LocalFoodAI_lanfr144/app.py

# $Id$
# $Author$
# $log$
#ident "@(#)LocalFoodAI:app.py:$Format:%D:%ci:%cN:%h$"
import streamlit as st
import pymysql
import myloginpath
import ollama
import bcrypt
import requests
import string
import random
import smtplib
from email.message import EmailMessage
import pandas as pd
from unit_converter import UnitConverter
from snmp_notifier import notifier
import time

import threading

def pull_model_bg():
    try: ollama.pull('llama3.2:1b')
    except: pass
threading.Thread(target=pull_model_bg, daemon=True).start()

def local_web_search(query: str) -> str:
    try:
        req = requests.get(f'http://127.0.0.1:8080/search', params={'q': query, 'format': 'json'})
        if req.status_code == 200:
            data = req.json()
            results = data.get('results', [])
            if not results: return f"No results found on the web for '{query}'."
            snippets = [f"Source: {r.get('url')}\nContent: {r.get('content')}" for r in results[:3]]
            return "\n\n".join(snippets)
        return "Search engine returned an error."
    except Exception as e: return f"Local search engine unreachable: {e}"

search_tool_schema = {
    'type': 'function',
    'function': {
        'name': 'local_web_search',
        'description': 'Search the internet for info not in DB.',
        'parameters': {'type': 'object', 'properties': {'query': {'type': 'string'}}, 'required': ['query']},
    },
}

def search_nutrition_db(query: str, user_eav=None) -> str:
    conn = get_db_connection('app_reader')
    if not conn: return "Database connection failed."
    try:
        with conn.cursor() as cursor:
            # Dynamically build strictly-enforced clinical SQL filters
            clinical_filters = ""
            if user_eav:
                for p in user_eav:
                    name = p['name'].lower()
                    val = p['value'].lower()
                    if name in ['condition', 'illness']:
                        if val == 'diabetes': clinical_filters += " AND m.sugars_100g < 5.0"
                        elif 'kidney' in val: clinical_filters += " AND m.proteins_100g < 15.0"
                        elif 'hypertension' in val: clinical_filters += " AND m.sodium_100g < 0.2"
                    elif name in ['diet', 'religious', 'preference']:
                        if val == 'kosher': clinical_filters += " AND c.ingredients_text NOT LIKE '%pork%' AND c.ingredients_text NOT LIKE '%shellfish%'"
                        elif val == 'halal': clinical_filters += " AND c.ingredients_text NOT LIKE '%pork%' AND c.ingredients_text NOT LIKE '%wine%' AND c.ingredients_text NOT LIKE '%alcohol%'"
                        elif val in ['christian', 'good friday', 'ash wednesday']: clinical_filters += " AND c.ingredients_text NOT LIKE '%meat%' AND c.ingredients_text NOT LIKE '%beef%' AND c.ingredients_text NOT LIKE '%chicken%' AND c.ingredients_text NOT LIKE '%pork%'"

            sql = f"""
                SELECT c.code, c.product_name, m.proteins_100g, m.fat_100g, m.carbohydrates_100g, m.sugars_100g 
                FROM food_db.products_core c
                LEFT JOIN food_db.products_macros m ON c.code = m.code
                WHERE MATCH(c.product_name, c.ingredients_text) AGAINST(%s IN BOOLEAN MODE)
                AND c.product_name IS NOT NULL AND c.product_name != '' AND c.product_name != 'None'
                {clinical_filters}
                LIMIT 15
            """
            bool_query = " ".join([f"+{w}" for w in query.split()])
            cursor.execute(sql, (bool_query,))
            results = cursor.fetchall()
            if not results: return f"No database records found for '{query}'."
            
            snippets = []
            for r in results:
                snippets.append(f"- {r['product_name']}: Protein {r['proteins_100g']}g, Fat {r['fat_100g']}g, Carbs {r['carbohydrates_100g']}g, Sugars {r['sugars_100g']}g (per 100g)")
            return "\n".join(snippets)
    except Exception as e:
        return f"Database query failed: {e}"
    finally:
        conn.close()

db_search_tool_schema = {
    'type': 'function',
    'function': {
        'name': 'search_nutrition_db',
        'description': 'Search the local medical nutrition database for product macros and ingredients. ALWAYS prioritize this over web search.',
        'parameters': {'type': 'object', 'properties': {'query': {'type': 'string', 'description': 'The product or food name to search for (e.g. apple, chicken, bread)'}}, 'required': ['query']},
    },
}

def get_db_connection(login_path):
    try:
        import os
        db_host = os.environ.get('DB_HOST')
        # Check if environment variables exist for this login path
        db_user = os.environ.get(f'{login_path.upper()}_USER') or os.environ.get('DB_USER')
        db_pass = os.environ.get(f'{login_path.upper()}_PASS') or os.environ.get('DB_PASS')

        if db_host and db_user and db_pass:
            return pymysql.connect(
                host=db_host,
                user=db_user,
                password=db_pass,
                database='food_db',
                cursorclass=pymysql.cursors.DictCursor
            )
            
