import streamlit as st
from typing import Dict, List, Optional
from datetime import datetime
from pathlib import Path
import json
import os
from utils.legal_prompt_generator import LegalPromptGenerator
from anthropic import Anthropic

class LegalNotebookInterface:

    def __init__(self, case_manager, vector_store, document_processor):
        """Initialize LegalNotebookInterface with required components."""
        # Store the components as instance attributes
        self.case_manager = case_manager
        self.vector_store = vector_store
        self.document_processor = document_processor
        self.prompt_generator = LegalPromptGenerator()
        
        # Initialize session state
        if "chat_history" not in st.session_state:
            st.session_state.chat_history = []
        if "current_case" not in st.session_state:
            st.session_state.current_case = None
        if "selected_documents" not in st.session_state:
            st.session_state.selected_documents = []
        if "active_citation" not in st.session_state:
            st.session_state.active_citation = None

        # Add styling for notebook-like interface
        st.markdown("""
            <style>
            .main-content {
                max-width: 900px;
                margin: 0 auto;
            }
            .citation-number {
                display: inline-flex;
                align-items: center;
                justify-content: center;
                width: 18px;
                height: 18px;
                background-color: #E8E8E8;
                color: #666;
                border-radius: 50%;
                font-size: 11px;
                cursor: pointer;
                margin: 0 2px;
                vertical-align: super;
                border: 1px solid #D0D0D0;
            }
            .citation-number:hover {
                background-color: #0066cc;
                color: white;
            }
            .source-preview {
                background: white;
                border: 1px solid #E0E0E0;
                border-radius: 8px;
                padding: 15px;
                margin-top: 10px;
                margin-bottom: 10px;
            }
            .source-header {
                font-size: 14px;
                color: #666;
                margin-bottom: 8px;
                border-bottom: 1px solid #E0E0E0;
                padding-bottom: 8px;
            }
            .highlighted-text {
                background-color: #FFF3CD;
                padding: 2px 4px;
                border-radius: 3px;
            }
            .chat-message {
                margin-bottom: 20px;
                padding: 10px;
                border-radius: 8px;
            }
            .user-message {
                background-color: #F8F9FA;
            }
            .assistant-message {
                background-color: white;
                border: 1px solid #E9ECEF;
            }
            </style>
            """, unsafe_allow_html=True)

    def _render_chat_interface(self):
        """Render the chat interface with notebook-style citations."""
        st.title("💬 Document Analysis")
        
        # Create two-column layout for chat and preview
        chat_col, preview_col = st.columns([2, 1])

        with chat_col:
            # Show context if needed
            if st.session_state.current_case:
                case = self.case_manager.get_case(st.session_state.current_case)
                documents = self.case_manager.list_documents(case['id'])
                selected_docs = [doc for doc in documents if doc['id'] in st.session_state.selected_documents]
                
                if selected_docs:
                    st.markdown(
                        f"""
                        *Analyzing {len(selected_docs)} selected document(s)*
                        """)
                else:
                    st.info("Please select documents from the sidebar")

            # Display chat history
            for msg_idx, message in enumerate(st.session_state.chat_history):
                if message["role"] == "user":
                    st.markdown(f"""<div class="chat-message user-message">
                        {message['content']}</div>""", unsafe_allow_html=True)
                else:
                    content = message["content"]
                    citations = message.get("citations", [])
                    
                    # Add citation numbers to content
                    for idx, citation in enumerate(citations, 1):
                        # Create unique identifier for this citation
                        citation_id = f"citation_{msg_idx}_{idx}"
                        # Add clickable number that will trigger preview
                        content = content.replace(
                            citation['text'][:50],
                            f"{citation['text'][:50]}<span class='citation-number' onclick='show_citation(\"{citation_id}\")'>{idx}</span>"
                        )
                    
                    st.markdown(f"""<div class="chat-message assistant-message">
                        {content}</div>""", unsafe_allow_html=True)

                    # Store citations in session state for preview
                    for idx, citation in enumerate(citations, 1):
                        citation_key = f"citation_{msg_idx}_{idx}"
                        st.session_state[citation_key] = citation

        # Preview panel
        with preview_col:
            if st.session_state.active_citation:
                citation = st.session_state.get(st.session_state.active_citation)
                if citation:
                    st.markdown("""<div class="source-preview">
                        <div class="source-header">
                            📄 Source Reference
                        </div>
                        <strong>{}</strong>
                        <div class="highlighted-text">{}</div>
                        <small>Section: {}</small>
                    </div>""".format(
                        citation['document'],
                        citation['text'],
                        citation['section']
                    ), unsafe_allow_html=True)

        # Add JavaScript for handling citation clicks
        st.markdown("""
            <script>
            function show_citation(citation_id) {
                // Update active citation in session state
                window.parent.postMessage({
                    type: 'streamlit:set_session_state',
                    data: { active_citation: citation_id }
                }, '*');
            }
            </script>
        """, unsafe_allow_html=True)

