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@@ -1,1221 +1,1247 @@
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-# $Id$
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-# $Author$
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-# $log$
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-#ident "@(#)LocalFoodAI:app.py:$Format:%D:%ci:%cN:%h$"
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-#ident "@(#)$Format:LocalFoodAI:app.py:%an:%ae:%ad:%cn:%ce:%cd:%H:%D:%N$"
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-import streamlit as st
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-import extra_streamlit_components as stx
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-import subprocess
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-import pymysql
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-import bcrypt
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-import random
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-import string
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-import time
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-import os
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-import pandas as pd
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-import html
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-from snmp_notifier import notifier
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-from unit_converter import UnitConverter
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-from fpdf import FPDF
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-import myloginpath
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-import ollama
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-import requests
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-import smtplib
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-from email.message import EmailMessage
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-from typing import Optional, List, Dict, Any, Tuple
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-import threading
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-import os
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-
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-ACTIVE_MODEL = os.environ.get('LLM_MODEL', 'llama3.2-vision:11b')
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-
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-def strip_scratchpad(text: str) -> str:
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- import re
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- # Strip out the XML <scratchpad> tag and everything in between, non-greedily
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- clean_text = re.sub(r'<scratchpad>.*?</scratchpad>', '', text, flags=re.DOTALL)
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- return clean_text.strip()
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-
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-def detect_allergens_from_text(name: str, ingredients: str) -> set:
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- import re
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- detected = set()
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- text = (name + " " + ingredients).lower()
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- mappings = {
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- "peanut": "Peanuts",
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- "cacahuète": "Peanuts",
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- "cacahuete": "Peanuts",
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- "egg": "Eggs",
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- "oeuf": "Eggs",
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- "œuf": "Eggs",
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- "milk": "Milk",
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- "lait": "Milk",
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- "butter": "Milk",
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- "beurre": "Milk",
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- "cheese": "Milk",
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- "fromage": "Milk",
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- "cream": "Milk",
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- "crème": "Milk",
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- "creme": "Milk",
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- "wheat": "Wheat",
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- "blé": "Wheat",
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- "ble": "Wheat",
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- "gluten": "Gluten",
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- "soy": "Soy",
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- "soja": "Soy",
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- "almond": "Tree Nuts",
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- "amande": "Tree Nuts",
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- "cashew": "Tree Nuts",
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- "walnut": "Tree Nuts",
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- "noix": "Tree Nuts",
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- "hazelnut": "Tree Nuts",
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- "noisette": "Tree Nuts",
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- "pecan": "Tree Nuts",
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- "pistachio": "Tree Nuts",
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- "pistache": "Tree Nuts",
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- "fish": "Fish",
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- "poisson": "Fish",
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- "salmon": "Fish",
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- "saumon": "Fish",
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- "tuna": "Fish",
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- "thon": "Fish",
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- "shrimp": "Shellfish",
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- "crevette": "Shellfish",
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- "crab": "Shellfish",
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- "crabe": "Shellfish",
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- "lobster": "Shellfish",
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- "homard": "Shellfish",
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- "mussel": "Shellfish",
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- "moule": "Shellfish",
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- "oyster": "Shellfish",
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- "huître": "Shellfish",
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- "huitre": "Shellfish",
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- "sesame": "Sesame",
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- "sésame": "Sesame",
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- "mustard": "Mustard",
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- "moutarde": "Mustard",
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- "celery": "Celery",
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- "céleri": "Celery",
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- "celeri": "Celery",
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- "lupin": "Lupin",
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- "mollusc": "Molluscs",
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- "mollusque": "Molluscs",
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- "sulphite": "Sulphites",
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- "sulfite": "Sulphites"
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- }
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- for keyword, allergen in mappings.items():
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- pattern = r'\b' + re.escape(keyword) + r's?\b'
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- if re.search(pattern, text):
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- detected.add(allergen)
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- return detected
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-
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-def filter_scratchpad_stream(stream, raw_accumulator=None):
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- buffer = ""
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- in_scratchpad = False
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- for chunk in stream:
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- content = chunk['message']['content']
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- if raw_accumulator is not None:
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- raw_accumulator.append(content)
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- buffer += content