        conf = myloginpath.parse(login_path)
        if not conf or not conf.get('user'):
            st.error(f"⚠️ MySQL configuration missing for `{login_path}`. If you are testing locally on Windows, this app must be run on the Ubuntu server where `mysql_config_editor` is properly configured.")
            return None
            
        return pymysql.connect(
            host=conf.get('host', '127.0.0.1'),
            user=conf.get('user'),
            password=conf.get('password'),
            database='food_db',
            cursorclass=pymysql.cursors.DictCursor
        )
    except Exception as e:
        st.error(f"Connection Failed: {e}")
        return None

def verify_login(username, password):
    conn = get_db_connection('app_auth')
    if not conn: return False
    with conn.cursor() as cursor:
        cursor.execute("SELECT password_hash FROM users WHERE username = %s LIMIT 1", (username,))
        result = cursor.fetchone()
        conn.close()
        if result: return bcrypt.checkpw(password.encode('utf-8'), result['password_hash'].encode('utf-8'))
    return False

def get_user_id(username):
    conn = get_db_connection('app_auth')
    if not conn: return None
    with conn.cursor() as cursor:
        cursor.execute("SELECT id FROM users WHERE username = %s LIMIT 1", (username,))
        result = cursor.fetchone()
        conn.close()
        return result['id'] if result else None

def get_eav_profile(username):
    uid = get_user_id(username)
    if not uid: return []
    conn = get_db_connection('app_auth')
    with conn.cursor() as cursor:
        cursor.execute("SELECT id, illness_health_condition_diet_dislikes_name as name, illness_health_condition_diet_dislikes_value as value FROM user_health_profiles WHERE user_id = %s", (uid,))
        res = cursor.fetchall()
        conn.close()
        return res

def get_user_limit(username):
    conn = get_db_connection('app_auth')
    if not conn: return "50"
    with conn.cursor() as cursor:
        cursor.execute("SELECT search_limit FROM users WHERE username = %s LIMIT 1", (username,))
        result = cursor.fetchone()
        conn.close()
        return result['search_limit'] if (result and result['search_limit']) else "50"

def register_user(username, password, email):
    conn = get_db_connection('app_auth')
    if not conn: return False
    hashed = bcrypt.hashpw(password.encode('utf-8'), bcrypt.gensalt()).decode('utf-8')
    try:
        with conn.cursor() as cursor:
            cursor.execute("INSERT INTO users (username, password_hash, email) VALUES (%s, %s, %s)", (username, hashed, email))
            conn.commit()
        conn.close()
        send_email(email, "Welcome to Local Food AI", f"Hello {username}, your account was securely created!", to_name=username.title())
        return True
    except pymysql.err.IntegrityError:
        return False

def send_email(to_email, subject, body, to_name="User"):
    msg = EmailMessage()
    msg.set_content(body)
    msg['Subject'] = subject
    msg['From'] = '"Clinical Food AI System" <security@localfoodai.com>'
    msg['To'] = f'"{to_name}" <{to_email}>'
    
    for attempt in range(5):
        try:
            s = smtplib.SMTP('localhost', 25)
            s.send_message(msg)
            s.quit()
            return True
        except Exception as e:
            if attempt == 4:
                return f"SMTP Delivery Failed: {str(e)}"
            time.sleep(2)
    return "Unknown Error Occurred"

def reset_password(username, email):
    conn = get_db_connection('app_auth')
    if not conn: return False
    with conn.cursor() as cursor:
        cursor.execute("SELECT id, email FROM users WHERE username = %s", (username,))
        user = cursor.fetchone()
        if user and user['email'] == email:
            new_pass = ''.join(random.choices(string.ascii_letters + string.digits, k=10))
            hashed = bcrypt.hashpw(new_pass.encode('utf-8'), bcrypt.gensalt()).decode('utf-8')
            cursor.execute("UPDATE users SET password_hash = %s WHERE id = %s", (hashed, user['id']))
            conn.commit()
            conn.close()
            status = send_email(email, "Password Reset", f"Your new temporary password is: {new_pass}", to_name=username.title())
            if status is True:
                return True
            return status
    return False

# UI Theming
def render_version():
    st.markdown("---")
    st.caption("🚀 Version: v1.3.0")
    st.caption(f"📅 Git ID: $Id$")

st.set_page_config(page_title="Food AI Explorer", page_icon="🍔", layout="wide")
st.markdown("""
<style>
    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap');
    html, body, [class*="css"]  { font-family: 'Inter', sans-serif; background-color: #0b192c; color: #e2e8f0; }
    h1, h2, h3 { color: #38bdf8 !important; font-weight: 600; letter-spacing: 0.5px; }
    div[data-testid="stSidebar"] { background: rgba(11, 25, 44, 0.95) !important; backdrop-filter: blur(10px); border-right: 1px solid #1e293b; }
    .stButton>button { background: linear-gradient(135deg, #0ea5e9, #0284c7); color: white; border: none; border-radius: 6px; }
    .stButton>button:hover { transform: scale(1.02); }
    .stTextInput>div>div>input, .stNumberInput>div>div>input, .stSelectbox>div>div>div { background-color: #0f172a; color: #f8fafc; border: 1px solid #38bdf8; }
</style>
""", unsafe_allow_html=True)

