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AI-Digital-Library-Assistant

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Nihal2000
Update app.py
3ec7ef1 verified 11 months ago
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import gradio as gr
import os
import asyncio
import json
import logging
import tempfile
import uuid
import requests
from PIL import Image
from io import BytesIO
from datetime import datetime
from pathlib import Path
from typing import List, Dict, Any, Optional
import nest_asyncio

# Apply nest_asyncio to handle nested event loops in Gradio
nest_asyncio.apply()

# Import our custom modules
from mcp_tools.ingestion_tool import IngestionTool
from mcp_tools.search_tool import SearchTool
from mcp_tools.generative_tool import GenerativeTool
from mcp_tools.voice_tool import VoiceTool
from mcp_tools.podcast_tool import PodcastTool
from services.vector_store_service import VectorStoreService
from services.document_store_service import DocumentStoreService
from services.embedding_service import EmbeddingService
from services.llm_service import LLMService
from services.ocr_service import OCRService
from services.llamaindex_service import LlamaIndexService
from services.elevenlabs_service import ElevenLabsService
from services.podcast_generator_service import PodcastGeneratorService
from core.models import SearchResult, Document
import config
from mcp_server import mcp as fast_mcp

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ContentOrganizerMCPServer:
    def __init__(self):
        # Initialize services
        logger.info("Initializing Content Organizer MCP Server...")
        self.vector_store = VectorStoreService()
        self.document_store = DocumentStoreService()
        self.embedding_service = EmbeddingService()
        self.llm_service = LLMService()
        self.ocr_service = OCRService()
        self.llamaindex_service = LlamaIndexService(self.document_store)
        
        # Initialize ElevenLabs voice service
        self.elevenlabs_service = ElevenLabsService(self.llamaindex_service)
        
        # Initialize Podcast Generator
        self.podcast_generator = PodcastGeneratorService(
            llamaindex_service=self.llamaindex_service,
            llm_service=self.llm_service
        )
        
        # Initialize tools
        self.ingestion_tool = IngestionTool(
            vector_store=self.vector_store,
            document_store=self.document_store,
            embedding_service=self.embedding_service,
            ocr_service=self.ocr_service
        )
        self.search_tool = SearchTool(
            vector_store=self.vector_store,
            embedding_service=self.embedding_service,
            document_store=self.document_store
        )
        self.generative_tool = GenerativeTool(
            llm_service=self.llm_service,
            search_tool=self.search_tool
        )
        self.voice_tool = VoiceTool(self.elevenlabs_service)
        self.podcast_tool = PodcastTool(self.podcast_generator)
        
        # Track processing status
        self.processing_status = {}
        
        # Document cache for quick access
        self.document_cache = {}
        logger.info("Content Organizer MCP Server initialized successfully!")

    def run_async(self, coro):
        """Helper to run async functions in Gradio"""
        try:
            loop = asyncio.get_event_loop()
        except RuntimeError:
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
        if loop.is_running():
            # If loop is already running, create a task
            import concurrent.futures
            with concurrent.futures.ThreadPoolExecutor() as executor:
                future = executor.submit(asyncio.run, coro)
                return future.result()
        else:
            return loop.run_until_complete(coro)

    async def ingest_document_async(self, file_path: str, file_type: str) -> Dict[str, Any]:
        """MCP Tool: Ingest and process a document"""
        try:
            task_id = str(uuid.uuid4())
            self.processing_status[task_id] = {"status": "processing", "progress": 0}
            result = await self.ingestion_tool.process_document(file_path, file_type, task_id)
            if result.get("success"):
                self.processing_status[task_id] = {"status": "completed", "progress": 100}
                doc_id = result.get("document_id")
                if doc_id:
                    doc = await self.document_store.get_document(doc_id)
                    if doc:
                        self.document_cache[doc_id] = doc
                return result
            else:
                self.processing_status[task_id] = {"status": "failed", "error": result.get("error")}
                return result
        except Exception as e:
            logger.error(f"Document ingestion failed: {str(e)}")
            return {"success": False, "error": str(e), "message": "Failed to process document"}

    async def get_document_content_async(self, document_id: str) -> Optional[str]:
        """Get document content by ID"""
        try:
            # Check cache first
            if document_id in self.document_cache:
                return self.document_cache[document_id].content
            
            # Get from store
            doc = await self.document_store.get_document(document_id)
            if doc:
                self.document_cache[document_id] = doc
                return doc.content
            return None
        except Exception as e:
            logger.error(f"Error getting document content: {str(e)}")
            return None

    async def semantic_search_async(self, query: str, top_k: int = 5, filters: Optional[Dict] = None) -> Dict[str, Any]:
        """MCP Tool: Perform semantic search"""
        try:
            results = await self.search_tool.search(query, top_k, filters)
            return {"success": True, "query": query, "results": [result.to_dict() for result in results], "total_results": len(results)}
        except Exception as e:
            logger.error(f"Semantic search failed: {str(e)}")
            return {"success": False, "error": str(e), "query": query, "results": []}

    async def summarize_content_async(self, content: str = None, document_id: str = None, style: str = "concise") -> Dict[str, Any]:
        try:
            if document_id and document_id != "none":
                content = await self.get_document_content_async(document_id)
                if not content:
                    return {"success": False, "error": f"Document {document_id} not found"}
            if not content or not content.strip():
                return {"success": False, "error": "No content provided for summarization"}
            max_content_length = 4000
            if len(content) > max_content_length:
                content = content[:max_content_length] + "..."
            summary = await self.generative_tool.summarize(content, style)
            return {"success": True, "summary": summary, "original_length": len(content), "summary_length": len(summary), "style": style, "document_id": document_id}
        except Exception as e:
            logger.error(f"Summarization failed: {str(e)}")
            return {"success": False, "error": str(e)}

    async def generate_tags_async(self, content: str = None, document_id: str = None, max_tags: int = 5) -> Dict[str, Any]:
        """MCP Tool: Generate tags for content"""
        try:
            if document_id and document_id != "none":
                content = await self.get_document_content_async(document_id)
                if not content:
                    return {"success": False, "error": f"Document {document_id} not found"}
            if not content or not content.strip():
                return {" success": False, "error": "No content provided for tag generation"}
            tags = await self.generative_tool.generate_tags(content, max_tags)
            if document_id and document_id != "none" and tags:
                await self.document_store.update_document_metadata(document_id, {"tags": tags})
            return {"success": True, "tags": tags, "content_length": len(content), "document_id": document_id}
        except Exception as e:
            logger.error(f"Tag generation failed: {str(e)}")
            return {"success": False, "error": str(e)}
    
    async def generate_podcast_async(
        self,
        document_ids: List[str],
        style: str = "conversational",
        duration_minutes: int = 10,
        host1_voice: str = "Rachel",
        host2_voice: str = "Adam"
    ) -> Dict[str, Any]:
        """Generate podcast from documents"""
        try:
            result = await self.podcast_tool.generate_podcast(
                document_ids=document_ids,
                style=style,
                duration_minutes=duration_minutes,
                host1_voice=host1_voice,
                host2_voice=host2_voice
            )
            return result
        except Exception as e:
            logger.error(f"Podcast generation failed: {str(e)}")
            return {"success": False, "error": str(e)}

    async def generate_podcast_transcript_async(
        self,
        document_ids: List[str],
        style: str = "conversational",
        duration_minutes: int = 10
    ) -> Dict[str, Any]:
        """Generate podcast transcript without audio"""
        try:
            return await self.podcast_tool.generate_transcript(
                document_ids=document_ids,
                style=style,
                duration_minutes=duration_minutes
            )
        except Exception as e:
            logger.error(f"Transcript generation failed: {str(e)}")
            return {"success": False, "error": str(e)}

    def list_podcasts_sync(self, limit: int = 10) -> Dict[str, Any]:
        """List generated podcasts"""
        try:
            return self.podcast_tool.list_podcasts(limit)
        except Exception as e:
            logger.error(f"Listing podcasts failed: {str(e)}")
            return {"success": False, "error": str(e)}

    async def get_podcast_async(self, podcast_id: str) -> Dict[str, Any]:
        """Get podcast metadata"""
        try:
            return self.podcast_tool.get_podcast(podcast_id)
        except Exception as e:
            logger.error(f"Getting podcast failed: {str(e)}")
            return {"success": False, "error": str(e)}

