src/streamlit_app.py

import streamlit as st
import requests
import os
import unicodedata
import resources # Assuming this file exists in your repo
import tracker
import rag_engine # Now safe to import at top level (lazy loading enabled)
from openai import OpenAI
from datetime import datetime

# --- CONFIGURATION ---
st.set_page_config(page_title="Navy AI Toolkit", page_icon="⚓", layout="wide")

# 1. SETUP CREDENTIALS
API_URL_ROOT = os.getenv("API_URL")  # For Ollama models
OPENAI_KEY = os.getenv("OPENAI_API_KEY")  # For GPT-4o

# --- INITIALIZATION ---
if "roles" not in st.session_state:
    st.session_state.roles = []

# --- LOGIN / REGISTER LOGIC ---
if "authentication_status" not in st.session_state or st.session_state["authentication_status"] is None:
    # If not logged in, show tabs
    login_tab, register_tab = st.tabs(["🔑 Login", "📝 Register"])
    
    with login_tab:
        is_logged_in = tracker.check_login()
        # FIX: Trigger User DB Download ONLY on fresh login
        if is_logged_in:
            tracker.download_user_db(st.session_state.username)
            st.rerun() # Refresh to show the app
    
    with register_tab:
        st.header("Create Account")
        with st.form("reg_form"):
            new_user = st.text_input("Username")
            new_name = st.text_input("Display Name")
            new_email = st.text_input("Email")
            new_pwd = st.text_input("Password", type="password")
            invite = st.text_input("Invitation Passcode") 
            
            if st.form_submit_button("Register"):
                success, msg = tracker.register_user(new_email, new_user, new_name, new_pwd, invite)
                if success:
                    st.success(msg)
                else:
                    st.error(msg)

    # Stop execution if not logged in
    if not st.session_state.get("authentication_status"):
        st.stop()

# --- GLOBAL PLACEHOLDERS ---
metric_placeholder = None
admin_metric_placeholder = None

# --- SIDEBAR (CONSOLIDATED) ---
with st.sidebar:
    st.header("👤 User Profile")
    st.write(f"Welcome, **{st.session_state.name}**")
    
    st.header("📊 Usage Tracker")
    metric_placeholder = st.empty()
    
    # Admin Tools
    if "admin" in st.session_state.roles:
        st.divider()
        st.header("🛡️ Admin Tools")
        admin_metric_placeholder = st.empty()
        
        # FIX: Point to the correct persistence path
        log_path = tracker.get_log_path()
        if log_path.exists():
            with open(log_path, "r") as f:
                log_data = f.read()
            st.download_button(
                label="📥 Download Usage Logs",
                data=log_data,
                file_name=f"usage_log_{datetime.now().strftime('%Y-%m-%d')}.json",
                mime="application/json"
            )
        else:
            st.warning("No logs found yet.")
    
    # Logout
    if "authenticator" in st.session_state:
        st.session_state.authenticator.logout(location='sidebar')
    
    st.divider()
    
    # --- MODEL SELECTOR ---
    st.header("🧠 Model Selector")

    model_map = {
        "Granite 4 (IBM)": "granite4:latest",
        "Llama 3.2 (Meta)": "llama3.2:latest",
        "Gemma 3 (Google)": "gemma3:latest"
    }
    
    model_options = list(model_map.keys())
    model_captions = ["Slower for now, but free and private" for _ in model_options]

    if "admin" in st.session_state.roles:
        model_options.append("GPT-4o (Omni)")
        model_captions.append("Fast, smart, sends data to OpenAI")

    model_choice = st.radio(
        "Choose your Intelligence:",
        model_options,
        captions=model_captions
    )
    st.info(f"Connected to: **{model_choice}**")
    
    st.divider()
    st.header("⚙️ Controls")
    max_len = st.slider("Max Response Length (Tokens)", 100, 2000, 500)