if "authenticated_user" not in st.session_state:
    st.session_state["authenticated_user"] = None

with st.sidebar:
    st.title("User Portal 🔐")
    render_version()
    
    with st.expander("🛠️ Diagnostic: App Database View"):
        conn = get_db_connection('app_auth')
        if conn:
            with conn.cursor() as c:
                c.execute("DESCRIBE users;")
                st.json(c.fetchall())
                c.execute("SELECT DATABASE(), CURRENT_USER();")
                st.json(c.fetchall())
            conn.close()
            
    if st.session_state["authenticated_user"]:
        st.success(f"Logged in as: {st.session_state['authenticated_user']}")
        if st.button("Logout"):
            st.session_state["authenticated_user"] = None
            st.rerun()
            
        eav_data = get_eav_profile(st.session_state["authenticated_user"])
        uid = get_user_id(st.session_state["authenticated_user"])
        user_lim = get_user_limit(st.session_state["authenticated_user"])
        
        with st.expander("⚙️ Account Preferences"):
            opts = ["10", "20", "50", "100", "All"]
            idx = opts.index(user_lim) if user_lim in opts else 2
            new_lim = st.selectbox("Default Search Limit", opts, index=idx)
            if new_lim != user_lim:
                conn = get_db_connection('app_auth')
                with conn.cursor() as c:
                    c.execute("UPDATE users SET search_limit = %s WHERE id = %s", (new_lim, uid))
                    conn.commit()
                st.rerun()

        with st.expander("➕ Add Condition / Diet"):
            new_cat = st.selectbox("Category", ["Condition", "Illness", "Diet", "Dislike", "Allergy"])
            new_val = st.text_input("Value (e.g. 'vegan', 'diabetes', 'broccoli')").strip().lower()
            if st.button("Add to Profile") and new_val and uid:
                conn = get_db_connection('app_auth')
                with conn.cursor() as c:
                    c.execute("INSERT INTO user_health_profiles (user_id, illness_health_condition_diet_dislikes_name, illness_health_condition_diet_dislikes_value) VALUES (%s, %s, %s)", (uid, new_cat, new_val))
                    conn.commit()
                st.rerun()
                
        if eav_data:
            st.markdown("#### Active Flags")
            for e in eav_data:
                col1, col2 = st.columns([4, 1])
                col1.info(f"**{e['name']}:** {e['value'].title()}")
                if col2.button("X", key=f"del_eav_{e['id']}"):
                    conn = get_db_connection('app_auth')
                    with conn.cursor() as c:
                        c.execute("DELETE FROM user_health_profiles WHERE id = %s", (e['id'],))
                        conn.commit()
                    st.rerun()
    else:
        tab1, tab2, tab3 = st.tabs(["Login", "Register", "Reset"])
        with tab1:
            l_user = st.text_input("Username", key="l_user").strip()
            l_pass = st.text_input("Password", type="password", key="l_pass")
            if st.button("Login"):
                if verify_login(l_user, l_pass):
                    notifier.send_alert(f"User Login Success: {l_user}")
                    st.session_state["authenticated_user"] = l_user
                    st.rerun()
                else:
                    notifier.send_alert(f"User Login Failed: {l_user}")
                    st.error("Invalid login.")
        with tab2:
            r_user = st.text_input("Username", key="r_user")
            r_email = st.text_input("Email Address", key="r_email")
            r_pass = st.text_input("Password", type="password", key="r_pass")
            if st.button("Register"):
                if len(r_pass) < 6: st.error("Password too short.")
                elif register_user(r_user, r_pass, r_email): st.success("Registered safely!")
                else: st.error("Username exists.")
        with tab3:
            f_user = st.text_input("Username", key="f_user")
            f_email = st.text_input("Registered Email", key="f_email")
            if st.button("Send Reset Link"):
                status = reset_password(f_user, f_email)
                if status is True: 
                    st.success("Password reset emailed.")
                else: 
                    st.error(f"Failed: {status}")

if not st.session_state["authenticated_user"]:
    st.title("🍔 Food AI Medical Explorer")
    st.info("Please login to interrogate the Clinical Data.")
    st.stop()

st.title("🍔 Food AI Clinical Explorer")
conn_reader = get_db_connection('app_reader')

tab_chat, tab_explore, tab_plate, tab_planner = st.tabs(["💬 AI Chat", "🔬 Clinical Search", "🍽️ My Plate Builder", "🤖 AI Meal Planner"])

import re

with tab_chat:
    c1, c2 = st.columns([4, 1])
    c1.subheader("Chat with the Context")
    if c2.button("🧹 Clear Chat"):
        st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you analyze the food data today?"}]
        st.rerun()
    st.info("""
    ℹ️ **How to use this feature (Examples)**
    **Your active conditions (e.g. Pregnant, Diabetic) are automatically sent to the AI in the background. You do not need to type them out.**
    
    *Examples:*
    1. "I am pregnant, diabetic, and have kidney problems. Can I eat sushi?"
    2. "What is a safe snack to stabilize my blood sugar without hurting my kidneys?"
    3. "Can I drink milk? I need calcium for the baby."
    4. "Is it safe to eat a large steak for iron?"
    5. "What foods are strictly forbidden for me?"
    """)
    if "messages" not in st.session_state:
        st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you analyze the food data today?"}]