    async def get_podcast_audio_async(self, podcast_id: str) -> Dict[str, Any]:
        """Get podcast audio path"""
        try:
            return self.podcast_tool.get_podcast_audio(podcast_id)
        except Exception as e:
            logger.error(f"Getting podcast audio failed: {str(e)}")
            return {"success": False, "error": str(e)}

    async def answer_question_async(self, question: str, context_filter: Optional[Dict] = None) -> Dict[str, Any]:
        try:
            search_results = await self.search_tool.search(question, top_k=5, filters=context_filter)
            if not search_results:
                return {"success": False, "error": "No relevant context found in your documents. Please make sure you have uploaded relevant documents.", "question": question}
            answer = await self.generative_tool.answer_question(question, search_results)
            return {"success": True, "question": question, "answer": answer, "sources": [result.to_dict() for result in search_results], "confidence": "high" if len(search_results) >= 3 else "medium"}
        except Exception as e:
            logger.error(f"Question answering failed: {str(e)}")
            return {"success": False, "error": str(e), "question": question}

    async def generate_outline_async(self, topic: str, num_sections: int = 5, detail_level: str = "medium") -> Dict[str, Any]:
        try:
            outline = await self.generative_tool.generate_outline(topic, num_sections, detail_level)
            return {"success": True, "result": outline}
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def explain_concept_async(self, concept: str, audience: str = "general", length: str = "medium") -> Dict[str, Any]:
        try:
            explanation = await self.generative_tool.explain_concept(concept, audience, length)
            return {"success": True, "result": explanation}
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def paraphrase_text_async(self, text: str, style: str = "formal") -> Dict[str, Any]:
        try:
            paraphrase = await self.generative_tool.paraphrase_text(text, style)
            return {"success": True, "result": paraphrase}
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def categorize_content_async(self, content: str, categories: List[str]) -> Dict[str, Any]:
        try:
            category = await self.generative_tool.categorize(content, categories)
            return {"success": True, "result": category}
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def extract_key_insights_async(self, content: str, num_insights: int = 5) -> Dict[str, Any]:
        try:
            insights = await self.generative_tool.extract_key_insights(content, num_insights)
            return {"success": True, "result": "\n".join([f"- {insight}" for insight in insights])}
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def generate_questions_async(self, content: str, question_type: str = "comprehension", num_questions: int = 5) -> Dict[str, Any]:
        try:
            questions = await self.generative_tool.generate_questions(content, question_type, num_questions)
            return {"success": True, "result": "\n".join([f"{i+1}. {q}" for i, q in enumerate(questions)])}
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def extract_key_information_async(self, content: str) -> Dict[str, Any]:
        try:
            info = await self.llm_service.extract_key_information(content)
            return {"success": True, "result": json.dumps(info, indent=2)}
        except Exception as e:
            return {"success": False, "error": str(e)}

    def list_documents_sync(self, limit: int = 100, offset: int = 0) -> Dict[str, Any]:
        try:
            documents = self.run_async(self.document_store.list_documents(limit, offset))
            return {"success": True, "documents": [doc.to_dict() for doc in documents], "total": len(documents)}
        except Exception as e:
            return {"success": False, "error": str(e)}

mcp_server = ContentOrganizerMCPServer()
try:
    print("⏳ Initializing LlamaIndex Service...")
    # Use the fixed run_async method to safely initialize
    mcp_server.run_async(mcp_server.llamaindex_service.initialize())
    print("✅ LlamaIndex Initialized Successfully!")
except Exception as e:
    print(f"⚠️ Warning during LlamaIndex init: {e}")


async def generate_podcast_transcript(
    document_ids: List[str],
    style: str = "conversational",
    duration_minutes: int = 10
) -> Dict[str, Any]:
    """Generate podcast transcript without audio"""
    return await mcp_server.generate_podcast_transcript_async(document_ids, style, duration_minutes)

async def list_podcasts(limit: int = 10) -> Dict[str, Any]:
    """List generated podcasts"""
    return mcp_server.list_podcasts_sync(limit)

async def get_podcast(podcast_id: str) -> Dict[str, Any]:
    """Get podcast metadata"""
    return await mcp_server.get_podcast_async(podcast_id)

async def get_podcast_audio(podcast_id: str) -> Dict[str, Any]:
    """Get podcast audio path"""
    return await mcp_server.get_podcast_audio_async(podcast_id)

def get_document_list():
    try:
        result = mcp_server.list_documents_sync(limit=100)
        if result["success"]:
            if result["documents"]:
                doc_list_str = "📚 Documents in Library:\n\n"
                for i, doc_item in enumerate(result["documents"], 1):
                    doc_list_str += f"{i}. {doc_item['filename']} (ID: {doc_item['id'][:8]}...)\n"
                    doc_list_str += f"   Type: {doc_item['doc_type']}, Size: {doc_item['file_size']} bytes\n"
                    if doc_item.get('tags'):
                        doc_list_str += f"   Tags: {', '.join(doc_item['tags'])}\n"
                    doc_list_str += f"   Created: {doc_item['created_at'][:10]}\n\n"
                return doc_list_str
            else:
                return "No documents in library yet. Upload some documents to get started!"
        else:
            return f"Error loading documents: {result['error']}"
    except Exception as e:
        return f"Error: {str(e)}"

def get_document_choices():
    try:
        result = mcp_server.list_documents_sync(limit=100)
        if result["success"] and result["documents"]:
            choices = [(f"{doc['filename']} ({doc['id'][:8]}...)", doc['id']) for doc in result["documents"]]
            logger.info(f"Generated {len(choices)} document choices")
            return choices
        return []
    except Exception as e:
        logger.error(f"Error getting document choices: {str(e)}")
        return []

def refresh_library():
    """Refresh library and update all document selectors"""
    doc_list_refreshed = get_document_list()
    doc_choices_refreshed = get_document_choices()
    logger.info(f"Refreshing library. Found {len(doc_choices_refreshed)} choices.")
    return (
        doc_list_refreshed,
        gr.update(choices=doc_choices_refreshed),
        gr.update(choices=doc_choices_refreshed),
        gr.update(choices=doc_choices_refreshed),
        gr.update(choices=doc_choices_refreshed)
    )

def upload_and_process_file(file):
    """
    Upload and process a document (PDF, TXT, DOCX, or images) into the AI Digital Library.
    Extracts text, generates embeddings, and indexes for semantic search.
    
    Args:
        file: Document file to upload and process
        
    Returns:
        Processing status, document ID, and updated library information
    """
    if file is None:
        doc_list_initial = get_document_list()
        doc_choices_initial = get_document_choices()
        return (
            "No file uploaded", 
            "", 
            doc_list_initial,
            gr.update(choices=doc_choices_initial),  # delete_doc_dropdown_visible
            gr.update(choices=doc_choices_initial),  # doc_dropdown_content
            gr.update(choices=doc_choices_initial),  # doc_dropdown_tag_visible
            gr.update(choices=doc_choices_initial)   # podcast_doc_selector (ADD THIS!)
        )
    try:
        file_path = file.name if hasattr(file, 'name') else str(file)
        file_type = Path(file_path).suffix.lower().strip('.')
        logger.info(f"Processing file: {file_path}, type: {file_type}")
        result = mcp_server.run_async(mcp_server.ingest_document_async(file_path, file_type))
        if result["success"]:
            logger.info("Syncing LlamaIndex with new document...")
            mcp_server.run_async(mcp_server.llamaindex_service.sync_on_demand())
        
        doc_list_updated = get_document_list()
        doc_choices_updated = get_document_choices()

        if result["success"]:
            return (
                f"✅ Success: {result['message']}\nDocument ID: {result['document_id']}\nChunks created: {result['chunks_created']}",
                result["document_id"],
                doc_list_updated,
                gr.update(choices=doc_choices_updated),  # delete_doc_dropdown_visible
                gr.update(choices=doc_choices_updated),  # doc_dropdown_content
                gr.update(choices=doc_choices_updated),  # doc_dropdown_tag_visible
                gr.update(choices=doc_choices_updated)   # podcast_doc_selector (ADD THIS!)
            )
        else:
            return (
                f"❌ Error: {result.get('error', 'Unknown error')}", 
                "",
                doc_list_updated,
                gr.update(choices=doc_choices_updated),  # delete_doc_dropdown_visible
                gr.update(choices=doc_choices_updated),  # doc_dropdown_content
                gr.update(choices=doc_choices_updated),  # doc_dropdown_tag_visible
                gr.update(choices=doc_choices_updated)   # podcast_doc_selector (ADD THIS!)
            )
    except Exception as e:
        logger.error(f"Error processing file: {str(e)}")
        doc_list_error = get_document_list()
        doc_choices_error = get_document_choices()
        return (
            f"❌ Error: {str(e)}", 
            "",
            doc_list_error,
            gr.update(choices=doc_choices_error),  # delete_doc_dropdown_visible
            gr.update(choices=doc_choices_error),  # doc_dropdown_content
            gr.update(choices=doc_choices_error),  # doc_dropdown_tag_visible
            gr.update(choices=doc_choices_error)   # podcast_doc_selector (ADD THIS!)
        )

def perform_search(query, top_k):
    """
    Search through all uploaded documents using semantic search.
    Finds relevant content based on meaning, not just keywords.
    