        # Chat input
        if prompt := st.chat_input(
            "Ask about your documents...",
            disabled=not st.session_state.selected_documents
        ):
            self._process_query(prompt)

    def render(self):
        """Main render method with sidebar controls."""
        self._render_sidebar()
        self._render_chat_interface()

    def _render_sidebar(self):
        """Render sidebar with case and document selection."""
        with st.sidebar:
            st.markdown("### 📁 Case Selection")
            
            cases = self.case_manager.get_all_cases()
            if not cases:
                st.info("No cases found. Please create a case first.")
                return

            selected_case = st.selectbox(
                "Select Case",
                cases,
                format_func=lambda x: x['title'],
                key="case_selector"
            )

            if selected_case:
                st.session_state.current_case = selected_case['id']
                documents = self.case_manager.list_documents(selected_case['id'])

                if documents:
                    st.markdown("### 📑 Document Selection")
                    
                    col1, col2 = st.columns([3, 1])
                    with col1:
                        st.markdown("#### Documents")
                    with col2:
                        if st.button("Select All"):
                            st.session_state.selected_documents = [doc['id'] for doc in documents]

                    for doc in documents:
                        doc_col, del_col = st.columns([4, 1])
                        with doc_col:
                            doc_selected = st.checkbox(
                                f"📄 {doc['title']}",
                                value=doc['id'] in st.session_state.selected_documents,
                                key=f"doc_{doc['id']}"
                            )
                        with del_col:
                            if st.button("🗑️", key=f"del_{doc['id']}", help="Delete document"):
                                try:
                                    self.vector_store.delete_document(doc['id'])
                                    self.case_manager.remove_document(selected_case['id'], doc['id'])
                                    if doc['id'] in st.session_state.selected_documents:
                                        st.session_state.selected_documents.remove(doc['id'])
                                    st.rerun()
                                except Exception as e:
                                    st.error(f"Error deleting document: {str(e)}")
                        
                        if doc_selected and doc['id'] not in st.session_state.selected_documents:
                            st.session_state.selected_documents.append(doc['id'])
                        elif not doc_selected and doc['id'] in st.session_state.selected_documents:
                            st.session_state.selected_documents.remove(doc['id'])

                else:
                    st.info("No documents in this case. Please add documents first.")

    def _process_query(self, prompt: str):
        """Process query with legal prompt generation and structured LLM response."""
        if not st.session_state.current_case:
            st.error("Please select a case first.")
            return

        if not st.session_state.selected_documents:
            st.error("Please select at least one document to analyze.")
            return

        st.session_state.chat_history.append({
            "role": "user",
            "content": prompt
        })

        try:
            with st.status("Processing your query...", expanded=True) as status:
                status.write("🔍 Finding relevant documents...")
                results = self.vector_store.similarity_search(
                    query=prompt,
                    k=5,
                    filter_docs=st.session_state.selected_documents
                )
                
                if not results:
                    st.warning("No relevant information found in the selected documents.")
                    return

                case = self.case_manager.get_case(st.session_state.current_case)
                
                status.write("💭 Analyzing legal context...")
                context_chunks = []
                context_text = []
                
                for r in results:
                    doc_id = r['doc_id']
                    doc = next((d for d in self.case_manager.list_documents(st.session_state.current_case) 
                               if d['id'] == doc_id), None)
                    doc_title = doc['title'] if doc else 'Unknown Document'
                    
                    context_chunks.append({
                        'text': r['text'],
                        'metadata': {
                            **r.get('metadata', {}),
                            'document_title': doc_title
                        }
                    })
                    
                    context_text.append(f"From {doc_title}:\n{r['text']}\n")

                system_message = self.prompt_generator.generate_system_message(
                    context_chunks=context_chunks,
                    query=prompt,
                    case_metadata=case
                )

                user_message = self.prompt_generator.generate_user_message(
                    query=prompt,
                    context="\n\n".join(context_text)
                )

                status.write("✍️ Generating legal analysis...")
                response = self._generate_legal_response(
                    system_message=system_message,
                    user_message=user_message,
                    results=results
                )
                
                status.update(label="✅ Analysis complete!", state="complete")

                st.session_state.chat_history.append(response)
                st.rerun()

        except Exception as e:
            st.error(f"An error occurred: {str(e)}")
            st.exception(e)

    def _generate_legal_response(self, system_message: str, user_message: str, results: List[Dict]) -> Dict:
        """Generate structured legal response using Anthropic API with proper legal prompting."""
        if not results:
            return {
                "role": "assistant",
                "content": "I couldn't find any relevant information in the documents.",
                "citations": []
            }

        try:
            citations = []
            for idx, result in enumerate(results):
                doc_id = result['doc_id']
                doc = next((d for d in self.case_manager.list_documents(st.session_state.current_case) 
                           if d['id'] == doc_id), None)
                
                source_doc = doc['title'] if doc else 'Unknown Document'
                chunk_idx = result.get('metadata', {}).get('chunk_index', 'unknown')
                
                citations.append({
                    "document": source_doc,
                    "section": f"Section {chunk_idx}",
                    "text": result['text'].strip(),
                    "score": result.get('score', 0)
                })

            try:
                api_key = os.getenv('ANTHROPIC_API_KEY')
                if not api_key:
                    raise ValueError("ANTHROPIC_API_KEY not found in environment variables")

                anthropic = Anthropic(api_key=api_key)
                
                # Create a single message with the system context included
                combined_prompt = f"{system_message}\n\nUser Query: {user_message}"
                
                # Make the API call with the correct format
                response = anthropic.messages.create(
                    model="claude-3-sonnet-20240229",
                    max_tokens=4096,
                    messages=[{
                        "role": "user",
                        "content": combined_prompt
                    }],
                    temperature=0.2
                )
                
                llm_response = response.content[0].text

                return {
                    "role": "assistant",
                    "content": llm_response,
                    "citations": sorted(citations, key=lambda x: x['score'], reverse=True)
                }

            except Exception as api_error:
                st.error(f"Claude API Error: {str(api_error)}")
                if "ANTHROPIC_API_KEY" not in os.environ:
                    st.error("ANTHROPIC_API_KEY not found. Please ensure it's set in HuggingFace Secrets.")
                raise api_error

        except Exception as e:
            st.error(f"Error in response generation: {str(e)}")
            return {
                "role": "assistant",
                "content": f"I apologize, but I encountered an error while analyzing the documents: {str(e)}",
                "citations": []
            }