src/app.py

import streamlit as st
import requests
import os
import unicodedata
import resources 
import tracker
import rag_engine 
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()
        if is_logged_in:
            # Check if a different user was previously logged in
            if "last_user" in st.session_state and st.session_state.last_user != st.session_state.username:
                # WIPE EVERYTHING
                st.session_state.messages = []
                st.session_state.email_draft = ""
                st.session_state.user_openai_key = None
                
            # Update the tracker
            st.session_state.last_user = st.session_state.username
            
            # Download DB and Refresh
            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()
        
        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]

    # 2. CHECK FOR GPT-4o ACCESS (Admin OR User Key)
    # We moved the input UP so the user can unlock the option immediately
    
    # Check if user is admin
    is_admin = "admin" in st.session_state.roles
    
    # Input for Non-Admins
    user_api_key = None
    if not is_admin:
        user_api_key = st.text_input(
            "🔓 Unlock GPT-4o (Enter API Key)", 
            type="password", 
            help="Enter your OpenAI API key to access GPT-4o. Press Enter to apply.",
            key=f"user_key_{st.session_state.username}"
        )
        if user_api_key:
            st.session_state.user_openai_key = user_api_key
            st.caption("✅ Key Active")
        else:
            st.session_state.user_openai_key = None
    else:
        st.session_state.user_openai_key = None

    # 3. DYNAMICALLY ADD GPT-4o TO THE LIST
    # If Admin OR if they just entered a key, show the option
    if is_admin or st.session_state.get("user_openai_key"):
        model_options.append("GPT-4o (Omni)")
        model_captions.append("Fast, smart, sends data to OpenAI")

    # 4. RENDER THE SELECTOR
    model_choice = st.radio(
        "Choose your Intelligence:",
        model_options,
        captions=model_captions,
        key="model_selector_radio" 
    )
    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(messages, max_tokens, model_name): 
    if not API_URL_ROOT:
        return "Error: API_URL not set.", None
    
    url = API_URL_ROOT + "/generate"
    
    # --- FLATTEN MESSAGE HISTORY ---
    formatted_history = ""
    system_persona = "You are a helpful assistant." # Default
    
    for msg in messages:
        if msg['role'] == 'system':
            system_persona = msg['content']
        elif msg['role'] == 'user':
            formatted_history += f"User: {msg['content']}\n"
        elif msg['role'] == 'assistant':
            formatted_history += f"Assistant: {msg['content']}\n"
            
    # Append the "Assistant:" prompt at the end to cue the model
    formatted_history += "Assistant: "

    payload = {
        "text": formatted_history, 
        "persona": system_persona, 
        "max_tokens": max_tokens,
        "model": model_name 
    } 
    
    try:
        response = requests.post(url, json=payload, timeout=300)
        
        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_openai_model(messages, max_tokens):
    # 1. Check for User Key first
    api_key_to_use = st.session_state.get("user_openai_key")
    
    # 2. Fallback to System Key
    if not api_key_to_use:
        api_key_to_use = OPENAI_KEY

    # 3. Final Safety Check
    if not api_key_to_use:
        return "Error: No API Key available. Please enter one in the sidebar.", None
    
    client = OpenAI(api_key=api_key_to_use)
    
    try:
        response = client.chat.completions.create(
            model="gpt-4o",
            max_tokens=max_tokens,
            messages=messages,
            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):
    # 1. Standardize Input: Convert the strings into the Message List format
    messages_payload = [
        {"role": "system", "content": system_persona},
        {"role": "user", "content": user_prompt}
    ]

    # 2. Routing Logic
    if "GPT-4o" in model_choice:
        return query_openai_model(messages_payload, max_tokens)
    else:
        technical_name = model_map[model_choice]
        return query_local_model(messages_payload, 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:
                    if "GPT-4o" in model_choice:
                         m_name = "GPT-4o"
                    else:
                         m_name = model_choice.split(" ")[0]
                    tracker.log_usage(m_name, usage["input"], usage["output"])
                    update_sidebar_metrics()

    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")
    
    # --- INITIALIZE CHAT MEMORY (MUST BE DONE FIRST) ---
    if "messages" not in st.session_state:
        st.session_state.messages = []
        
    # --- CONTROLS AND METRICS ---
    c1, c2, c3 = st.columns([2, 1, 1])
    with c1:
        # FIX: Access the correct key from the sidebar widget
        # We default to the global variable 'model_choice' if state is missing
        selected_model_name = st.session_state.get('model_selector_radio', model_choice)
        st.caption(f"Active Model: **{selected_model_name}**")
        
    with c2:
        use_rag = st.toggle("🔌 Enable Knowledge Base", value=False)
    
    with c3:
        # --- NEW FEATURE: DOWNLOAD CHAT ---
        chat_log = ""
        for msg in st.session_state.messages:
            role = "USER" if msg['role'] == 'user' else "ASSISTANT"
            chat_log += f"[{role}]: {msg['content']}\n\n"
            
        if chat_log:
            st.download_button(
                label="💾 Save Chat",
                data=chat_log,
                file_name="mission_log.txt",
                mime="text/plain",
                help="Download the current conversation history."
            )
    
    st.divider()
    
    # --- DISPLAY CONVERSATION HISTORY ---
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # --- CHAT INPUT HANDLING ---
    if prompt := st.chat_input("Ask a question..."):
        # 1. Display User Message and save to history
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.markdown(prompt)

        # 2. Default Configuration (Standard AI Mode)
        system_persona = "You are a helpful AI assistant. Answer the user's question to the best of your ability."
        final_user_content = prompt
        retrieved_docs = [] 
        