    Args:
        query: Natural language search query
        top_k: Number of results to return (1-20)
        
    Returns:
        Search results with relevance scores and source documents
    """
    if not query.strip():
        return "Please enter a search query"
    try:
        result = mcp_server.run_async(mcp_server.semantic_search_async(query, int(top_k)))
        if result["success"]:
            if result["results"]:
                output_str = f"🔍 Found {result['total_results']} results for: '{query}'\n\n"
                for i, res_item in enumerate(result["results"], 1):
                    output_str += f"Result {i}:\n"
                    output_str += f"📊 Relevance Score: {res_item['score']:.3f}\n"
                    output_str += f"📄 Content: {res_item['content'][:300]}...\n"
                    if 'document_filename' in res_item.get('metadata', {}):
                        output_str += f"📁 Source: {res_item['metadata']['document_filename']}\n"
                    output_str += f"🔗 Document ID: {res_item.get('document_id', 'Unknown')}\n"
                    output_str += "-" * 80 + "\n\n"
                return output_str
            else:
                return f"No results found for: '{query}'\n\nMake sure you have uploaded relevant documents first."
        else:
            return f"❌ Search failed: {result['error']}"
    except Exception as e:
        logger.error(f"Search error: {str(e)}")
        return f"❌ Error: {str(e)}"

def update_options_visibility(task):
    """Update visibility of options based on selected task"""
    return (
        gr.update(visible=task == "Summarize"),
        gr.update(visible=task == "Generate Outline"),
        gr.update(visible=task == "Generate Outline"),
        gr.update(visible=task == "Explain Concept"),
        gr.update(visible=task == "Explain Concept"),
        gr.update(visible=task == "Paraphrase"),
        gr.update(visible=task == "Categorize"),
        gr.update(visible=task in ["Key Insights", "Generate Questions"]),
        gr.update(visible=task == "Generate Questions")
    )

def execute_content_task(task, doc_choice, custom_text, 
                        summary_style, outline_sections, outline_detail,
                        explain_audience, explain_length,
                        paraphrase_style, categories_input,
                        num_items, question_type):
    try:
        content = ""
        if custom_text and custom_text.strip():
            content = custom_text
        elif doc_choice and doc_choice != "none":
            content = mcp_server.run_async(mcp_server.get_document_content_async(doc_choice))
            if not content:
                return "❌ Error: Document not found or empty"
        else:
            if task == "Generate Outline":
                content = custom_text
            else:
                return "⚠️ Please select a document or enter text"

        result = {"success": False, "error": "Unknown task"}
        
        if task == "Summarize":
            result = mcp_server.run_async(mcp_server.summarize_content_async(content=content, style=summary_style))
            if result["success"]:
                return f"📝 Summary ({summary_style}):\n\n{result['summary']}"
                
        elif task == "Generate Outline":
            result = mcp_server.run_async(mcp_server.generate_outline_async(content, int(outline_sections), outline_detail))
            if result["success"]:
                return f"📝 Outline for '{content}':\n\n{result['result']}"
                
        elif task == "Explain Concept":
            result = mcp_server.run_async(mcp_server.explain_concept_async(content, explain_audience, explain_length))
            if result["success"]:
                return f"💡 Explanation ({explain_audience}):\n\n{result['result']}"
                
        elif task == "Paraphrase":
            result = mcp_server.run_async(mcp_server.paraphrase_text_async(content, paraphrase_style))
            if result["success"]:
                return f"🔄 Paraphrased Text ({paraphrase_style}):\n\n{result['result']}"
                
        elif task == "Categorize":
            categories = [c.strip() for c in categories_input.split(',')] if categories_input else []
            result = mcp_server.run_async(mcp_server.categorize_content_async(content, categories))
            if result["success"]:
                return f"🏷️ Category:\n\n{result['result']}"
                
        elif task == "Key Insights":
            result = mcp_server.run_async(mcp_server.extract_key_insights_async(content, int(num_items)))
            if result["success"]:
                return f"🔍 Key Insights:\n\n{result['result']}"
                
        elif task == "Generate Questions":
            result = mcp_server.run_async(mcp_server.generate_questions_async(content, question_type, int(num_items)))
            if result["success"]:
                return f"❓ Generated Questions ({question_type}):\n\n{result['result']}"
                
        elif task == "Extract Key Info":
            result = mcp_server.run_async(mcp_server.extract_key_information_async(content))
            if result["success"]:
                return f"📊 Key Information:\n\n{result['result']}"

        if not result["success"]:
            return f"❌ Error: {result.get('error', 'Unknown error')}"
            
        return "✅ Task completed"

    except Exception as e:
        logger.error(f"Task execution error: {str(e)}")
        return f"❌ Error: {str(e)}"

def generate_tags_for_document(doc_choice, custom_text, max_tags):
    """
    Generate relevant tags for a document or custom text using AI.
    Tags are automatically saved to the document metadata.
    
    Args:
        doc_choice: Document ID to generate tags for (or None)
        custom_text: Custom text to generate tags from (if no document selected)
        max_tags: Maximum number of tags to generate (3-15)
        
    Returns:
        Generated tags and statistics
    """
    try:
        logger.info(f"Generate tags called with doc_choice: {doc_choice}, type: {type(doc_choice)}")
        document_id = doc_choice if doc_choice and doc_choice != "none" and doc_choice != "" else None

        if custom_text and custom_text.strip():
            logger.info("Using custom text for tag generation")
            result = mcp_server.run_async(mcp_server.generate_tags_async(content=custom_text, max_tags=int(max_tags)))
        elif document_id:
            logger.info(f"Generating tags for document: {document_id}")
            result = mcp_server.run_async(mcp_server.generate_tags_async(document_id=document_id, max_tags=int(max_tags)))
        else:
            return "Please select a document from the dropdown or enter text to generate tags"
        
        if result["success"]:
            tags_str = ", ".join(result["tags"])
            output_str = f"🏷️ Generated Tags:\n\n{tags_str}\n\n"
            output_str += f"📊 Statistics:\n"
            output_str += f"- Content length: {result['content_length']} characters\n"
            output_str += f"- Number of tags: {len(result['tags'])}\n"
            if result.get('document_id'):
                output_str += f"- Document ID: {result['document_id']}\n"
                output_str += f"\n✅ Tags have been saved to the document."
            return output_str
        else:
            return f"❌ Tag generation failed: {result['error']}"
    except Exception as e:
        logger.error(f"Tag generation error: {str(e)}")
        return f"❌ Error: {str(e)}"

def ask_question(question):
    """
    Ask questions about your uploaded documents using RAG (Retrieval Augmented Generation).
    The AI searches through documents to find relevant context and provides comprehensive answers.
    
