time setup with silent book processing"""
-        try:
-            # Initialize metadata
-            self.metadata = {
-                'version': '2.0-minimal',
-                'created_at': datetime.now().isoformat(),
-                'last_updated': datetime.now().isoformat(),
-                'total_documents': 0,
-                'book_processed': False,
-                'book_info': {},
-                'initialization_complete': False
-            }
-            
-            # Initialize stats
-            self.stats = {
-                'total_queries': 0,
-                'learning_sessions': 0,
-                'book_chunks': 0,
-                'conversation_chunks': 0,
-                'silly_questions_blocked': 0
-            }
-            
-            # Initialize conversations
-            self.conversations = []
-            
-            # Process book if available (silently)
-            book_processed = self.process_startup_book()
-            
-            # Create default knowledge if no book
-            if not book_processed:
-                self.create_default_knowledge()
-            
-            # Mark as initialized
-            self.metadata['initialization_complete'] = True
-            self.save_all_data()
-            
-            # Create initialization flag
-            with open(self.init_flag_path, 'w') as f:
-                f.write(f"Initialized on {datetime.now().isoformat()}")
-            
-        except Exception as e:
-            self.fallback_initialization()
-    
-    def process_startup_book(self):
-        """Process the book included with the deployment (silently)"""
-        book_paths = [
-            "book.pdf",
-            "problem_solving_book.pdf", 
-            "default_book.pdf",
-            "ashok_book.pdf"
-        ]
-        
-        for book_path in book_paths:
-            if Path(book_path).exists():
-                success = self.process_book_file(Path(book_path))
-                if success:
-                    return True
-        
-        return False
-    
-    def process_book_file(self, book_path):
-        """Process a specific book file (silently)"""
-        try:
-            # Extract text from PDF
-            reader = PdfReader(str(book_path))
-            page_texts = []
-            
-            for page_num, page in enumerate(reader.pages, 1):
-                page_text = page.extract_text()
-                if page_text.strip():
-                    page_texts.append({
-                        'page': page_num,
-                        'text': page_text,
-                        'word_count': len(page_text.split())
-                    })
-            
-            if not page_texts:
-                return False
-            
-            # Create book info
-            book_info = {
-                'title': book_path.name,
-                'path': str(book_path),
-                'pages': len(page_texts),
-                'processed_at': datetime.now().isoformat(),
-                'source': 'deployment_book'
-            }
-            
-            # Process content
-            success, message = self.process_book_content("", page_texts, book_info)
-            
-            return success
-                
-        except Exception as e:
-            return False
-    
-    def create_default_knowledge(self):
-        """Create comprehensive default knowledge base"""
-        default_knowledge = [
-            {
-                "content": "Problem-solving methodology: 1) Problem Definition - Clearly articulate what needs to be solved, 2) Information Gathering - Collect relevant data and context, 3) Root Cause Analysis - Identify underlying causes, not just symptoms, 4) Solution Generation - Brainstorm multiple potential solutions, 5) Solution Evaluation - Assess feasibility, impact, and resources, 6) Implementation Planning - Create detailed action steps, 7) Execution and Monitoring - Implement and track progress, 8) Review and Learning - Evaluate outcomes and extract lessons.",
-                "metadata": {
-                    "source": "core_knowledge",
-                    "type": "framework",
-                    "topic": "problem_solving_process",
-                    "chapter": "Core Problem-Solving Framework"
-                }
-            },
-            {
-                "content": "Decision-making best practices: Use the DECIDE framework - D: Define the problem clearly, E: Establish criteria for solutions, C: Consider alternatives systematically, I: Identify best alternatives using criteria, D: Develop and implement action plan, E: Evaluate and monitor solution effectiveness. Always consider stakeholder impact, resource constraints, time limitations, and potential risks.",
-                "metadata": {
-                    "source": "core_knowledge",
-                    "type": "framework",
-                    "topic": "decision_making",
-                    "chapter": "Decision-Making Framework"