# --- HELPER FUNCTIONS ---
def update_sidebar_metrics():
    """Refreshes the global placeholders defined in the sidebar."""
    if metric_placeholder is None: 
        return 
    
    stats = tracker.get_daily_stats()
    user_stats = stats["users"].get(st.session_state.username, {"input":0, "output":0})
    
    metric_placeholder.metric("My Tokens Today", user_stats["input"] + user_stats["output"])
    
    if "admin" in st.session_state.roles and admin_metric_placeholder is not None:
        admin_metric_placeholder.metric("Team Total Today", stats["total_tokens"])

# Call metrics once on load
update_sidebar_metrics()

def query_local_model(user_prompt, system_persona, max_tokens, model_name): 
    if not API_URL_ROOT:
        return "Error: API_URL not set.", None
    
    url = API_URL_ROOT + "/generate"
    payload = {
        "text": user_prompt, 
        "persona": system_persona, 
        "max_tokens": max_tokens,
        "model": model_name 
    } 
    
    try:
        response = requests.post(url, json=payload, timeout=120)
        
        if response.status_code == 200:
            response_data = response.json()
            ans = response_data.get("response", "")
            usage = response_data.get("usage", {"input":0, "output":0})
            return ans, usage
            
        return f"Error {response.status_code}: {response.text}", None
        
    except Exception as e:
        return f"Connection Error: {e}", None

def query_gpt4o(prompt, persona, max_tokens):
    if not OPENAI_KEY:
        return "Error: OPENAI_API_KEY not set.", None
    
    client = OpenAI(api_key=OPENAI_KEY)
    
    try:
        response = client.chat.completions.create(
            model="gpt-4o",
            max_tokens=max_tokens,
            messages=[
                {"role": "system", "content": persona},
                {"role": "user", "content": prompt}
            ],
            temperature=0.3
        )
        usage_obj = response.usage
        usage_dict = {"input": usage_obj.prompt_tokens, "output": usage_obj.completion_tokens}
        return response.choices[0].message.content, usage_dict
        
    except Exception as e:
        return f"OpenAI Error: {e}", None

def clean_text(text):
    if not text: return ""
    text = unicodedata.normalize('NFKC', text)
    replacements = {'“': '"', '”': '"', '‘': "'", '’': "'", '–': '-', '—': '-', '…': '...', '\u00a0': ' '}
    for old, new in replacements.items():
        text = text.replace(old, new)
    return text.strip()

def ask_ai(user_prompt, system_persona, max_tokens):
    if "GPT-4o" in model_choice:
        return query_gpt4o(user_prompt, system_persona, max_tokens)
    else:
        technical_name = model_map[model_choice]
        return query_local_model(user_prompt, system_persona, max_tokens, technical_name)

# --- MAIN UI ---
st.title("AI Toolkit")
tab1, tab2, tab3, tab4 = st.tabs(["📧 Email Builder", "💬 Chat Playground", "🛠️ Prompt Architect", "📚 Knowledge Base"])

# --- TAB 1: EMAIL BUILDER ---
with tab1:
    st.header("Structured Email Generator")
    if "email_draft" not in st.session_state:
        st.session_state.email_draft = ""

    st.subheader("1. Define the Voice")
    style_mode = st.radio("How should the AI write?", ["Use a Preset Persona", "Mimic My Style"], horizontal=True)
    
    selected_persona_instruction = ""
    if style_mode == "Use a Preset Persona":
        persona_name = st.selectbox("Select a Persona", list(resources.TONE_LIBRARY.keys()))
        selected_persona_instruction = resources.TONE_LIBRARY[persona_name]
        st.info(f"**System Instruction:** {selected_persona_instruction}")
    else:
        st.info("Upload 1-3 text files of your previous emails.")
        uploaded_style_files = st.file_uploader("Upload Samples (.txt)", type=["txt"], accept_multiple_files=True)
        if uploaded_style_files:
            style_context = ""
            for uploaded_file in uploaded_style_files:
                string_data = uploaded_file.read().decode("utf-8")
                style_context += f"---\n{string_data}\n---\n"
            selected_persona_instruction = f"Analyze these examples and mimic the style:\n{style_context}"

    st.divider()
    st.subheader("2. Details")
    c1, c2 = st.columns(2)
    with c1: recipient = st.text_input("Recipient")
    with c2: topic = st.text_input("Topic")
    
    st.caption("Content Source")
    input_method = st.toggle("Upload notes file?")
    raw_notes = ""
    if input_method:
        notes_file = st.file_uploader("Upload Notes (.txt)", type=["txt"])
        if notes_file: raw_notes = notes_file.read().decode("utf-8")
    else:
        raw_notes = st.text_area("Paste notes:", height=150)