    # Display chat history, filtering out TOOL_CALLS
    for msg in st.session_state.messages:
        if msg["role"] == "tool": continue
        display_text = re.sub(r'\[TOOL_CALLS\]\s*\[.*?\]', '', msg["content"]).strip()
        if display_text:
            st.chat_message(msg["role"]).write(display_text)

    if prompt := st.chat_input("Ask a clinical question about your food..."):
        st.session_state.messages.append({"role": "user", "content": prompt})
        st.chat_message("user").write(prompt)
        
        user_eav = get_eav_profile(st.session_state["authenticated_user"])
        profile_text = ", ".join([f"{p['name']}: {p['value']}" for p in user_eav]) if user_eav else "None"
        
        sys_prompt = f"""You are a helpful medical data analyst AI. 
        Health profile: {profile_text}. 
        Act as a specialized clinical dietitian. Provide a direct answer. Skip all thinking, reasoning, and pleasantries.
        Use this database context if relevant to the user's question: {search_nutrition_db(prompt)}
        """
        
        try:
            temp_messages = [{"role": "system", "content": sys_prompt}] + [m for m in st.session_state.messages if m["role"] != "tool"]
            response_stream = ollama.chat(model='llama3.2:1b', messages=temp_messages, stream=True)
            
            with st.chat_message("assistant"):
                ai_reply = st.write_stream(chunk['message']['content'] for chunk in response_stream)
            
            st.session_state.messages.append({"role": "assistant", "content": ai_reply})
        except Exception as e: 
            ai_reply = f"Hold on! Engine execution fault: {e}"
            st.session_state.messages.append({"role": "assistant", "content": ai_reply})
            st.chat_message("assistant").write(ai_reply)

def highlight_medical_warnings(row):
    try:
        val = str(row.get('Medical Warning', ''))
        if '⚠️' in val: return ['background-color: rgba(255, 0, 0, 0.4); color: white;'] * len(row)
        if '💚' in val: return ['background-color: rgba(0, 255, 0, 0.3); color: white;'] * len(row)
    except: pass
    return [''] * len(row)

with tab_explore:
    st.subheader("Clinical Data Search")
    st.info("""
    ℹ️ **How to use this feature (Examples)**
    **Your active conditions are automatically flagged (⚠️ or 💚) in the search results.**
    
    *Example Searches:*
    1. `Cereal` *(Checks for high sugar & hidden phosphorus)*
    2. `Cheese` *(Checks for unpasteurized pregnancy risks & high sodium)*
    3. `Fruit Juice` *(Checks for high sugar spikes)*
    4. `Deli Meat` *(Checks for Listeria risk & extreme sodium)*
    5. `White Rice` *(Safe for kidneys but flags high glycemic index)*
    """)
    sq = st.text_input("Search Product Name or Ingredient")
    cols = st.columns(5)
    min_pro = cols[0].number_input("Min Protein (g)", 0, 1000, 0)
    min_fat = cols[1].number_input("Min Fat (g)", 0, 1000, 0)
    min_carb = cols[2].number_input("Min Carbs (g)", 0, 1000, 0)
    max_sug = cols[3].number_input("Max Sugar (g)", 0, 1000, 1000)
    
    # Load dynamically fetched limit to prevent Pandas Styler crash
    pd.set_option("styler.render.max_elements", 5000000)
    opts = [10, 50, 100, 500, 1000]
    
    user_lim_str = get_user_limit(st.session_state["authenticated_user"])
    user_lim_val = 1000 if user_lim_str == "All" else int(user_lim_str)
    if user_lim_val not in opts: user_lim_val = 50
    idx = opts.index(user_lim_val)
    limit_rc = cols[4].selectbox("Limit Results", opts, index=idx)
    
    if st.button("Search Database") and sq and conn_reader:
        notifier.send_alert(f"Medical DB Search Executed: {sq}")
        with st.spinner("Processing massive clinical query..."):
            try:
                with conn_reader.cursor() as cursor:
                    l_str = "" if limit_rc == "All" else f"LIMIT {limit_rc}"
                    query = f"""
                        SELECT c.code, c.product_name, c.generic_name, c.brands, c.ingredients_text,
                               a.allergens,
                               m.`energy-kcal_100g`, m.proteins_100g, m.fat_100g, m.carbohydrates_100g, m.sugars_100g, m.fiber_100g, m.sodium_100g, m.salt_100g, m.cholesterol_100g,
                               v.`vitamin-a_100g`, v.`vitamin-b1_100g`, v.`vitamin-b2_100g`, v.`vitamin-pp_100g`, v.`vitamin-b6_100g`, v.`vitamin-b9_100g`, v.`vitamin-b12_100g`, v.`vitamin-c_100g`, v.`vitamin-d_100g`, v.`vitamin-e_100g`, v.`vitamin-k_100g`,
                               min.calcium_100g, min.iron_100g, min.magnesium_100g, min.potassium_100g, min.zinc_100g
                        FROM (
                            SELECT code, product_name, generic_name, brands, ingredients_text
                            FROM food_db.products_core
                            WHERE MATCH(product_name, ingredients_text) AGAINST(%s IN BOOLEAN MODE)
                            AND product_name IS NOT NULL AND product_name != '' AND product_name != 'None'
                            {l_str}
                        ) c
                        LEFT JOIN food_db.products_allergens a ON c.code = a.code
                        LEFT JOIN food_db.products_macros m ON c.code = m.code
                        LEFT JOIN food_db.products_vitamins v ON c.code = v.code
                        LEFT JOIN food_db.products_minerals min ON c.code = min.code
                        WHERE (m.proteins_100g >= %s OR m.proteins_100g IS NULL)
                        AND (m.fat_100g >= %s OR m.fat_100g IS NULL)
                        AND (m.carbohydrates_100g >= %s OR m.carbohydrates_100g IS NULL)
                        AND (m.sugars_100g <= %s OR m.sugars_100g IS NULL)
                    """
                    sq_bool = " ".join([f"+{w}" for w in sq.split()])
                    start_time = time.time()
                    cursor.execute(query, (sq_bool, min_pro, min_fat, min_carb, max_sug))
                    results = cursor.fetchall()
                    elapsed = time.time() - start_time
                    st.caption(f"⏱️ DB Query Executed in {elapsed:.3f} seconds")
                    