-                }
-            },
-            {
-                "content": "Conflict resolution strategies: 1) Active Listening - Understand all perspectives without judgment, 2) Identify Interests - Focus on underlying needs, not stated positions, 3) Find Common Ground - Identify shared goals and values, 4) Generate Options - Create win-win solutions collaboratively, 5) Use Objective Criteria - Apply fair standards for evaluation, 6) Separate People from Problems - Address issues, not personalities, 7) Maintain Relationships - Preserve working relationships while solving problems.",
-                "metadata": {
-                    "source": "core_knowledge",
-                    "type": "strategy",
-                    "topic": "conflict_resolution",
-                    "chapter": "Conflict Resolution Techniques"
-                }
-            },
-            {
-                "content": "Critical thinking skills development: Analysis (breaking complex information into components), Evaluation (assessing credibility and logical strength), Inference (drawing reasonable conclusions), Interpretation (understanding meaning and significance), Explanation (articulating reasoning clearly), Self-regulation (monitoring and correcting one's thinking). Practice questioning assumptions, considering multiple perspectives, examining evidence quality, and recognizing logical fallacies.",
-                "metadata": {
-                    "source": "core_knowledge",
-                    "type": "skills",
-                    "topic": "critical_thinking",
-                    "chapter": "Critical Thinking Development"
-                }
-            },
-            {
-                "content": "Team problem-solving dynamics: Establish psychological safety for open communication, define roles and responsibilities clearly, use structured problem-solving processes, encourage diverse perspectives, facilitate effective meetings, manage conflicts constructively, ensure equal participation, document decisions and action items, follow up on commitments, celebrate successes and learn from failures.",
-                "metadata": {
-                    "source": "core_knowledge",
-                    "type": "team_dynamics",
-                    "topic": "team_problem_solving",
-                    "chapter": "Team Collaboration for Problem Solving"
-                }
-            }
-        ]
-        
-        # Create documents
-        documents = []
-        for item in default_knowledge:
-            doc = Document(
-                page_content=item["content"],
-                metadata=item["metadata"]
-            )
-            documents.append(doc)
-        
-        # Create vectorstore
-        if documents and self.embeddings:
-            self.vectorstore = FAISS.from_documents(documents, self.embeddings)
-            self.stats['book_chunks'] = len(documents)
-            self.metadata['total_documents'] = len(documents)
-            self.metadata['book_processed'] = True
-            self.metadata['book_info'] = {
-                'title': 'Core Problem-Solving Knowledge',
-                'type': 'built_in',
-                'chunks': len(documents)
-            }
-            return True
-        return False
-    
-    def load_existing_knowledge(self):
-        """Load existing knowledge base from persistent storage"""
-        try:
-            # Load metadata
-            if self.metadata_path.exists():
-                with open(self.metadata_path, 'r') as f:
-                    self.metadata = json.load(f)
-            
-            # Load stats
-            if self.stats_path.exists():
-                with open(self.stats_path, 'r') as f:
-                    self.stats = json.load(f)
-            
-            # Load conversations
-            if self.conversations_path.exists():
-                with open(self.conversations_path, 'r') as f:
-                    self.conversations = json.load(f)
-            
-            # Load vectorstore
-            if self.vectorstore_path.exists() and self.embeddings:
-                self.vectorstore = FAISS.load_local(
-                    str(self.vectorstore_path), 
-                    self.embeddings,
-                    allow_dangerous_deserialization=True
-                )
-                return True
-        except Exception as e:
-            return False
-        return False
-    
-    def save_all_data(self):
-        """Save all knowledge base data to persistent storage"""
-        try:
-            # Save metadata
-            self.metadata['last_updated'] = datetime.now().isoformat()
-            with open(self.metadata_path, 'w') as f:
-                json.dump(self.metadata, f, indent=2)
-            
-            # Save stats
-            with open(self.stats_path, 'w') as f:
-                json.dump(self.stats, f, indent=2)
-            
-            # Save conversations
-            with open(self.conversations_path, 'w') as f:
-                json.dump(self.conversations, f, indent=2)
-            
-            # Save vectorstore
-            if self.vectorstore:
-                self.vectorstore.save_local(str(self.vectorstore_path))
-            
-            return True
-        except Exception as e:
-            return False
-    
-    def fallback_initialization(self):
-        """Fallback initialization if main process fails"""
-        self.create_default_knowledge()
-        self.metadata = {'fallback': True, 'created_at': datetime.now().isoformat()}
-        self.stats = {'total_queries': 0, 'learning_sessions': 0, 'book_chunks': 0, 'conversation_chunks': 0, 'silly_questions_blocked': 0}
-        self.conversations = []
-    
-    def process_book_content(self, text, page_texts, book_info):
-        """Process book content and add to knowledge base"""
-        try:
-            # Text splitter
-            text_splitter = RecursiveCharacterTextSplitter(
-                chunk_size=800,
-                chunk_overlap=150,
-                length_function=len,
-                separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""]
-            )
-            
-            # Create documents
-            documents = []
-            for page_info in page_texts:
-                page_text = page_info['text']
-                chapter_title = self._extract_chapter_title(page_text.split('\n'))
-                
-                doc = Document(
-                    page_content=page_text,
-                    metadata={
-                        "source": "book",
-                        "type": "book_content",
-                        "page": page_info['page'],
-                        "chapter": chapter_title,
-                        "word_count": page_info['word_count'],
-                        "book_title": book_info.get('title', 'Problem Solving Book'),
-                        "processed_at": datetime.now().isoformat()
-                    }
-                )
-                documents.append(doc)
-            
-            # Split into chunks
-            chunks = text_splitter.split_documents(documents)
-            
-            # Add to vectorstore
-            if self.vectorstore is None:
-                self.vectorstore = FAISS.from_documents(chunks, self.embeddings)
-            else:
-                new_vectorstore = FAISS.from_documents(chunks, self.embeddings)
-                self.vectorstore.merge_from(new_vectorstore)
-            
-            # Update metadata
-            self.metadata['book_processed'] = True
-            self.metadata['book_info'] = book_info
-            self.metadata['total_documents'] += len(chunks)
-            self.stats['book_chunks'] += len(chunks)
-            
-            return True, f"Successfully processed {len(chunks)} chunks from {len(page_texts)} pages!"
-            
-        except Exception as e:
-            return False, f"Error processing book: {str(e)}"
-    
-    def add_conversation_to_knowledge(self, question, answer):
-        """Add conversation to persistent knowledge base (auto-save)"""
-        if len(question.strip()) < 10 or len(answer.strip()) < 20:
-            return False
-        
-        try:
-            conversation_text = f"Question: {question}\n\nAnswer: {answer}"
-            
-            doc = Document(
-                page_content=conversation_text,
-                metadata={
-                    "source": "learned_conversation",
-                    "type": "qa_pair",
-                    "question": question,
-                    "answer_preview": answer[:200] + "..." if len(answer) > 200 else answer,
-                    "conversation_id": hashlib.md5(conversation_text.encode()).hexdigest()[:8],
-                    "added_at": datetime.now().isoformat(),
-                    "quality_score": self._calculate_quality_score(question, answer)
-                }
-            )
-            
-            # Add to vectorstore
-            if self.vectorstore is None:
-                self.vectorstore = FAISS.from_documents([doc], self.embeddings)
-            else:
-                new_vectorstore = FAISS.from_documents([doc], self.embeddings)
-                self.vectorstore.merge_from(new_vectorstore)
-            
-            # Store conversation
-            self.conversations.append({
-                'question': question,
-                'answer': answer,
-                'timestamp': datetime.now().isoformat(),
-                'learned': True
-            })
-            
-            # Update stats
-            self.stats['conversation_chunks'] += 1
-            self.stats['learning_sessions'] += 1
-            self.metadata['total_documents'] += 1
-            
-            # Auto-save to persistent storage
-            self.save_all_data()
-            
-            return True
-            
-        except Exception as e:
-            return False
-    
-    def search_knowledge_base(self, query, k=5):
-        """Search the persistent knowledge base"""