    # Context Bar
    est_tokens = len(raw_notes) / 4 
    st.progress(min(est_tokens / 128000, 1.0), text=f"Context: {int(est_tokens)} tokens")

    if st.button("Draft Email", type="primary"):
        if not raw_notes:
            st.warning("Please provide notes.")
        else:
            clean_notes = clean_text(raw_notes)
            with st.spinner(f"Drafting with {model_choice}..."):
                prompt = f"TASK: Write email.\nTO: {recipient}\nTOPIC: {topic}\nSTYLE: {selected_persona_instruction}\nDATA: {clean_notes}"
                
                reply, usage = ask_ai(prompt, "You are an expert ghostwriter.", max_len)
                st.session_state.email_draft = reply
                
                if usage:
                    m_name = "Granite" if "Granite" in model_choice else "GPT-4o"
                    tracker.log_usage(m_name, usage["input"], usage["output"])
                    update_sidebar_metrics() # Force update

    if st.session_state.email_draft:
        st.subheader("Draft Result")
        st.text_area("Copy your email:", value=st.session_state.email_draft, height=300)

# --- TAB 2: CHAT PLAYGROUND ---
with tab2:
    st.header("Choose Your Model and Start a Discussion")
    
    if "chat_response" not in st.session_state:
        st.session_state.chat_response = ""

    user_input = st.text_input("Ask a question:")
    
    c1, c2 = st.columns([1,1])
    with c1:
        use_rag = st.toggle("🔌 Enable Knowledge Base", value=True)
    with c2:
        est_tokens = len(user_input) / 4 
        st.progress(min(est_tokens / 2000, 1.0), text=f"Input: {int(est_tokens)} tokens")

    if st.button("Send Query"):
        if not user_input:
            st.warning("Please enter a question.")
        else:
            final_prompt = user_input
            system_persona = "You are a helpful assistant."
            
            # --- RAG LOGIC ---
            if use_rag:
                with st.spinner("🧠 Searching Knowledge Base..."):
                    # 1. Retrieve & Rerank (Now using the fixed function)
                    retrieved_docs = rag_engine.search_knowledge_base(
                        user_input, 
                        st.session_state.username, 
                        k=3
                    )
                    
                    if retrieved_docs:
                        # 2. Format Context
                        context_text = ""
                        for i, doc in enumerate(retrieved_docs):
                            # Add metadata relevance score if available
                            score = doc.metadata.get('relevance_score', 'N/A')
                            src = os.path.basename(doc.metadata.get('source', 'Unknown'))
                            context_text += f"---\nSOURCE: {src} (Rel: {score})\nTEXT: {doc.page_content}\n"
                        
                        # 3. Update Prompt
                        system_persona = (
                            "You are a Navy Document Analyst. "
                            "Answer the user's question strictly based on the Context provided below. "
                            "If the answer is not in the Context, state 'I cannot find that information in the provided documents.' \n\n"
                            f"### CONTEXT:\n{context_text}"
                        )
                        st.success(f"Found {len(retrieved_docs)} relevant documents.")
                        with st.expander("View Context Used"):
                            st.text(context_text)
                    else:
                        st.warning("No relevant documents found. Using general knowledge.")

