Delete streamlit_app.py

streamlit_app.py

@@ -1,445 +0,0 @@
-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()