    Args:
        question: Natural language question about your documents
        
    Returns:
        AI-generated answer with source documents and confidence level
    """
    if not question.strip():
        return "Please enter a question"
    try:
        result = mcp_server.run_async(mcp_server.answer_question_async(question))
        if result["success"]:
            output_str = f"❓ Question: {result['question']}\n\n"
            output_str += f"💡 Answer:\n{result['answer']}\n\n"
            output_str += f"🎯 Confidence: {result['confidence']}\n\n"
            output_str += f"📚 Sources Used ({len(result['sources'])}):\n"
            for i, source_item in enumerate(result['sources'], 1):
                filename = source_item.get('metadata', {}).get('document_filename', 'Unknown')
                output_str += f"\n{i}. 📄 {filename}\n"
                output_str += f"   📝 Excerpt: {source_item['content'][:150]}...\n"
                output_str += f"   📊 Relevance: {source_item['score']:.3f}\n"
            return output_str
        else:
            return f"❌ {result.get('error', 'Failed to answer question')}"
    except Exception as e:
        return f"❌ Error: {str(e)}"

def delete_document_from_library(document_id):
    if not document_id:
        doc_list_current = get_document_list()
        doc_choices_current = get_document_choices()
        return (
            "No document selected to delete.",
            doc_list_current,
            gr.update(choices=doc_choices_current),  # delete_doc_dropdown_visible
            gr.update(choices=doc_choices_current),  # doc_dropdown_content
            gr.update(choices=doc_choices_current),  # doc_dropdown_tag_visible
            gr.update(choices=doc_choices_current)   # podcast_doc_selector (ADD THIS!)
        )
    try:
        delete_doc_store_result = mcp_server.run_async(mcp_server.document_store.delete_document(document_id))
        delete_vec_store_result = mcp_server.run_async(mcp_server.vector_store.delete_document(document_id))

        msg = ""
        if delete_doc_store_result:
            msg += f"🗑️ Document {document_id[:8]}... deleted from document store. "
        else:
            msg += f"❌ Failed to delete document {document_id[:8]}... from document store. "
        
        if delete_vec_store_result:
             msg += "Embeddings deleted from vector store."
        else:
             msg += "Failed to delete embeddings from vector store (or no embeddings existed)."

        doc_list_updated = get_document_list()
        doc_choices_updated = get_document_choices()
        return (
            msg,
            doc_list_updated,
            gr.update(choices=doc_choices_updated),  # delete_doc_dropdown_visible
            gr.update(choices=doc_choices_updated),  # doc_dropdown_content
            gr.update(choices=doc_choices_updated),  # doc_dropdown_tag_visible
            gr.update(choices=doc_choices_updated)   # podcast_doc_selector (ADD THIS!)
        )
    except Exception as e:
        logger.error(f"Error deleting document: {str(e)}")
        doc_list_error = get_document_list()
        doc_choices_error = get_document_choices()
        return (
            f"❌ Error deleting document: {str(e)}",
            doc_list_error,
            gr.update(choices=doc_choices_error),  # delete_doc_dropdown_visible
            gr.update(choices=doc_choices_error),  # doc_dropdown_content
            gr.update(choices=doc_choices_error),  # doc_dropdown_tag_visible
            gr.update(choices=doc_choices_error)   # podcast_doc_selector (ADD THIS!)
        )

# Voice conversation state
voice_conversation_state = {
    "session_id": None,
    "active": False,
    "transcript": []
}

# voice_conversation_state = {
#     "session_id": None,
#     "active": False,
#     "transcript": []
# }

# def start_voice_conversation():
#     """
#     Start a new voice conversation session
    
#     Returns:
#         Tuple of (status_message, start_button_state, stop_button_state, chatbot_history)
#     """
#     try:
#         # Check if service is available
#         if not mcp_server.elevenlabs_service.is_available():
#             return (
#                 "⚠️ Voice assistant not configured.\n\n"
#                 "**Setup Instructions:**\n"
#                 "1. Get API key from: https://elevenlabs.io/app/settings/api-keys\n"
#                 "2. Create an agent at: https://elevenlabs.io/app/conversational-ai\n"
#                 "3. Add to .env file:\n"
#                 "   - ELEVENLABS_API_KEY=your_api_key\n"
#                 "   - ELEVENLABS_AGENT_ID=your_agent_id\n"
#                 "4. Restart the application",
#                 gr.update(interactive=True),   # start button enabled
#                 gr.update(interactive=False),  # stop button disabled
#                 []
#             )
        
#         # Create new session
#         session_id = str(uuid.uuid4())
#         result = mcp_server.run_async(
#             mcp_server.elevenlabs_service.start_conversation(session_id)
#         )
        
#         if result.get("success"):
#             voice_conversation_state["session_id"] = session_id
#             voice_conversation_state["active"] = True
#             voice_conversation_state["transcript"] = []
            
#             # Initialize chatbot with welcome message
#             initial_message = {
#                 "role": "assistant",
#                 "content": "👋 Hello! I'm your AI librarian. Ask me anything about your documents!"
#             }
            
#             return (
#                 "✅ Voice assistant is ready!\n\n"
#                 "You can now ask questions about your uploaded documents.",
#                 gr.update(interactive=False),  # start button disabled
#                 gr.update(interactive=True),   # stop button enabled
#                 [initial_message]
#             )
#         else:
#             error_msg = result.get("error", "Unknown error")
#             return (
#                 f"❌ Failed to start: {error_msg}\n\n"
#                 "**Troubleshooting:**\n"
#                 "• Check your API key is valid\n"
#                 "• Verify agent ID is correct\n"
#                 "• Check internet connection",
#                 gr.update(interactive=True),
#                 gr.update(interactive=False),
#                 []
#             )
#     except Exception as e:
#         logger.error(f"Error starting voice conversation: {str(e)}", exc_info=True)
#         return (
#             f"❌ Error: {str(e)}",
#             gr.update(interactive=True),
#             gr.update(interactive=False),
#             []
#         )

# def stop_voice_conversation():
#     """
#     Stop active voice conversation
    
#     Returns:
#         Tuple of (status_message, start_button_state, stop_button_state, chatbot_history)
#     """
#     try:
#         if not voice_conversation_state["active"]:
#             return (
#                 "ℹ️ No active conversation",
#                 gr.update(interactive=True),
#                 gr.update(interactive=False),
#                 voice_conversation_state["transcript"]
#             )
        
#         session_id = voice_conversation_state["session_id"]
#         if session_id:
#             mcp_server.run_async(
#                 mcp_server.elevenlabs_service.end_conversation(session_id)
#             )
        
#         # Get conversation stats
#         message_count = len(voice_conversation_state["transcript"])
        
#         voice_conversation_state["active"] = False
#         voice_conversation_state["session_id"] = None
        
#         return (
#             f"✅ Conversation ended\n\n"
#             f"📊 Stats: {message_count} messages exchanged",
#             gr.update(interactive=True),
#             gr.update(interactive=False),
#             voice_conversation_state["transcript"]
#         )
#     except Exception as e:
#         logger.error(f"Error stopping conversation: {str(e)}")
#         return (
#             f"❌ Error: {str(e)}",
#             gr.update(interactive=True),
#             gr.update(interactive=False),
#             voice_conversation_state["transcript"]
#         )

# def send_voice_message_v6(message, chat_history):
#     """
#     Send message in voice conversation - Gradio 6+ format
    
#     Args:
#         message: User's text message
#         chat_history: Current chat history (list of message dicts)
    
#     Returns:
#         Tuple of (updated_chat_history, cleared_input_box)
#     """
#     try:
#         # Validate state
#         if not voice_conversation_state["active"]:
#             chat_history.append({
#                 "role": "assistant",
#                 "content": "⚠️ Please start a conversation first by clicking 'Start Conversation'"
#             })
#             return chat_history, ""
        
#         # Validate input
#         if not message or not message.strip():
#             return chat_history, message
        
#         session_id = voice_conversation_state["session_id"]
        
#         # Add user message to display
#         chat_history.append({
#             "role": "user",
#             "content": message
#         })
        
#         # Show typing indicator
#         chat_history.append({
#             "role": "assistant",
#             "content": "🤔 Thinking..."
#         })
        
#         # Get AI response
#         result = mcp_server.run_async(
#             mcp_server.voice_tool.voice_qa(message, session_id)
#         )
        
#         # Remove typing indicator
#         chat_history = chat_history[:-1]
        
#         # Add response
#         if result.get("success"):
#             answer = result.get("answer", "No response")
            
#             # Add helpful context if RAG was used
#             if "document" in answer.lower() or "file" in answer.lower():
#                 footer = "\n\n💡 *Answer based on your documents*"
#             else:
#                 footer = ""
            
#             chat_history.append({
#                 "role": "assistant",
#                 "content": answer + footer
#             })
#         else:
#             error_msg = result.get("error", "Unknown error")
#             chat_history.append({
#                 "role": "assistant",
#                 "content": f"❌ Error: {error_msg}\n\n"
#                           "**Suggestions:**\n"
#                           "• Try rephrasing your question\n"
#                           "• Make sure you have uploaded relevant documents\n"
#                           "• Check if the question is about your document library"
#             })
        