            # --- GENERATION ---
            with st.spinner(f"Thinking with {model_choice}..."):
                reply, usage = ask_ai(final_prompt, system_persona, max_len)
                st.session_state.chat_response = reply
                
                if usage:
                    m_name = "Granite" if "Granite" in model_choice else "GPT-4o"
                    tracker.log_usage(m_name, usage["input"], usage["output"])
                    update_sidebar_metrics() 

    if st.session_state.chat_response:
        st.divider()
        st.markdown("**AI Response:**")
        st.write(st.session_state.chat_response)

# --- TAB 3: PROMPT ARCHITECT ---
with tab3:
    st.header("🛠️ Mega-Prompt Factory")
    st.info("Build standard templates for NIPRGPT.")
    
    c1, c2 = st.columns([1,1])
    with c1:
        st.subheader("1. Parameters")
        p = st.text_area("Persona", placeholder="Act as...", height=100)
        c = st.text_area("Context", placeholder="Background...", height=100)
        t = st.text_area("Task", placeholder="Action...", height=100)
        v = st.text_input("Placeholder Name", value="PASTE_DATA_HERE")
    
    with c2:
        st.subheader("2. Result")
        final = f"### ROLE\n{p}\n### CONTEXT\n{c}\n### TASK\n{t}\n### INPUT DATA\n\"\"\"\n[{v}]\n\"\"\""
        st.code(final, language="markdown")
        st.download_button("💾 Download .txt", final, "template.txt")

# --- TAB 4: KNOWLEDGE BASE ---
with tab4:
    st.header("🧠 Unit Knowledge Base")
    
    is_admin = "admin" in st.session_state.roles
    kb_tab1, kb_tab2 = st.tabs(["📤 Add Documents", "🗂️ Manage Database"])
    
    # --- SUB-TAB 1: UPLOAD ---
    with kb_tab1:
        if is_admin:
            st.subheader("Ingest New Knowledge")
            uploaded_file = st.file_uploader("Upload Instructions, Manuals, or Logs", type=["pdf", "docx", "txt", "md"])
            
            col1, col2 = st.columns([1, 2])
            with col1:
                chunk_strategy = st.selectbox(
                    "Chunking Strategy", 
                    ["paragraph", "token", "page"],
                    help="Paragraph: Manuals. Token: Dense text. Page: Forms."
                )
            
            if uploaded_file and st.button("Process & Add"):
                with st.spinner("Analyzing and Indexing..."):
                    # Use safe save + process
                    temp_path = rag_engine.save_uploaded_file(uploaded_file)
                    success, msg = rag_engine.process_and_add_document(
                        temp_path, 
                        st.session_state.username, 
                        chunk_strategy
                    )
                    
                    if success:
                        st.success(msg)
                        st.rerun() 
                    else:
                        st.error(f"Failed: {msg}")
        else:
            st.info("🔒 Only Admins can upload documents.")

        st.divider()
        st.subheader("🔎 Quick Test")
        test_query = st.text_input("Ask the brain something...")
        if test_query:
            results = rag_engine.search_knowledge_base(test_query, st.session_state.username)
            for i, doc in enumerate(results):
                # Using cleaned safe basename
                src_name = os.path.basename(doc.metadata.get('source', '?'))
                score = doc.metadata.get('relevance_score', 'N/A')
                with st.expander(f"Match {i+1}: {src_name} (Score: {score})"):
                    st.write(doc.page_content)

    # --- SUB-TAB 2: MANAGE ---
    with kb_tab2:
        st.subheader("🗄️ Database Inventory")
        
        # 1. Fetch current docs
        docs = rag_engine.list_documents(st.session_state.username)
        
        if not docs:
            st.info("Knowledge Base is empty.")
        else:
            st.markdown(f"**Total Documents:** {len(docs)}")
            
            for doc in docs:
                c1, c2, c3 = st.columns([3, 1, 1])
                with c1:
                    st.text(f"📄 {doc['filename']}")
                with c2:
                    st.caption(f"{doc['chunks']} chunks")
                with c3:
                    if is_admin:
                        if st.button("🗑️ Delete", key=doc['source']):
                            with st.spinner("Deleting..."):
                                success, msg = rag_engine.delete_document(st.session_state.username, doc['source'])
                                if success:
                                    st.success(msg)
                                    st.rerun()
                                else:
                                    st.error(msg)
                    else:
                        st.caption("Read Only")

        if is_admin and docs:
            st.divider()
            with st.expander("🚨 Danger Zone"):
                if st.button("☢️ RESET ENTIRE DATABASE", type="primary"):
                    success, msg = rag_engine.reset_knowledge_base(st.session_state.username)
                    if success:
                        st.success(msg)
                        st.rerun()