-        if self.vectorstore is None:
-            return []
-        
-        try:
-            self.stats['total_queries'] += 1
-            docs = self.vectorstore.similarity_search_with_score(query, k=k)
-            
-            results = []
-            for doc, score in docs:
-                result = {
-                    'content': doc.page_content,
-                    'source': doc.metadata.get('source', 'unknown'),
-                    'type': doc.metadata.get('type', 'unknown'),
-                    'page': doc.metadata.get('page', 'N/A'),
-                    'chapter': doc.metadata.get('chapter', 'Unknown Section'),
-                    'similarity_score': float(score),
-                    'metadata': doc.metadata
-                }
-                results.append(result)
-            
-            return results
-            
-        except Exception as e:
-            return []
-    
-    def _extract_chapter_title(self, lines):
-        """Extract chapter title from text lines"""
-        for line in lines[:10]:
-            line = line.strip()
-            if line and len(line) < 100:
-                if re.match(r'(chapter|section|part|unit)\s+\d+', line.lower()):
-                    return line
-                if line.isupper() or line.istitle():
-                    return line
-        return "General Content"
-    
-    def _calculate_quality_score(self, question, answer):
-        """Calculate conversation quality score"""
-        score = 0
-        
-        # Question quality
-        if len(question.split()) >= 5: score += 1
-        if any(word in question.lower() for word in ['how', 'what', 'why', 'strategy', 'problem']): score += 1
-        if '?' in question: score += 1
-        
-        # Answer quality  
-        if len(answer.split()) >= 20: score += 1
-        if any(word in answer.lower() for word in ['approach', 'solution', 'method', 'step']): score += 1
-        
-        return min(score, 5)
-
-class AshokMinimalChatbot:
-    def __init__(self):
-        self.knowledge_base = PersistentHFKnowledgeBase()
-    
-    def is_silly_question(self, question):
-        """Detect silly or irrelevant questions"""
-        question_lower = question.lower().strip()
-        
-        if len(question_lower) < 3:
-            return True
-        
-        # Greeting patterns
-        greeting_patterns = [
-            r'\b(hello|hi|hey|salam|namaste|adab)\b',
-            r'\b(good morning|evening|afternoon)\b',
-            r'\b(how are you|kaise ho|kya hal)\b',
-            r'\b(what.*your name|who are you)\b'
-        ]
-        
-        # Silly keywords
-        silly_keywords = [
-            'stupid', 'dumb', 'joke', 'funny', 'lol', 'weather', 'movie',
-            'song', 'game', 'gossip', 'love', 'dating', 'facebook',
-            'instagram', 'politics', 'religion', 'age', 'appearance'
-        ]
-        
-        # Problem-solving keywords
-        good_keywords = [
-            'problem', 'solve', 'solution', 'strategy', 'approach',
-            'method', 'challenge', 'decision', 'plan', 'analyze',
-            'conflict', 'team', 'work', 'project', 'manage'
-        ]
-        
-        # Check patterns
-        for pattern in greeting_patterns:
-            if re.search(pattern, question_lower):
-                return True
-        
-        # Score keywords
-        good_score = sum(1 for keyword in good_keywords if keyword in question_lower)
-        if good_score >= 2:
-            return False
-        
-        silly_score = sum(1 for keyword in silly_keywords if keyword in question_lower)
-        if silly_score >= 1:
-            return True
-        
-        # Check structure
-        question_words = ['what', 'how', 'why', 'when', 'where', 'can', 'should']
-        has_question_word = any(word in question_lower.split() for word in question_words)
-        word_count = len(question_lower.split())
-        
-        if word_count < 4 and not has_question_word:
-            return True
-        
-        return False
-    
-    def generate_response(self, question, api_key):
-        """Generate response using Gemini"""
-        try:
-            # Check for silly questions
-            if self.is_silly_question(question):
-                self.knowledge_base.stats['silly_questions_blocked'] += 1
-                
-                silly_responses = [
-                    "Abay Sallay! Don't waste my time with such bakwas. Ask me something related to problem solving yaar!",
-                    "Abay Sallay! Ye kya timepass hai? I'm here to help with problem solving, not for chit-chat. Be serious!",
-                    "Abay Sallay! Focus on real problems that need solving, samjha? Ask about strategies and approaches!",
-                    "Abay Sallay! This is a problem-solving platform. Ask me about challenges, decisions, ya conflict resolution!",
-                    "Abay Sallay! Tumhara dimagh kahan hai? Ask meaningful questions about problem-solving techniques, yaar!"