                    if results:
                        # Fetch EAV Medical Profile
                        eav_profile = get_eav_profile(st.session_state["authenticated_user"])
                        df = pd.DataFrame(results)
                        
                        st.markdown("### 🛠️ Dynamic Column Display")
                        default_columns = [
                            'code', 'product_name', 'generic_name', 'brands', 'allergens', 'ingredients_text',
                            'proteins_100g', 'fat_100g', 'carbohydrates_100g', 'sugars_100g', 'sodium_100g', 'energy-kcal_100g',
                            'vitamin-c_100g', 'iron_100g', 'calcium_100g'
                        ]
                        all_fetched_cols = list(df.columns)
                        valid_defaults = [c for c in default_columns if c in all_fetched_cols]
                        
                        if "selected_columns" not in st.session_state or st.button("Reset Default Columns"):
                            st.session_state["selected_columns"] = valid_defaults
                            st.rerun()
                            
                        chosen_cols = st.multiselect("Customize Dataset View", all_fetched_cols, default=st.session_state["selected_columns"], key="multi_cols")
                        st.session_state["selected_columns"] = chosen_cols
                        
                        # Filter dataframe gracefully, but we retain a copy for background analytics
                        df_display = df[chosen_cols].copy()
                        warnings_col = []
                        
                        for idx, row in df.iterrows():
                            warns = []
                            ing_text = str(row['ingredients_text']).lower()
                            all_text = str(row['allergens']).lower()
                            
                            for param in eav_profile:
                                cat = param['name'].lower()
                                val = param['value']
                                
                                # Disease Analytics
                                if cat == 'illness':
                                    if val == 'diabetes' and pd.notnull(row.get('sugars_100g')) and float(row['sugars_100g']) > 10.0:
                                        warns.append("⚠️ High Sugar (Diabetes)")
                                    if (val == 'hypertension' or val == 'high bp') and pd.notnull(row.get('sodium_100g')) and float(row['sodium_100g']) > 1.5:
                                        warns.append("⚠️ High Salt (Hypertension)")
                                    if val == 'scurvy' and pd.notnull(row.get('vitamin-c_100g')) and float(row['vitamin-c_100g']) > 0.005:
                                        warns.append("💚 High Vitamin C (Scurvy Recommended)")
                                    if val == 'anemia' and pd.notnull(row.get('iron_100g')) and float(row['iron_100g']) > 0.002:
                                        warns.append("💚 High Iron (Anemia Recommended)")
                                        
                                # Condition Analytics
                                if cat == 'condition':
                                    if val == 'pregnant':
                                        if ('cru' in ing_text or 'raw' in ing_text or 'viande crue' in ing_text):
                                            warns.append("⚠️ Raw Foods (Pregnancy Toxoplasmosis)")
                                        if pd.notnull(row.get('iron_100g')) and float(row['iron_100g']) > 0.002:
                                            warns.append("💚 Med-High Iron (Pregnancy Health)")
                                    if val == 'low fat' and pd.notnull(row.get('fat_100g')) and float(row['fat_100g']) > 20.0:
                                        warns.append("⚠️ High Fat")
                                    if val == 'osteoporosis' and pd.notnull(row.get('calcium_100g')) and float(row['calcium_100g']) > 0.1:
                                        warns.append("💚 High Calcium (Bone Health)")
                                        
                            if eav_data:
                                ing_text = str(row.get('ingredients_text', '')).lower()
                                all_text = str(row.get('allergens', '')).lower()
                                product_name_text = str(row.get('product_name', '')).lower()
                                
                                for e in eav_data:
                                    cat = str(e['name']).lower()
                                    val = str(e['value']).lower()
                                    
