Update src/app.py

src/app.py

@@ -108,7 +108,8 @@ with st.sidebar:
     model_choice = st.radio(
         "Choose your Intelligence:",
         model_options,
-        captions=model_captions
+        captions=model_captions,
+        key="model_selector_radio" # Unique Key Added for Safety
     )
     st.info(f"Connected to: **{model_choice}**")
     
@@ -140,9 +141,6 @@ def query_local_model(messages, max_tokens, model_name):
     url = API_URL_ROOT + "/generate"
     
     # --- FLATTEN MESSAGE HISTORY ---
-    # Since the backend expects a single string ("text"), we format the history here.
-    # We extract the system persona separately to pass to the 'persona' field.
-    
     formatted_history = ""
     system_persona = "You are a helpful assistant." # Default
     
@@ -158,7 +156,7 @@ def query_local_model(messages, max_tokens, model_name):
     formatted_history += "Assistant: "
 
     payload = {
-        "text": formatted_history, # <--- History goes here
+        "text": formatted_history, 
         "persona": system_persona, 
         "max_tokens": max_tokens,
         "model": model_name 
@@ -208,7 +206,6 @@ def clean_text(text):
 
 def ask_ai(user_prompt, system_persona, max_tokens):
     # 1. Standardize Input: Convert the strings into the Message List format
-    # This ensures compatibility with our new memory-aware backend functions
     messages_payload = [
         {"role": "system", "content": system_persona},
         {"role": "user", "content": user_prompt}
@@ -216,12 +213,9 @@ def ask_ai(user_prompt, system_persona, max_tokens):
 
     # 2. Routing Logic
     if "GPT-4o" in model_choice:
-        # CORRECTED: Now calls the OpenAI function
         return query_openai_model(messages_payload, max_tokens)
     else:
-        # Lookup the technical name for Ollama
         technical_name = model_map[model_choice]
-        # Calls the Local function
         return query_local_model(messages_payload, max_tokens, technical_name)
 
 # --- MAIN UI ---
@@ -283,15 +277,12 @@ with tab1:
                 st.session_state.email_draft = reply
                 
                 if usage:
-                    # 1. Determine a clean name for the log
                     if "GPT-4o" in model_choice:
                          m_name = "GPT-4o"
                     else:
-                         # Use the first word of the model choice (e.g., "Llama", "Gemma", "Granite")
                          m_name = model_choice.split(" ")[0]
-                    # 2. Log it
                     tracker.log_usage(m_name, usage["input"], usage["output"])
-                    update_sidebar_metrics() # Force update
+                    update_sidebar_metrics()
 
     if st.session_state.email_draft:
         st.subheader("Draft Result")
@@ -306,362 +297,20 @@ with tab2:
         st.session_state.messages = []
         
     # --- CONTROLS AND METRICS ---
-    # The controls are kept outside the chat loop.
     c1, c2, c3 = st.columns([2, 1, 1])
     with c1:
-        # Use the global model_choice from the sidebar/tab1 initialization
+        # Use the global model_choice
         selected_model_name = st.session_state.get('model_choice', 'Granite 4 (IBM)')
     with c2:
         use_rag = st.toggle("🔌 Enable Knowledge Base", value=False)
-    # The token progress bar will be handled inside the prompt logic based on input length
-
-    with c3:
-        # --- NEW FEATURE: DOWNLOAD CHAT ---
-        # Convert history to a readable string
-        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"
-            
-        # Only show button if there is history to save
-        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 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.")
-    
-    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(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 ---
-    # Since the backend expects a single string ("text"), we format the history here.
-    # We extract the system persona separately to pass to the 'persona' field.
-    
-    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, # <--- History goes here
-        "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):
-    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=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
-    # This ensures compatibility with our new memory-aware backend functions
-    messages_payload = [
-        {"role": "system", "content": system_persona},
-        {"role": "user", "content": user_prompt}
-    ]
-
-    # 2. Routing Logic
-    if "GPT-4o" in model_choice:
-        # CORRECTED: Now calls the OpenAI function
-        return query_openai_model(messages_payload, max_tokens)
-    else:
-        # Lookup the technical name for Ollama
-        technical_name = model_map[model_choice]
-        # Calls the Local function
-        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:
-                    # 1. Determine a clean name for the log
-                    if "GPT-4o" in model_choice:
-                         m_name = "GPT-4o"
-                    else:
-                         # Use the first word of the model choice (e.g., "Llama", "Gemma", "Granite")
-                         m_name = model_choice.split(" ")[0]
-                    # 2. Log it
-                    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")
-    
-    # --- INITIALIZE CHAT MEMORY (MUST BE DONE FIRST) ---
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-        
-    # --- CONTROLS AND METRICS ---
-    # The controls are kept outside the chat loop.
-    c1, c2, c3 = st.columns([2, 1, 1])
-    with c1:
-        # Use the global model_choice from the sidebar/tab1 initialization
-        selected_model_name = st.session_state.get('model_choice', 'Granite 4 (IBM)')
-    with c2:
-        use_rag = st.toggle("🔌 Enable Knowledge Base", value=False)
-    # The token progress bar will be handled inside the prompt logic based on input length
-
     with c3:
         # --- NEW FEATURE: DOWNLOAD CHAT ---
-        # Convert history to a readable string
         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"
             