#         # Update conversation state
#         voice_conversation_state["transcript"] = chat_history
        
#         return chat_history, ""
        
#     except Exception as e:
#         logger.error(f"Error in voice message: {str(e)}", exc_info=True)
        
#         # Remove typing indicator if present
#         if chat_history and chat_history[-1]["role"] == "assistant" and "Thinking" in chat_history[-1]["content"]:
#             chat_history = chat_history[:-1]
        
#         chat_history.append({
#             "role": "assistant",
#             "content": f"❌ An error occurred: {str(e)}\n\nPlease try again."
#         })
#         return chat_history, ""

# def test_voice_connection():
#     """
#     Test voice assistant connection
    
#     Returns:
#         Status message with test results
#     """
#     try:
#         result = mcp_server.run_async(
#             mcp_server.voice_tool.test_connection()
#         )
        
#         if result.get("success"):
#             return (
#                 "✅ **Connection Test Passed**\n\n"
#                 f"• API Status: Connected\n"
#                 f"• Voices Available: {result.get('voices_available', 0)}\n"
#                 f"• RAG Tool: {'✓ Working' if result.get('rag_tool_working') else '✗ Failed'}\n"
#                 f"• Client Tools: {'✓ Registered' if result.get('client_tools_registered') else '✗ Not Registered'}\n\n"
#                 "🎉 Voice assistant is ready to use!"
#             )
#         else:
#             return (
#                 "❌ **Connection Test Failed**\n\n"
#                 f"Error: {result.get('message', 'Unknown error')}\n\n"
#                 "**Troubleshooting:**\n"
#                 "1. Verify ELEVENLABS_API_KEY in .env\n"
#                 "2. Check ELEVENLABS_AGENT_ID is set\n"
#                 "3. Ensure API key is valid\n"
#                 "4. Check internet connection"
#             )
#     except Exception as e:
#         logger.error(f"Connection test error: {str(e)}")
#         return (
#             f"❌ **Test Error**\n\n{str(e)}\n\n"
#             "Please check your configuration and try again."
#         )

# def get_conversation_stats():
#     """
#     Get statistics about current conversation
    
#     Returns:
#         Formatted stats string
#     """
#     try:
#         if not voice_conversation_state["active"]:
#             return "ℹ️ No active conversation"
        
#         transcript = voice_conversation_state["transcript"]
#         user_msgs = sum(1 for msg in transcript if msg["role"] == "user")
#         ai_msgs = sum(1 for msg in transcript if msg["role"] == "assistant")
        
#         return (
#             "📊 **Conversation Statistics**\n\n"
#             f"• Session ID: {voice_conversation_state['session_id'][:8]}...\n"
#             f"• Your messages: {user_msgs}\n"
#             f"• AI responses: {ai_msgs}\n"
#             f"• Total exchanges: {user_msgs}\n"
#             f"• Status: {'🟢 Active' if voice_conversation_state['active'] else '🔴 Inactive'}"
#         )
#     except Exception as e:
#         logger.error(f"Error getting stats: {str(e)}")
#         return f"❌ Error: {str(e)}"

def generate_podcast_ui(doc_ids, style, duration, voice1, voice2):
    """UI wrapper for podcast generation (EXISTING FUNCTION - keep as is)"""
    try:
        logger.info(f"generate_podcast_ui called with:")
        logger.info(f"  doc_ids: {doc_ids} (type: {type(doc_ids)})")
        logger.info(f"  style: {style}")
        logger.info(f"  duration: {duration}")
        logger.info(f"  voice1: {voice1}")
        logger.info(f"  voice2: {voice2}")
        
        if doc_ids is None:
            logger.warning("doc_ids is None")
            return ("⚠️ Please select at least one document", None, "No documents selected", "")
        
        if isinstance(doc_ids, str):
            logger.info(f"Converting string doc_id to list: {doc_ids}")
            doc_ids = [doc_ids]
        
        if not doc_ids or len(doc_ids) == 0:
            logger.warning(f"doc_ids is empty or has length 0: {doc_ids}")
            return ("⚠️ Please select at least one document", None, "No documents selected", "")
        
        doc_ids = [doc_id for doc_id in doc_ids if doc_id and doc_id.strip()]
        
        if not doc_ids:
            logger.warning("After filtering, no valid doc_ids remain")
            return ("⚠️ Please select at least one document", None, "No documents selected", "")
        
        logger.info(f"Generating podcast with {len(doc_ids)} valid documents: {doc_ids}")
        
        result = mcp_server.run_async(
            mcp_server.generate_podcast_async(
                document_ids=doc_ids,
                style=style,
                duration_minutes=int(duration),
                host1_voice=voice1,
                host2_voice=voice2
            )
        )
        
        logger.info(f"Podcast generation result: success={result.get('success')}")
        
        if result.get("success"):
            audio_file = result.get("audio_file")
            transcript = result.get("transcript", "Transcript not available")
            message = result.get("message", "Podcast generated!")
            formatted_transcript = f"## Podcast Transcript\n\n{transcript}"
            
            logger.info(f"Podcast generated successfully: {audio_file}")
            
            return (
                f"✅ {message}",
                audio_file,
                formatted_transcript,
                result.get("podcast_id", "")
            )
        else:
            error = result.get("error", "Unknown error")
            logger.error(f"Podcast generation failed: {error}")
            return (f"❌ Error: {error}", None, "Generation failed", "")
    except Exception as e:
        logger.error(f"Podcast UI error: {str(e)}", exc_info=True)
        return (f"❌ Error: {str(e)}", None, "An error occurred", "")

def load_dashboard_stats():
    """Load dashboard statistics for the UI"""
    try:
        docs_result = mcp_server.list_documents_sync(limit=1000)
        doc_count = 0
        total_chunks = 0
        total_size = 0
        recent_data = []
        
        if docs_result.get("success"):
            documents = docs_result.get("documents", [])
            doc_count = len(documents)
            total_chunks = sum(doc.get("metadata", {}).get("chunk_count", 0) for doc in documents)
            total_size = sum(doc.get("file_size", 0) for doc in documents)
            storage_mb = round(total_size / (1024 * 1024), 2) if total_size > 0 else 0.0
            
            recent = documents[:5]
            recent_data = [
                [
                    doc.get("filename", "Unknown"), 
                    doc.get("doc_type", "unknown"), 
                    doc.get("created_at", "")[:10] if doc.get("created_at") else "N/A", 
                    f"{doc.get('file_size', 0)} bytes"
                ]
                for doc in recent
            ]
        else:
            storage_mb = 0.0
        
        vector_stat = "✅ Online" if getattr(mcp_server, "vector_store", None) else "❌ Offline"
        llm_stat = "✅ Ready" if getattr(mcp_server, "llm_service", None) else "❌ Offline"
        voice_stat = "✅ Ready" if (getattr(mcp_server, "elevenlabs_service", None) and mcp_server.elevenlabs_service.is_available()) else "⚠️ Configure API Key"
        
        return (
            doc_count,
            total_chunks,
            storage_mb,
            recent_data,
            vector_stat,
            llm_stat,
            voice_stat,
        )
    except Exception as e:
        logger.error(f"Error loading dashboard stats: {str(e)}")
        return (0, 0, 0.0, [], "❌ Error", "❌ Error", "❌ Error")

# ADD THESE HELPER FUNCTIONS BEFORE create_gradio_interface():
def load_podcast_library_ui():
    """Load and display podcast library with audio players"""
    try:
        result = mcp_server.list_podcasts_sync(limit=50)
        
        if not result.get("success"):
            return (
                gr.Column(visible=False),
                f"❌ Failed to load podcasts: {result.get('error', 'Unknown error')}"
            )
        
        podcasts = result.get("podcasts", [])
        
        if not podcasts:
            return (
                gr.Column(visible=False),
                "📭 No podcasts generated yet. Create your first podcast above!"
            )
        
        # Build the library UI dynamically
        with gr.Column(visible=True) as library_col:
            for idx, podcast in enumerate(podcasts, 1):
                with gr.Group():
                    with gr.Row():
                        # Column 1: Podcast Info (40%)
                        with gr.Column(scale=2):
                            gr.Markdown(f"### 🎙️ Podcast #{idx}")
                            
                            # Extract document names from metadata
                            doc_names = []
                            doc_ids = []  # Initialize here to avoid reference error
                            
                            if podcast.get("metadata"):
                                doc_ids = podcast["metadata"].get("document_ids", [])
                                # Get document names
                                for doc_id in doc_ids:
                                    try:
                                        doc = mcp_server.run_async(
                                            mcp_server.document_store.get_document(doc_id)
                                        )
                                        if doc:
                                            doc_names.append(doc.filename)
                                    except Exception as e:
                                        logger.warning(f"Could not fetch document {doc_id}: {e}")
                                        doc_names.append(f"Doc {doc_id[:8]}...")
                            