                                    # Clinical Trace Checks...
                                    if cat == 'condition' and (val == 'pregnant' or val == 'pregnancy' or val == 'breastfeeding'):
                                        # Forbidden / High Risk (Toxoplasmosis & Listeria)
                                        if any(x in ing_text or x in product_name_text for x in ['cru', 'raw', 'viande crue', 'sushi', 'sashimi', 'poisson cru']):
                                            warns.append("⚠️ Forbidden: Raw Meat/Fish (Toxoplasmosis/Parasite Risk)")
                                        if any(x in ing_text or x in product_name_text for x in ['lait cru', 'unpasteurized', 'non pasteurisé']):
                                            warns.append("⚠️ Forbidden: Unpasteurized Dairy (Listeria Risk)")
                                        if any(x in ing_text or x in product_name_text for x in ['alcool', 'wine', 'alcohol', 'beer']):
                                            warns.append("⚠️ Forbidden: Contains Alcohol")
                                            
                                        # Recommended (Iron & Calcium)
                                        if float(row.get('iron_100g', 0) or 0) > 0.003:
                                            warns.append("💚 Recommended: High Iron (Pregnancy Health)")
                                        if float(row.get('calcium_100g', 0) or 0) > 0.120:
                                            warns.append("💚 Recommended: High Calcium (Bone Health / Breastfeeding)")
                                    
                                    if cat == 'illness' and val == 'osteoporosis':
                                        if float(row.get('calcium_100g', 0) or 0) < 0.120:
                                            warns.append("⚠️ Low Calcium (Osteoporosis Risk)")
                                        else:
                                            warns.append("💚 Recommended (High Calcium)")
                                            
                                    if cat == 'illness' and val == 'scurvy':
                                        if float(row.get('vitamin-c_100g', 0) or 0) < 0.010:
                                            warns.append("⚠️ Low Vitamin C (Scurvy Risk)")
                                        else:
                                            warns.append("💚 Recommended (High Vitamin C)")
                                            
                                    if cat == 'diet' and val in ['vegan', 'vegetarian']:
                                        if any(x in ing_text for x in ['meat', 'beef', 'chicken', 'fish', 'gelatin', 'whey', 'pork', 'porc', 'poulet']):
                                            warns.append("⚠️ Contains Animal Products")
                                    if cat == 'diet' and val == 'halal':
                                        if any(x in ing_text for x in ['pork', 'pig', 'porc', 'wine', 'alcohol', 'beer', 'vin']):
                                            warns.append("⚠️ Probable Haram Ingredients (e.g. Pork/Wine)")
                                            
                                    if cat in ['dislike', 'allergy']:
                                        if val in ing_text or val in all_text or val in product_name_text:
                                            warns.append(f"⚠️ Contains: {val.upper()}")
                                            
                            warnings_col.append(" | ".join(list(set(warns))) if warns else "✅ Safe for Profile")
                            
                        df_display.insert(0, 'Medical Warning', warnings_col)
                        styled_df = df_display.style.apply(highlight_medical_warnings, axis=1)

                        st.success(f"Analysed {len(results)} records utilizing dynamic Partitions!")
                        st.dataframe(styled_df, use_container_width=True)
                        
                        if st.button("🤖 Ask AI to Evaluate This Table"):
                            with st.spinner("AI is dynamically evaluating these records against your profile..."):
                                user_eav = get_eav_profile(st.session_state["authenticated_user"])
                                profile_text = ", ".join([f"{p['name']}: {p['value']}" for p in user_eav]) if user_eav else "None"
                                eval_prompt = f"The user has this profile: {profile_text}. Evaluate these foods and state which are highly recommended or strictly forbidden: {df_display.to_dict('records')}. Provide a direct answer. Skip all thinking, reasoning, and pleasantries."
                                try:
                                    response_stream = ollama.chat(model='llama3.2:1b', messages=[{'role': 'user', 'content': eval_prompt}], stream=True)
                                    st.write_stream(chunk['message']['content'] for chunk in response_stream)
                                except Exception as e:
                                    error_msg = str(e).lower()
                                    if "404" in error_msg or "not found" in error_msg:
                                        st.warning("⚠️ The AI engine is currently downloading its core models in the background. Please wait a minute and try again!")
                                    else:
                                        st.error(f"AI Evaluation Failed: {e}")
                    else:
                        st.warning("No products found matching those strict terms.")
            except Exception as e: st.error(f"SQL/Pandas Error: {e}")

with tab_plate:
    st.subheader("🍽️ My Plate Builder")
    st.info("""
    ℹ️ **How to use this feature (Examples & Logic)**
    **Plate Builder Logic:**
    1. Create a New Plate.
    2. Search for exact food words (e.g. 'chicken', 'egg').
    3. Add the food with a specific portion (e.g. '150g').
    4. The system calculates the combined macros.
    5. Use the 🗑️ buttons to delete incorrect items or entire plates.
    