-                ]
-                
-                import random
-                return random.choice(silly_responses), False
-            
-            # Configure Gemini
-            genai.configure(api_key=api_key)
-            model = genai.GenerativeModel('gemini-2.0-flash')
-            
-            # Search knowledge base
-            relevant_results = self.knowledge_base.search_knowledge_base(question, k=5)
-            
-            # Build context
-            context = ""
-            references = []
-            
-            if relevant_results:
-                context = "=== RELEVANT KNOWLEDGE ===\n\n"
-                for i, result in enumerate(relevant_results, 1):
-                    source_type = result['type']
-                    if source_type == 'book_content':
-                        context += f"**Reference {i}** (Chapter: {result['chapter']}, Page: {result['page']}):\n"
-                    elif source_type == 'qa_pair':
-                        context += f"**Learning {i}** (From past conversations):\n"
-                    else:
-                        context += f"**Framework {i}** (Core knowledge):\n"
-                    
-                    context += f"{result['content']}\n\n"
-                    references.append(result)
-            
-            # Create prompt
-            prompt = f"""
-            You are Ashok, a problem-solving expert with Pakistani/Indian conversational style.
-            
-            Your characteristics:
-            1. Mix English with Urdu naturally: "yaar", "acha", "bilkul", "samjha", "dekho"
-            2. Enthusiastic responses: "Excellent question yaar!" or "Bahut acha sawal!"
-            3. Reference knowledge sources when available
-            4. Provide practical, actionable advice
-            5. Encouraging and professional tone
-            
-            {context}
-            
-            User Question: {question}
-            
-            Provide a comprehensive, practical response using your characteristic style.
-            Reference the knowledge sources when relevant and give actionable steps.