                            # Display document names
                            if doc_names:
                                gr.Markdown(f"**📄 Documents:** {', '.join(doc_names)}")
                            else:
                                doc_count = len(doc_ids) if doc_ids else 'N/A'
                                gr.Markdown(f"**📄 Documents:** {doc_count} document(s)")
                            
                            # Podcast metadata
                            style = podcast.get("metadata", {}).get("style", "Unknown")
                            duration = podcast.get("metadata", {}).get("duration_minutes", "N/A")
                            created = podcast.get("created_at", "Unknown")[:19] if podcast.get("created_at") else "Unknown"
                            # Safe ID handling
                            podcast_id = podcast.get("id") or podcast.get("podcast_id") or "Unknown"
                            if isinstance(podcast_id, str) and len(podcast_id) > 16:
                                display_id = f"{podcast_id[:16]}..."
                            else:
                                display_id = podcast_id
                            
                            gr.Markdown(
                                f"**🎨 Style:** {style.title()}  \n"
                                f"**⏱️ Duration:** ~{duration} min  \n"
                                f"**📅 Created:** {created}  \n"
                                f"**🆔 ID:** `{display_id}`"
                            )

                        
                        # Column 2: Audio Player (60%)
                        with gr.Column(scale=3):
                            audio_file = podcast.get("audio_file")
                            
                            if audio_file and os.path.exists(audio_file):
                                gr.Audio(
                                    value=audio_file,
                                    type="filepath",
                                    interactive=False,
                                    show_label=False,
                                    show_download_button=True,
                                    waveform_options={"show_controls": True}
                                )
                            else:
                                gr.Markdown("⚠️ *Audio file not found*")
                                if audio_file:
                                    gr.Markdown(f"*Expected path: {audio_file}*")
                    
                    # Optional: Show transcript in accordion
                    with gr.Accordion(f"📝 View Transcript", open=False):
                        transcript = podcast.get("transcript", "Transcript not available")
                        gr.Markdown(transcript)
        
        status_msg = f"✅ Loaded {len(podcasts)} podcast{'s' if len(podcasts) != 1 else ''}"
        return library_col, status_msg
        
    except Exception as e:
        logger.error(f"Error loading podcast library: {str(e)}", exc_info=True)
        return (
            gr.Column(visible=False),
            f"❌ Error loading library: {str(e)}"
        )

def create_gradio_interface():
    custom_theme = gr.themes.Soft(
        primary_hue=gr.themes.colors.indigo,
        secondary_hue=gr.themes.colors.blue,
        neutral_hue=gr.themes.colors.slate,
        font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"],
        font_mono=[gr.themes.GoogleFont("Fira Code"), "monospace"],
    ).set(
        button_primary_background_fill="*primary_500",
        button_primary_background_fill_hover="*primary_600",
        block_title_text_weight="600",
        block_label_text_size="sm",
        block_label_text_weight="500",
    )
    
    # Force dark theme using JavaScript
    js_func = """
    function refresh() {
        const url = new URL(window.location);
        if (url.searchParams.get('__theme') !== 'dark') {
            url.searchParams.set('__theme', 'dark');
            window.location.href = url.href;
        }
    }
    """
    
    url = "https://huggingface.co/static-proxy/cdn-uploads.huggingface.co/production/uploads/66f1712d906c08084995f808/TSJexR45eNpUjHhbHDOag.png"
    resp = requests.get(url)
    img = Image.open(BytesIO(resp.content))
    
    with gr.Blocks(title="🧠 AI Digital Library Assistant", theme=custom_theme, js=js_func) as interface:
        with gr.Tabs():
            with gr.Tab("🏠 Dashboard"):
                gr.Markdown("""
                    # 🧠 AI Digital Library Assistant
                    ## Your Intelligent Document Management Platform
                    > **MCP 1st Birthday Hackathon Submission**  
                    > Transform documents into searchable knowledge • Generate AI podcasts • Voice Q&A • Smart tagging
                    ### 🚀 Quick Start Guide
                    1. **Upload** documents (PDF, Word, images) in the *Upload Documents* tab
                    2. **Search** your library using natural language in the *Search* tab  
                    3. **Ask questions** and get AI answers with sources in the *Ask Questions* tab
                    4. **Create content** with summarization, outlines, and more in *Content Studio*
                    5. **Generate podcasts** from your documents in the *Podcast Studio*
                    ### 👤 Author  
                    **Hugging Face ID:** [@Nihal2000](https://huggingface.co/Nihal2000)
                    ---
                    """)
                gr.Image(value=img,show_label=False)
                
                gr.Markdown("## 📊 Quick Stats")
                with gr.Row():
                    total_docs = gr.Number(
                        label="📚 Total Documents",
                        value=0,
                        interactive=False,
                        container=True
                    )
                    total_chunks = gr.Number(
                        label="🧩 Vector Chunks",
                        value=0,
                        interactive=False,
                        container=True
                    )
                    storage_size = gr.Number(
                        label="💾 Storage (MB)",
                        value=0,
                        interactive=False,
                        container=True
                    )
                
                gr.Markdown("## 📊 Recent Activity")
                with gr.Group():
                    recent_docs = gr.Dataframe(
                        headers=["Document", "Type", "Date", "Size"],
                        datatype=["str", "str", "str", "str"],
                        row_count=(5, "fixed"),
                        col_count=(4, "fixed"),
                        interactive=False,
                        label="Recently Added Documents"
                    )
                
                gr.Markdown("## System Status")
                with gr.Row():
                    vector_status = gr.Textbox(
                        label="Vector Store",
                        value="✅ Online",
                        interactive=False,
                        container=True
                    )
                    llm_status = gr.Textbox(
                        label="LLM Service",
                        value="✅ Ready",
                        interactive=False,
                        container=True
                    )
                    voice_status = gr.Textbox(
                        label="Voice Service",
                        value="⚠️ Configure API Key",
                        interactive=False,
                        container=True
                    )
            
            with gr.Tab("📚 Document Library"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### Your Document Collection")
                        document_list_display = gr.Textbox(label="Documents in Library", value=get_document_list(), lines=20, interactive=False)
                        refresh_btn_library = gr.Button("🔄 Refresh Library", variant="secondary")
                        delete_doc_dropdown_visible = gr.Dropdown(label="Select Document to Delete", choices=get_document_choices(), value=None, interactive=True, allow_custom_value=False)
                        delete_btn = gr.Button("🗑️ Delete Selected Document", variant="stop")
                        delete_output_display = gr.Textbox(label="Delete Status", visible=True)
            
            with gr.Tab("📄 Upload Documents"):
                gr.Markdown("""
                ### 📁 Upload & Process Documents
                Upload PDFs, DOCX, TXT or Images. OCR runs automatically.  
                Once processed, the document becomes searchable and available for summaries, tagging, podcasting and RAG.
                """)

                with gr.Row(equal_height=True):
                    
                    # LEFT SIDE: UPLOAD PANEL
                    with gr.Column(scale=1):
                        with gr.Group():
                            gr.Markdown("#### 📤 Upload File")
                            
                            file_input_upload = gr.File(
                                label="Select a document",
                                file_types=[".pdf", ".txt", ".docx", ".png", ".jpg", ".jpeg"],
                                type="filepath",
                                show_label=False
                            )

                            gr.HTML("<div style='height:10px'></div>")  # spacing
                            
                            upload_btn_process = gr.Button(
                                "🚀 Process & Add to Library",
                                variant="primary",
                                size="lg",
                                elem_id="upload-btn"
                            )

                    # RIGHT SIDE: STATUS PANEL
                    with gr.Column(scale=1):
                        with gr.Group():
                            gr.Markdown("#### 📦 Processing Preview")

                            upload_output_display = gr.Textbox(
                                label="Processing Status",
                                placeholder="⏳ Waiting for upload...",
                                lines=10,
                                interactive=False,
                            )