    *Example Plates:*
    1. `150g White Rice` + `50g Chicken Breast` + `100g Green Beans`
    2. `200g Potatoes` + `100g Tomatoes` + `100g Beef`
    3. `100g Spinach Salad` + `50g Feta Cheese`
    4. `200g Lentils` + `100g Quinoa`
    5. `100g Apple` + `30g Almonds`
    """)
    uid = get_user_id(st.session_state["authenticated_user"])
    conn = get_db_connection('app_auth')
    if conn and uid:
        with conn.cursor() as cursor:
            cursor.execute("SELECT id, plate_name FROM plates WHERE user_id = %s", (uid,))
            plates = cursor.fetchall()
            
            with st.expander("➕ Create a New Plate"):
                new_plate_name = st.text_input("Plate Name")
                if st.button("Create Plate"):
                    cursor.execute("INSERT INTO plates (user_id, plate_name) VALUES (%s, %s)", (uid, new_plate_name))
                    conn.commit()
                    st.session_state["active_plate"] = new_plate_name
                    st.rerun()

            if plates:
                colA, colB = st.columns([4, 1])
                plate_names = [p['plate_name'] for p in plates]
                default_idx = plate_names.index(st.session_state["active_plate"]) if "active_plate" in st.session_state and st.session_state["active_plate"] in plate_names else 0
                selected_plate = colA.selectbox("Select Active Plate", plate_names, index=default_idx)
                st.session_state["active_plate"] = selected_plate
                active_p_id = next(p['id'] for p in plates if p['plate_name'] == selected_plate)
                
                if colB.button("🗑️ Delete Plate"):
                    cursor.execute("DELETE FROM plates WHERE id = %s", (active_p_id,))
                    conn.commit()
                    if "active_plate" in st.session_state: del st.session_state["active_plate"]
                    st.rerun()
                
                cursor.execute("""
                    SELECT i.id, i.product_code, MAX(i.quantity_grams) as quantity_grams, MAX(p.product_name) as product_name, MAX(m.proteins_100g) as proteins_100g, MAX(m.fat_100g) as fat_100g, MAX(m.carbohydrates_100g) as carbohydrates_100g 
                    FROM plate_items i LEFT JOIN products_core p ON i.product_code = p.code LEFT JOIN products_macros m ON i.product_code = m.code WHERE i.plate_id = %s
                    GROUP BY i.id, i.product_code
                """, (active_p_id,))
                items = cursor.fetchall()
                if items:
                    for i in items:
                        c1, c2 = st.columns([5, 1])
                        c1.markdown(f"<li><b>{i['quantity_grams']}g</b> of {i['product_name']} (Pro: {i['proteins_100g'] or 0}g)</li>", unsafe_allow_html=True)
                        if c2.button("🗑️", key=f"del_item_{i['id']}"):
                            cursor.execute("DELETE FROM plate_items WHERE id = %s", (i['id'],))
                            conn.commit()
                            st.rerun()
                            
                    total_pro = sum((float(i['proteins_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items)
                    total_fat = sum((float(i['fat_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items)
                    total_carb = sum((float(i['carbohydrates_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items)
                    st.info(f"**Total Protein:** {total_pro:.1f}g | **Total Fat:** {total_fat:.1f}g | **Total Carbs:** {total_carb:.1f}g")
                
                st.markdown("---")
                st.markdown("#### ➕ Add Food to Plate")
                add_search = st.text_input("Search Exact Product Name (e.g. 'chicken', 'egg')")
                
                col_scope, col_comp = st.columns(2)
                search_scope = col_scope.radio("Search Scope", ["Auto (Cascaded)", "Product Name Only", "Both (Product & Ingredients)", "Ingredients Only"], horizontal=True)
                comp_reqs = col_comp.multiselect("Require Nutrients (Sorts by highest)", ["Iron", "Vitamin C", "Calcium", "Proteins", "Fiber"])
                
                if add_search:
                    bool_search = " ".join([f"+{w}" for w in add_search.split()])
                    start_time = time.time()
                    
                    def execute_search(match_col_override=None):
                        m_col = "product_name"
                        if match_col_override: m_col = match_col_override
                        elif "Both" in search_scope: m_col = "product_name, ingredients_text"
                        elif "Ingredients" in search_scope: m_col = "ingredients_text"
                        
                        join_min = "LEFT JOIN food_db.products_minerals min ON c.code = min.code" if any(n in comp_reqs for n in ["Iron", "Calcium"]) else ""
                        join_vit = "LEFT JOIN food_db.products_vitamins v ON c.code = v.code" if "Vitamin C" in comp_reqs else ""
                        
                        r_clauses, o_clauses = [], []
                        if "Iron" in comp_reqs: r_clauses.append("min.iron_100g > 0"); o_clauses.append("min.iron_100g DESC")
                        if "Vitamin C" in comp_reqs: r_clauses.append("v.`vitamin-c_100g` > 0"); o_clauses.append("v.`vitamin-c_100g` DESC")
                        if "Calcium" in comp_reqs: r_clauses.append("min.calcium_100g > 0"); o_clauses.append("min.calcium_100g DESC")
                        if "Proteins" in comp_reqs: r_clauses.append("m.proteins_100g > 0"); o_clauses.append("m.proteins_100g DESC")
                        if "Fiber" in comp_reqs: r_clauses.append("m.fiber_100g > 0"); o_clauses.append("m.fiber_100g DESC")
                        
                        wh_comp = " AND " + " AND ".join(r_clauses) if r_clauses else ""
                        order_by = "ORDER BY " + ", ".join(o_clauses) if o_clauses else ""
                        