-            """
-            
-            response = model.generate_content(prompt)
-            final_response = response.text
-            
-            # Add clean references section
-            if references:
-                final_response += "\n\n**Knowledge Sources:**\n"
-                for ref in references:
-                    if ref['type'] == 'book_content':
-                        final_response += f"• Book: {ref['chapter']} (Page {ref['page']})\n"
-                    elif ref['type'] == 'qa_pair':
-                        final_response += f"• Previous Learning\n"
-                    else:
-                        final_response += f"• Core Framework: {ref['metadata'].get('topic', 'Problem-solving')}\n"
-            
-            return final_response, True
-            
-        except Exception as e:
-            return f"Sorry yaar, I encountered an error: {str(e)}. Please check your API key and try again!", False
-
-def show_typing_indicator():
-    """Show typing indicator"""
-    st.markdown("""
-    <div class="typing-indicator">
-        <span>Ashok is thinking</span>
-        <div class="typing-dots">
-            <div class="typing-dot"></div>
-            <div class="typing-dot"></div>
-            <div class="typing-dot"></div>
-        </div>
-    </div>
-    """, unsafe_allow_html=True)
-
-def show_api_setup():
-    """Show API key setup interface"""
-    st.markdown("""
-    <div class="api-setup-container">
-        <div class="api-setup-title">Welcome to Ashok 2.0</div>
-        <div class="api-setup-subtitle">To get started, please enter your Gemini API key</div>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    # API key input
-    api_key = st.text_input(
-        "Gemini API Key",
-        type="password",
-        placeholder="Enter your API key here...",
-        label_visibility="collapsed"
-    )
-    
-    # API guide
-    st.markdown("""
-    <div class="api-guide">
-        <div class="api-guide-title">How to get your free API key:</div>
-        <div class="api-guide-text">1. Visit <a href="https://makersuite.google.com/app/apikey" target="_blank" class="api-link">Google AI Studio</a></div>
-        <div class="api-guide-text">2. Sign in with your Google account</div>
-        <div class="api-guide-text">3. Click "Create API Key"</div>
-        <div class="api-guide-text">4. Copy and paste the key above</div>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    return api_key
-
-def show_quick_actions():
-    """Show enhanced quick action buttons"""
-    st.markdown("""
-    <div class="quick-actions-container">
-        <div class="quick-actions-title">Quick Start Questions</div>
-        <div class="quick-action-grid">
-    """, unsafe_allow_html=True)
-    
-    # Quick action data
-    actions = [
-        {
-            "icon": "🎯",
-            "title": "Task Prioritization",
-            "desc": "Learn how to prioritize when everything seems urgent",
-            "question": "How do I prioritize tasks when everything seems urgent and important?"
-        },
-        {
-            "icon": "🤝",
-            "title": "Conflict Resolution",
-            "desc": "Effective strategies for resolving team conflicts",
-            "question": "What's the best approach to resolve conflicts in my team?"
-        },
-        {
-            "icon": "⚡",
-            "title": "Decision Making",
-            "desc": "Improve your decision-making process",
-            "question": "How can I improve my decision-making process for complex problems?"
-        },
-        {
-            "icon": "🎪",
-            "title": "Difficult People",
-            "desc": "Handle challenging stakeholders professionally",
-            "question": "How do I deal with difficult stakeholders effectively?"
-        },
-        {
-            "icon": "🔄",
-            "title": "Change Management",
-            "desc": "Navigate organizational changes smoothly",
-            "question": "How can I help my team adapt to organizational changes?"
-        },
-        {
-            "icon": "💡",
-            "title": "Creative Solutions",
-            "desc": "Generate innovative solutions to problems",
-            "question": "What techniques can I use to think more creatively about problems?"
-        }
-    ]
-    
-    # Create columns for actions
-    cols = st.columns(2)
-    for i, action in enumerate(actions):
-        with cols[i % 2]:
-            if st.button(
-                f"{action['icon']} {action['title']}",
-                key=f"action_{i}",
-                help=action['desc'],
-                use_container_width=True
-            ):
-                st.session_state.auto_question = action['question']
-                st.rerun()
-    
-    st.markdown("</div></div>", unsafe_allow_html=True)
-
-def test_api_key(api_key):
-    """Test if API key is valid"""
-    try:
-        genai.configure(api_key=api_key)
-        model = genai.GenerativeModel('gemini-2.0-flash')
-        test_response = model.generate_content("Hello")
-        return True
-    except Exception as e:
-        return False
-
-# Global instance for the app
-@st.cache_resource
-def get_chatbot():
-    """Get cached chatbot instance"""
-    return AshokMinimalChatbot()
-
-def main():
-    # Get cached chatbot instance
-    chatbot = get_chatbot()
-    
-    # Initialize session state
-    if 'messages' not in st.session_state:
-        st.session_state.messages = []
-    
-    if 'auto_question' not in st.session_state:
-        st.session_state.auto_question = None
-    
-    if 'api_key_valid' not in st.session_state:
-        st.session_state.api_key_valid = False
-    
-    # Main title
-    st.markdown('<h1 class="main-title">ASHOK 2.0</h1>', unsafe_allow_html=True)
-    
-    # API Key Setup Phase
-    if not st.session_state.api_key_valid:
-        api_key = show_api_setup()
-        
-        if api_key:
-            if test_api_key(api_key):
-                st.session_state.api_key = api_key
-                st.session_state.api_key_valid = True
-                st.markdown("""
-                <div class="status-success">
-                    API key configured successfully! Ready to chat.