                            doc_id_output_display = gr.Textbox(
                                label="📌 Document ID",
                                placeholder="Will appear after processing...",
                                interactive=False
                            )
                            

                # Optional: center button styling
                gr.HTML("""
                <style>
                    #upload-btn button {
                        width: 100%;
                        font-size: 18px;
                        padding: 14px;
                    }
                </style>
                """)


            
            with gr.Tab("📝 Content Studio"):
                gr.Markdown("""
                ### 🎨 Create & Analyze Content
                Transform documents with AI-powered tools: summarize, outline, explain, and more.
                """)
                
                with gr.Row():
                    with gr.Column(scale=2):
                        with gr.Group():
                            gr.Markdown("#### 📄 Content Source")
                            doc_dropdown_content = gr.Dropdown(
                                label="Select Document",
                                choices=get_document_choices(),
                                value=None,
                                interactive=True,
                                info="Choose a document from your library"
                            )
                            
                            gr.Markdown("**OR**")
                            
                            content_text_input = gr.Textbox(
                                label="Enter Text or Topic",
                                placeholder="Paste content or enter a topic...",
                                lines=4,
                                info="For outlines, enter a topic. For other tasks, paste text to analyze."
                            )
                        
                        with gr.Group():
                            gr.Markdown("#### 🛠️ Task Configuration")
                            task_dropdown = gr.Dropdown(
                                label="Select Task",
                                choices=[
                                    "Summarize", "Generate Outline", "Explain Concept",
                                    "Paraphrase", "Categorize", "Key Insights",
                                    "Generate Questions", "Extract Key Info"
                                ],
                                value="Summarize",
                                interactive=True,
                                info="Choose the type of analysis to perform"
                            )
                        
                        with gr.Accordion("⚙️ Advanced Options", open=False):
                            summary_style_opt = gr.Dropdown(
                                label="Summary Style",
                                choices=["concise", "detailed", "bullet_points", "executive"],
                                value="concise",
                                visible=True,
                                info="How detailed should the summary be?"
                            )
                            
                            outline_sections_opt = gr.Slider(
                                label="Number of Sections",
                                minimum=3, maximum=10, value=5, step=1,
                                visible=False,
                                info="How many main sections?"
                            )
                            outline_detail_opt = gr.Dropdown(
                                label="Detail Level",
                                choices=["brief", "medium", "detailed"],
                                value="medium",
                                visible=False
                            )
                            
                            explain_audience_opt = gr.Dropdown(
                                label="Target Audience",
                                choices=["general", "technical", "beginner", "expert"],
                                value="general",
                                visible=False,
                                info="Who is this explanation for?"
                            )
                            explain_length_opt = gr.Dropdown(
                                label="Length",
                                choices=["brief", "medium", "detailed"],
                                value="medium",
                                visible=False
                            )
                            
                            paraphrase_style_opt = gr.Dropdown(
                                label="Style",
                                choices=["formal", "casual", "academic", "simple", "technical"],
                                value="formal",
                                visible=False,
                                info="Writing style for paraphrasing"
                            )
                            
                            categories_input_opt = gr.Textbox(
                                label="Categories (comma separated)",
                                placeholder="Technology, Business, Science...",
                                visible=False
                            )
                            
                            num_items_opt = gr.Slider(
                                label="Number of Items",
                                minimum=1, maximum=10, value=5, step=1,
                                visible=False
                            )
                            question_type_opt = gr.Dropdown(
                                label="Question Type",
                                choices=["comprehension", "analysis", "application", "creative", "factual"],
                                value="comprehension",
                                visible=False
                            )
                        
                        run_task_btn = gr.Button("🚀 Run Task", variant="primary", size="lg")
                    
                    with gr.Column(scale=3):
                        with gr.Group():
                            gr.Markdown("#### 📊 Result")
                            content_output_display = gr.Textbox(
                                label="",
                                lines=25,
                                placeholder="Results will appear here...",
                                show_copy_button=True,
                                container=False
                            )

                task_dropdown.change(
                    fn=update_options_visibility,
                    inputs=[task_dropdown],
                    outputs=[
                        summary_style_opt, outline_sections_opt, outline_detail_opt,
                        explain_audience_opt, explain_length_opt, paraphrase_style_opt,
                        categories_input_opt, num_items_opt, question_type_opt
                    ]
                )
                
                run_task_btn.click(
                    fn=execute_content_task,
                    inputs=[
                        task_dropdown, doc_dropdown_content, content_text_input,
                        summary_style_opt, outline_sections_opt, outline_detail_opt,
                        explain_audience_opt, explain_length_opt, paraphrase_style_opt,
                        categories_input_opt, num_items_opt, question_type_opt
                    ],
                    outputs=[content_output_display]
                )

            # with gr.Tab("🎙️ Voice Assistant"):
            #     # Simple header
            #     gr.Markdown("### Ask questions about your documents using AI")
                
            #     with gr.Row():
            #         # Compact left sidebar (25% width)
            #         with gr.Column(scale=1):
            #             # Status box
            #             voice_status_display = gr.Textbox(
            #                 label="Status",
            #                 value="Click 'Start' to begin",
            #                 interactive=False,
            #                 lines=3,
            #                 max_lines=3
            #             )
                        
            #             # Control buttons stacked vertically
            #             start_voice_btn = gr.Button(
            #                 "🎤 Start",
            #                 variant="primary",
            #                 size="lg"
            #             )
                        
            #             stop_voice_btn = gr.Button(
            #                 "⏹️ Stop",
            #                 variant="stop",
            #                 size="lg",
            #                 interactive=False
            #             )
                        
            #             test_connection_btn = gr.Button(
            #                 "🔧 Test",
            #                 variant="secondary",
            #                 size="sm"
            #             )
                        
            #             gr.Markdown("---")
                        
            #             # Quick tips
            #             gr.Markdown("""
            #             **Quick Tips:**
            #             • Upload documents first
            #             • Ask specific questions
            #             • Press Enter to send
            #             """, elem_classes=["small-text"])
                    
            #         # Main chat area (75% width)
            #         with gr.Column(scale=3):
            #             # Large chat window
            #             voice_chatbot = gr.Chatbot(
            #                 type="messages",
            #                 height=550,
            #                 show_copy_button=True,
            #                 avatar_images=(None, "🤖"),
            #                 show_label=False,
            #                 container=True,
            #                 bubble_full_width=False
            #             )
                        
            #             # Input row
            #             with gr.Row():
            #                 voice_input_text = gr.Textbox(
            #                     placeholder="Ask me anything about your documents...",
            #                     lines=2,
            #                     max_lines=4,
            #                     scale=4,
            #                     show_label=False,
            #                     container=False,
            #                     autofocus=True
            #                 )
            #                 send_voice_btn = gr.Button(
            #                     "Send",
            #                     scale=1,
            #                     variant="primary"
            #                 )
                        
            #             # Footer actions
            #             with gr.Row():
            #                 clear_chat_btn = gr.Button("Clear", size="sm")
            #                 with gr.Column(scale=3):
            #                     gr.Markdown("*Tip: Type your question and press Enter*")
                
            #     # Event handlers
            #     start_voice_btn.click(
            #         fn=start_voice_conversation,
            #         outputs=[voice_status_display, start_voice_btn, stop_voice_btn, voice_chatbot]
            #     )
                
            #     stop_voice_btn.click(
            #         fn=stop_voice_conversation,
            #         outputs=[voice_status_display, start_voice_btn, stop_voice_btn, voice_chatbot]
            #     )
                
            #     send_voice_btn.click(
            #         fn=send_voice_message_v6,
            #         inputs=[voice_input_text, voice_chatbot],
            #         outputs=[voice_chatbot, voice_input_text]
            #     )
                
            #     voice_input_text.submit(
            #         fn=send_voice_message_v6,
            #         inputs=[voice_input_text, voice_chatbot],
            #         outputs=[voice_chatbot, voice_input_text]
            #     )
                
            #     clear_chat_btn.click(
            #         fn=lambda: [],
            #         outputs=[voice_chatbot]
            #     )
                