        # 3. Handle RAG Logic (Only if enabled)
        if use_rag:
            with st.spinner("🧠 Searching Knowledge Base..."):
                retrieved_docs = rag_engine.search_knowledge_base(
                    prompt, 
                    st.session_state.username
                )
                
                if retrieved_docs:
                    # RAG SUCCESS: Switch to Strict Navy Persona
                    system_persona = (
                        "You are a Navy Document Analyst. Your task is to answer the user's question "
                        "using ONLY the Context provided below. "
                        "If the answer is not present in the Context, return ONLY this exact phrase: "
                        "'I cannot find that information in the provided documents.'"
                    )
                    
                    # Format Context
                    context_text = ""
                    for doc in retrieved_docs:
                        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"
                    
                    # Augment User Prompt
                    final_user_content = (
                        f"User Question: {prompt}\n\n"
                        f"Relevant Context:\n{context_text}\n\n"
                        "Answer the question using the context provided."
                    )
        
        # 4. Construct Payload (Now using the CORRECT persona)
        messages_payload = [{"role": "system", "content": system_persona}]
        
        # --- MEMORY LOGIC: SLIDING WINDOW ---
        history_depth = 8 
        recent_history = st.session_state.messages[-(history_depth+1):-1] 
        messages_payload.extend(recent_history)
        
        # Add the final (potentially augmented) user message to payload
        messages_payload.append({"role": "user", "content": final_user_content})
        
        # 5. Generate Response
        with st.chat_message("assistant"):
            with st.spinner(f"Thinking with {selected_model_name}..."):
                # Determine model ID
                model_id = "" 
                ollama_map = {
                    "Granite 4 (IBM)": "granite4:latest",
                    "Llama 3.2 (Meta)": "llama3.2:latest",
                    "Gemma 3 (Google)": "gemma3:latest"
                }
                for key, val in ollama_map.items():
                    if key in selected_model_name:
                        model_id = val
                        break
                
                # ROUTING CHECK
                if not model_id and "gpt" in selected_model_name.lower():
                    # If it's the GPT model choice
                    response, usage = query_openai_model(messages_payload, max_len)
                elif model_id:
                    # If it's the local Ollama model
                    response, usage = query_local_model(messages_payload, max_len, model_id)
                else:
                    response, usage = "Error: Could not determine model to use.", None
                
                st.markdown(response)
            
        # 6. Save Assistant Response
        st.session_state.messages.append({"role": "assistant", "content": response})
        
        # 7. Metrics & Context Display
        if usage:
            if "GPT-4o" in selected_model_name:
                 m_name = "GPT-4o"
            else:
                 m_name = selected_model_name.split(" ")[0]
            tracker.log_usage(m_name, usage["input"], usage["output"])
            update_sidebar_metrics()

        if use_rag and retrieved_docs:
            with st.expander("📚 View Context Used"):
                for i, doc in enumerate(retrieved_docs):
                    score = doc.metadata.get('relevance_score', 'N/A')
                    src = os.path.basename(doc.metadata.get('source', 'Unknown'))
                    st.caption(f"Rank {i+1} (Source: {src}, Rel: {score})")
                    st.text(doc.page_content)
                    st.divider()
                    
# --- 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("🧠 Personal Knowledge Base")
    st.info(f"Managing knowledge for: **{st.session_state.username}**")
    
    # We no longer check 'is_admin' for the whole tab
    kb_tab1, kb_tab2 = st.tabs(["📤 Add Documents", "🗂️ Manage Database"])
    
    # --- SUB-TAB 1: UPLOAD (Unlocked for Everyone) ---
    with kb_tab1:
        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..."):
                # 1. Save temp file
                temp_path = rag_engine.save_uploaded_file(uploaded_file)
                
                # 2. Process locally
                success, msg = rag_engine.process_and_add_document(
                    temp_path, 
                    st.session_state.username, 
                    chunk_strategy
                )
                
                if success:
                    # 3. FIX: SYNC TO CLOUD IMMEDIATELY
                    with st.spinner("Backing up to Cloud..."):
                        tracker.upload_user_db(st.session_state.username)
                        
                    st.success(msg)
                    st.rerun() 
                else:
                    st.error(f"Failed: {msg}")

        st.divider()
        st.subheader("🔎 Quick Test")
        test_query = st.text_input("Ask your brain something...")
        if test_query:
            results = rag_engine.search_knowledge_base(test_query, st.session_state.username)
            if not results:
                st.warning("No matches found.")
            for i, doc in enumerate(results):
                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 (Unlocked for Everyone) ---
    with kb_tab2:
        st.subheader("🗄️ Database Inventory")
        
        docs = rag_engine.list_documents(st.session_state.username)
        
        if not docs:
            st.info("Your Knowledge Base is empty.")
        else:
            st.markdown(f"**Total Documents:** {len(docs)}")
            
            for doc in docs:
                c1, c2, c3, c4 = st.columns([3, 2, 1, 1])
                with c1:
                    st.text(f"📄 {doc['filename']}")
                with c2:
                    st.caption(f"⚙️ {doc.get('strategy', 'Unknown')}")
                with c3:
                    st.caption(f"{doc['chunks']}")
                with c4:
                    if st.button("🗑️", key=doc['source'], help="Delete Document"):
                        with st.spinner("Deleting..."):
                            success, msg = rag_engine.delete_document(st.session_state.username, doc['source'])
                            if success:
                                tracker.upload_user_db(st.session_state.username)
                                st.success(msg)
                                st.rerun()
                            else:
                                st.error(msg)

            st.divider()
            with st.expander("🚨 Danger Zone"):
                # Allow ANY user to reset their OWN database
                if st.button("☢️ RESET MY DATABASE", type="primary"):
                    success, msg = rag_engine.reset_knowledge_base(st.session_state.username)
                    if success:
                        st.success(msg)
                        st.rerun()