-                </div>
-                """, unsafe_allow_html=True)
-                time.sleep(1)
-                st.rerun()
-            else:
-                st.markdown("""
-                <div class="status-warning">
-                    Invalid API key. Please check and try again.
-                </div>
-                """, unsafe_allow_html=True)
-    
-    # Main Chat Interface Phase
-    else:
-        api_key = st.session_state.api_key
-        
-        # Handle auto questions from quick actions
-        if st.session_state.auto_question:
-            # Add user message
-            st.session_state.messages.append({"role": "user", "content": st.session_state.auto_question})
-            
-            # Generate response
-            with st.spinner("Processing your question..."):
-                response, is_helpful = chatbot.generate_response(st.session_state.auto_question, api_key)
-                st.session_state.messages.append({"role": "assistant", "content": response})
-                
-                # Auto-learn from helpful conversations
-                if is_helpful and not chatbot.is_silly_question(st.session_state.auto_question):
-                    chatbot.knowledge_base.add_conversation_to_knowledge(st.session_state.auto_question, response)
-            
-            # Clear auto question
-            st.session_state.auto_question = None
-            st.rerun()
-        
-        # Display chat history
-        if st.session_state.messages:
-            st.markdown('<div class="chat-container">', unsafe_allow_html=True)
-            for message in st.session_state.messages:
-                with st.chat_message(message["role"]):
-                    st.markdown(message["content"])
-            st.markdown('</div>', unsafe_allow_html=True)
-        
-        # Chat input
-        if prompt := st.chat_input("Ask about problem-solving strategies...", key="main_chat"):
-            # Add user message
-            st.session_state.messages.append({"role": "user", "content": prompt})
-            with st.chat_message("user"):
-                st.markdown(prompt)
-            
-            # Generate response with typing indicator
-            with st.chat_message("assistant"):
-                # Show typing indicator
-                typing_placeholder = st.empty()
-                with typing_placeholder:
-                    show_typing_indicator()
-                
-                # Simulate typing delay
-                time.sleep(1)
-                
-                # Clear typing indicator and show response
-                typing_placeholder.empty()
-                
-                with st.spinner("Processing..."):
-                    response, is_helpful = chatbot.generate_response(prompt, api_key)
-                    st.markdown(response)
-                    
-                    # Add to chat history
-                    st.session_state.messages.append({"role": "assistant", "content": response})
-                    
-                    # Auto-learn from helpful conversations
-                    if is_helpful and not chatbot.is_silly_question(prompt):
-                        learned = chatbot.knowledge_base.add_conversation_to_knowledge(prompt, response)
-                        if learned:
-                            st.markdown(
-                                '<div class="learning-indicator">Knowledge updated - This conversation has been learned!</div>',
-                                unsafe_allow_html=True
-                            )
-        
-        # Show quick actions if no messages yet
-        if not st.session_state.messages:
-            show_quick_actions()
-        
-        # Control panel
-        if st.session_state.messages:
-            st.markdown("""
-            <div class="control-panel">
-            """, unsafe_allow_html=True)
-            
-            if st.button("Clear Chat", key="clear_chat"):
-                st.session_state.messages = []
-                st.rerun()
-            
-            st.markdown("</div>", unsafe_allow_html=True)
-
-if __name__ == "__main__":
-    main()