            #     test_connection_btn.click(
            #         fn=test_voice_connection,
            #         outputs=[voice_status_display]
            #     )

            with gr.Tab("🎧 Podcast Studio"):
                gr.Markdown("""
                    # 🎙️ AI Podcast Studio
                    ## Transform Documents into Engaging Audio
                    Convert your documents into professional podcast conversations with AI-generated voices.
                    ### How It Works:
                    1. **Select Documents** - Choose 1-5 documents from your library
                    2. **Choose Style** - Pick conversation style (casual, educational, etc.)
                    3. **Set Duration** - Select podcast length (5-30 minutes)
                    4. **Select Voices** - Pick two AI hosts from available voices
                    5. **Generate** - AI creates natural dialogue discussing your content
                    ### Powered By:
                    - 🎵 **ElevenLabs AI** - Ultra-realistic voice synthesis
                    - 🤖 **LLM** - Intelligent content analysis and script generation
                    - 📚 **RAG** - Context-aware information retrieval
                    ---
                    """)
    
                with gr.Row():
                    with gr.Column(scale=2):
                        # Configuration Panel
                        with gr.Group():
                            gr.Markdown("#### 📚 Select Content")
                            
                            podcast_doc_selector = gr.CheckboxGroup(
                                choices=get_document_choices(),
                                label="Documents to Include",
                                info="Choose 1-5 documents for best results",
                                interactive=True,
                                value=[]
                            )
                            
                            gr.Markdown("*Selected document IDs will be used for podcast generation*")
                        
                        with gr.Accordion("🎨 Podcast Settings", open=True):
                            with gr.Row():
                                podcast_style = gr.Dropdown(
                                    label="Style",
                                    choices=["conversational", "educational", "technical", "casual"],
                                    value="conversational",
                                    info="Sets the tone and format"
                                )
                                
                                podcast_duration = gr.Slider(
                                    label="Duration (minutes)",
                                    minimum=5,
                                    maximum=30,
                                    value=10,
                                    step=5,
                                    info="Approximate length"
                                )
                            
                            gr.Markdown("#### 🗣️ Voice Selection")
                            with gr.Row():
                                host1_voice_selector = gr.Dropdown(
                                    label="Host 1",
                                    choices=["Rachel", "Adam", "Domi", "Bella", "Antoni", "Elli", "Josh"],
                                    value="Rachel"
                                )
                                host2_voice_selector = gr.Dropdown(
                                    label="Host 2",
                                    choices=["Adam", "Rachel", "Josh", "Sam", "Emily", "Antoni", "Arnold"],
                                    value="Adam"
                                )
                        
                        generate_podcast_btn = gr.Button(
                            "🎙️ Generate Podcast",
                            variant="primary",
                            size="lg"
                        )
                        
                        podcast_status = gr.Textbox(
                            label="Status",
                            interactive=False,
                            lines=2
                        )
                        
                        podcast_id_display = gr.Textbox(
                            label="Podcast ID",
                            interactive=False,
                            visible=False
                        )
                    
                    with gr.Column(scale=3):
                        # Output Panel - Latest Generated
                        with gr.Group():
                            gr.Markdown("#### 🎵 Latest Generated Podcast")
                            
                            podcast_audio_player = gr.Audio(
                                type="filepath",
                                interactive=False,
                                autoplay=True,
                                show_label=False
                            )
                        
                        with gr.Accordion("📝 Transcript", open=False):
                            podcast_transcript_display = gr.Markdown(
                                value="*Transcript will appear after generation...*"
                            )
                
                podcast_library_container = gr.Column()
                with gr.Row():
                    refresh_podcast_library_btn = gr.Button("🔄 Refresh Library", variant="secondary")
                    podcast_library_status = gr.Textbox(
                        label="Library Status",
                        value="Click 'Refresh Library' to load podcasts",
                        interactive=False,
                        scale=3
                    )

                # Event handler for generate button
                generate_podcast_btn.click(
                    fn=generate_podcast_ui,
                    inputs=[
                        podcast_doc_selector,
                        podcast_style,
                        podcast_duration,
                        host1_voice_selector,
                        host2_voice_selector
                    ],
                    outputs=[
                        podcast_status,
                        podcast_audio_player,
                        podcast_transcript_display,
                        podcast_id_display
                    ]
                ).then(
                    # Auto-refresh library after generation
                    fn=load_podcast_library_ui,
                    outputs=[podcast_library_container, podcast_library_status]
                )
                
                # NEW SECTION: Podcast Library (at tab level, same as first gr.Row())
                gr.Markdown("---")
                gr.Markdown("### 📚 Podcast Library")
                gr.Markdown("Browse and play all your generated podcasts")
                                
                # Event handler for refresh button
                refresh_podcast_library_btn.click(
                    fn=load_podcast_library_ui,
                    outputs=[podcast_library_container, podcast_library_status]
                )
                
            with gr.Tab("❓ Ask Questions"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("""### Ask Questions About Your Documents
                        The AI will search through all your uploaded documents to find relevant information 
                        and provide comprehensive answers with sources.""")
                        qa_question_input = gr.Textbox(label="Your Question", placeholder="Ask anything about your documents...", lines=3)
                        qa_btn_action = gr.Button("❓ Get Answer", variant="primary", size="lg")
                    with gr.Column():
                        qa_output_display = gr.Textbox(label="AI Answer", lines=20, placeholder="Answer will appear here with sources...")
            
            with gr.Tab("🏷️ Generate Tags"):
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### Generate Document Tags")
                        doc_dropdown_tag_visible = gr.Dropdown(label="Select Document to Tag", choices=get_document_choices(), value=None, interactive=True, allow_custom_value=False)
                        tag_text_input = gr.Textbox(label="Or Paste Text to Generate Tags", placeholder="Paste any text here to generate tags...", lines=8)
                        max_tags_slider = gr.Slider(label="Number of Tags", minimum=3, maximum=15, value=5, step=1)
                        tag_btn_action = gr.Button("🏷️ Generate Tags", variant="primary", size="lg")
                    with gr.Column():
                        tag_output_display = gr.Textbox(label="Generated Tags", lines=10, placeholder="Tags will appear here...")

            with gr.Tab("🔍 Search Documents"):
                gr.Markdown("""
                ### 🔎 Semantic Search
                Find relevant content across your entire document library using AI-powered semantic search.
                """)
                
                with gr.Row():
                    with gr.Column(scale=1):
                        with gr.Group():
                            search_query_input = gr.Textbox(
                                label="Search Query",
                                placeholder="What are you looking for?",
                                lines=2,
                                info="Use natural language to describe what you need"
                            )
                            
                            with gr.Accordion("🎛️ Search Options", open=False):
                                search_top_k_slider = gr.Slider(
                                    label="Number of Results",
                                    minimum=1, maximum=20, value=5, step=1,
                                    info="More results = broader search"
                                )
                            
                            search_btn_action = gr.Button("🔍 Search", variant="primary", size="lg")
                    
                    with gr.Column(scale=2):
                        with gr.Group():
                            search_output_display = gr.Textbox(
                                label="Results",
                                lines=20,
                                placeholder="Search results will appear here...",
                                show_copy_button=True
                            )

        all_dropdowns_to_update = [delete_doc_dropdown_visible, doc_dropdown_content,podcast_doc_selector]
        
        refresh_outputs = [document_list_display] + [dd for dd in all_dropdowns_to_update]
        refresh_btn_library.click(fn=refresh_library, outputs=refresh_outputs)
        
        upload_outputs = [upload_output_display, doc_id_output_display, document_list_display] + [dd for dd in all_dropdowns_to_update]
        upload_btn_process.click(upload_and_process_file, inputs=[file_input_upload], outputs=upload_outputs)

        delete_outputs = [delete_output_display, document_list_display] + [dd for dd in all_dropdowns_to_update]
        delete_btn.click(delete_document_from_library, inputs=[delete_doc_dropdown_visible], outputs=delete_outputs)
        
        search_btn_action.click(perform_search, inputs=[search_query_input, search_top_k_slider], outputs=[search_output_display])
        tag_btn_action.click(generate_tags_for_document, inputs=[doc_dropdown_tag_visible, tag_text_input, max_tags_slider], outputs=[tag_output_display])
        qa_btn_action.click(ask_question, inputs=[qa_question_input], outputs=[qa_output_display])

        interface.load(
            fn=load_dashboard_stats,
            outputs=[total_docs, total_chunks, storage_size, recent_docs, vector_status, llm_status, voice_status]
        )
        
        interface.load(fn=refresh_library, outputs=refresh_outputs)
        return interface           

if __name__ == "__main__":
    gradio_interface = create_gradio_interface()
    gradio_interface.launch(mcp_server=True)