                        sql = f"""
                            SELECT c.code, c.product_name
                            FROM (
                                SELECT code, product_name
                                FROM food_db.products_core
                                WHERE MATCH({m_col}) AGAINST(%s IN BOOLEAN MODE)
                                AND product_name IS NOT NULL AND product_name != '' AND product_name != 'None'
                                LIMIT 100
                            ) c
                            JOIN food_db.products_macros m ON c.code = m.code
                            {join_min}
                            {join_vit}
                            WHERE m.proteins_100g IS NOT NULL AND m.fat_100g IS NOT NULL AND m.carbohydrates_100g IS NOT NULL
                            {wh_comp}
                            {order_by}
                            LIMIT 15
                        """
                        cursor.execute(sql, (bool_search,))
                        return cursor.fetchall()

                    search_res = execute_search()
                    
                    if not search_res and search_scope == "Auto (Cascaded)":
                        st.warning("No product found in names, so I am looking into the ingredients...")
                        search_res = execute_search("ingredients_text")
                        
                    elapsed = time.time() - start_time
                    st.caption(f"⏱️ Plate Search Executed in {elapsed:.3f} seconds")
                    if search_res:
                        options = {f"{r['product_name']} ({r['code']})": r for r in search_res}
                        selected_str = st.selectbox("Select Product", list(options.keys()))
                        selected_product = options[selected_str]
                        
                        add_amount_str = st.text_input("Portion Quantity (e.g., '100g', '2 tbsp', '1.5 cups', '1 pinch')", value="100g")
                        
                        if st.button("Add Item to Plate"):
                            # Use UnitConverter to parse
                            grams = UnitConverter.parse_and_convert(add_amount_str, product_name=selected_product['product_name'])
                            if grams is not None:
                                cursor.execute("INSERT INTO plate_items (plate_id, product_code, quantity_grams) VALUES (%s, %s, %s)", 
                                              (active_p_id, selected_product['code'], grams))
                                conn.commit()
                                st.success(f"Added {grams}g of {selected_product['product_name']}!")
                                st.rerun()
                            else:
                                st.error("Could not parse unit. Please use format like '100g' or '1 cup'.")
                    else:
                        st.warning("No products found.")

with tab_planner:
    st.subheader("🤖 AI Meal Planner")
    st.info("""
    ℹ️ **How to use this feature (Examples)**
    **Your active conditions are automatically applied to the generated menu.**
    
    *Example Prompts:*
    1. "Generate a full day meal plan for me. I am pregnant, diabetic, and have kidney disease."
    2. "Plan a pregnancy-safe dinner that won't spike my blood sugar."
    3. "I need a high-iron lunch that is safe for my kidneys."
    4. "Plan a breakfast without dairy that is kidney-friendly."
    5. "Give me a 3-day meal prep plan ensuring no raw fish, controlled protein portions, and steady complex carbs."
    """)
    p_col1, p_col2, p_col3 = st.columns(3)
    target_cal = p_col1.number_input("Target Daily Calories (kcal)", 1000, 5000, 2000, 50)
    diet_pref = p_col2.selectbox("Dietary Preference", ["Omnivore", "Vegetarian", "Vegan", "Keto", "Paleo"])
    meal_count = p_col3.slider("Number of Meals", 2, 6, 3)
    extra_notes = st.text_input("Any additional allergies or goals?")
    
    if st.button("Generate Professional Menu"):
        with st.spinner("Executing Lightning-Fast Context RAG..."):
            user_eav = get_eav_profile(st.session_state["authenticated_user"])
            profile_text = ", ".join([f"{p['name']}: {p['value']}" for p in user_eav]) if user_eav else "None"
            
            # Pre-fetch database context directly without using AI tools!
            # Enforce the strict clinical constraints directly via SQL
            db_context = search_nutrition_db(diet_pref, user_eav)
            
            sys_prompt = f"""You are a professional clinical Dietitian planner. Target: {target_cal}kcal over {meal_count} meals. 
            Dietary constraint: {diet_pref}. Additional notes: {extra_notes}.
            Health profile: {profile_text}. 
            
            CRITICAL INSTRUCTIONS:
            - You MUST formulate the menu using ONLY the following real database items retrieved for you: {db_context}
            - Output the menu beautifully formatted as a Markdown Table.
            - Columns MUST be: | Meal Time | Exact Food | Portion Size | Calories | Protein |
            - Do NOT output JSON. Do NOT use tool calls.
            - Provide a direct answer. Skip all thinking, reasoning, and pleasantries.
            """
            
            temp_messages = [{'role': 'system', 'content': sys_prompt}, {'role': 'user', 'content': 'Generate my meal plan as a markdown table.'}]
            
            # Stream the response instantly!
            try:
                response_stream = ollama.chat(model='llama3.2:1b', messages=temp_messages, stream=True)
                st.markdown("### 📋 Your Professional Meal Plan")
                st.write_stream(chunk['message']['content'] for chunk in response_stream)
            except Exception as e:
                error_msg = str(e).lower()
                if "404" in error_msg or "not found" in error_msg:
                    st.warning("⚠️ The AI engine is currently downloading its core models in the background. Please wait a minute and try again!")
                else:
                    st.error(f"AI Generation Failed: {e}")

if conn_reader: conn_reader.close()