-        # Only show button if there is history to save
         if chat_log:
             st.download_button(
                 label="💾 Save Chat",
@@ -678,7 +327,7 @@ with tab2:
         with st.chat_message(message["role"]):
             st.markdown(message["content"])
 
-# --- CHAT INPUT HANDLING ---
+    # --- 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})
@@ -729,17 +378,13 @@ with tab2:
         recent_history = st.session_state.messages[-(history_depth+1):-1] 
         messages_payload.extend(recent_history)
         
-        # Add the final (potentially augmented) user message
+        # 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 max_len
-                # (Ensure max_len is defined in the script scope or use st.session_state if needed. 
-                # In your code it's defined in the sidebar, so it should be accessible here)
-                
-                # --- MODEL MAPPING ---
+                # Determine model ID
                 model_id = "" 
                 ollama_map = {
                     "Granite 4 (IBM)": "granite4:latest",
@@ -780,215 +425,6 @@ with tab2:
                     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  ---
-    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 into USER'S specific DB (st.session_state.username)
-                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}")
-
-        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:
-                    # FIX: Show strategy
-                    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:
-                                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() 
-    # --- 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. Initialize the Payload with System 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.' If no context is provided, answer generally."
-        
-        # Start the message payload with the system persona
-        messages_payload = [{"role": "system", "content": system_persona}]
-        
-        # --- MEMORY LOGIC: SLIDING WINDOW ---
-        # Get the last N messages (e.g., 6 total: 3 user + 3 assistant) for memory.
-        # We start from -7 because we need to exclude the current prompt (already added)
-        # and we want pairs of messages (user/assistant).
-        history_depth = 8 # 4 full exchanges (8 messages) + current
-        recent_history = st.session_state.messages[-(history_depth+1):-1] 
-        
-        # Add history to payload
-        messages_payload.extend(recent_history)
-        
-        # 3. Handle RAG & Current Prompt Augmentation
-        final_user_content = prompt
-        retrieved_docs = [] # Initialize for the context display later
-        
-        if use_rag:
-            with st.spinner("🧠 Searching Knowledge Base..."):
-                # Retrieve Docs
-                retrieved_docs = rag_engine.search_knowledge_base(
-                    prompt, 
-                    st.session_state.username
-                )
-                
-                # Format Context
-                context_text = ""
-                if retrieved_docs:
-                    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 the FINAL prompt with RAG context
-                    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. Add the final (potentially augmented) user message to payload
-        messages_payload.append({"role": "user", "content": final_user_content})
-        
-        # 5. Generate Response and Display
-        with st.chat_message("assistant"):
-            with st.spinner(f"Thinking with {selected_model_name}..."):
-                # Determine model ID and max_len (assuming these are defined globally)
-                max_len = 2000 # Example max length
-                model_id = "" # To be mapped
-                
-                # --- MODEL MAPPING LOGIC (Use your existing global logic) ---
-                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
-                
-                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. Final Steps: Save Assistant Response and Update Metrics
-        st.session_state.messages.append({"role": "assistant", "content": response})
-        
-        if usage:
-            m_name = "Granite" if "Granite" in selected_model_name else "GPT-4o"
-            tracker.log_usage(m_name, usage["input"], usage["output"])
-            # Assuming update_sidebar_metrics() is defined globally
-            update_sidebar_metrics()
-
-        # 7. Display Context Used (if RAG was enabled)
-        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:
@@ -1077,7 +513,6 @@ with tab4:
                 with c1:
                     st.text(f"📄 {doc['filename']}")
                 with c2:
-                    # FIX: Show strategy
                     st.caption(f"⚙️ {doc.get('strategy', 'Unknown')}")
                 with c3:
                     st.caption(f"{doc['chunks']}")