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-
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- while True:
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- if not in_scratchpad:
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- start_idx = buffer.find("<scratchpad>")
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- if start_idx != -1:
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- if start_idx > 0:
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- yield buffer[:start_idx]
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- yield "\n\n> 💭 **AI Thinking Process:**\n> "
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- buffer = buffer[start_idx + 12:]
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- in_scratchpad = True
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- else:
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- yield_len = len(buffer) - 11
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- if yield_len > 0:
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- yield buffer[:yield_len]
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- buffer = buffer[yield_len:]
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- break
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- else:
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- end_idx = buffer.find("</scratchpad>")
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- if end_idx != -1:
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- scratch_content = buffer[:end_idx]
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- scratch_content_formatted = scratch_content.replace("\n", "\n> ")
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- yield scratch_content_formatted
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- yield "\n\n"
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- buffer = buffer[end_idx + 13:]
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- in_scratchpad = False
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- else:
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- yield_len = len(buffer) - 12
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- if yield_len > 0:
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- scratch_content = buffer[:yield_len]
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- scratch_content_formatted = scratch_content.replace("\n", "\n> ")
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- yield scratch_content_formatted
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- buffer = buffer[yield_len:]
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- break
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- if buffer:
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- if in_scratchpad:
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- yield buffer.replace("\n", "\n> ")
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- else:
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- yield buffer
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-
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-def pull_model_bg():
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- try: ollama.pull(ACTIVE_MODEL)
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- except: pass
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-threading.Thread(target=pull_model_bg, daemon=True).start()
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-
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-def local_web_search(query: str) -> str:
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- try:
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- req = requests.get(f'http://127.0.0.1:8080/search', params={'q': query, 'format': 'json'})
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- if req.status_code == 200:
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- data = req.json()
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- results = data.get('results', [])
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- if not results: return f"No results found on the web for '{query}'."
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- snippets = [f"Source: {r.get('url')}\nContent: {r.get('content')}" for r in results[:3]]
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- return "\n\n".join(snippets)
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- return "Search engine returned an error."
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- except Exception as e: return f"Local search engine unreachable: {e}"
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-
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-search_tool_schema = {
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- 'type': 'function',
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- 'function': {
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- 'name': 'local_web_search',
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- 'description': 'Search the internet for info not in DB.',
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- 'parameters': {'type': 'object', 'properties': {'query': {'type': 'string'}}, 'required': ['query']},
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- },
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-}
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-
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-def search_nutrition_db(query: str, user_eav=None) -> str:
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- conn = get_db_connection('app_reader')
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- if not conn: return "Database connection failed."
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- try:
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- with conn.cursor() as cursor:
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- # Dynamically build strictly-enforced clinical SQL filters
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- clinical_filters = ""
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- if user_eav:
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- for p in user_eav:
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- name = p['name'].lower()
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- val = p['value'].lower()
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- if name in ['condition', 'illness']:
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- if val == 'diabetes': clinical_filters += " AND m.sugars_100g < 5.0"
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- elif 'kidney' in val: clinical_filters += " AND m.proteins_100g < 15.0"
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- elif 'hypertension' in val: clinical_filters += " AND m.sodium_100g < 0.2"
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- elif name in ['diet', 'religious', 'preference']:
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- if val == 'kosher': clinical_filters += " AND c.ingredients_text NOT LIKE '%pork%' AND c.ingredients_text NOT LIKE '%shellfish%'"
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- 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%'"
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- 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%'"
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-
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- sql = f"""
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- SELECT c.code, c.product_name, m.proteins_100g, m.fat_100g, m.carbohydrates_100g, m.sugars_100g
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- FROM food_db.products_core c
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- LEFT JOIN food_db.products_macros m ON c.code = m.code
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- WHERE MATCH(c.product_name, c.ingredients_text) AGAINST(%s IN BOOLEAN MODE)
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- AND c.product_name IS NOT NULL AND c.product_name != '' AND c.product_name != 'None'
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- {clinical_filters}
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- """
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- bool_query = " ".join([f"+{w}" for w in query.split()])
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- cursor.execute(sql, (bool_query,))
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- results = cursor.fetchall()
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- if not results: return f"No database records found for '{query}'."
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-
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- snippets = []
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- for r in results:
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- pro = float(r['proteins_100g'] or 0)
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- fat = float(r['fat_100g'] or 0)
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- carb = float(r['carbohydrates_100g'] or 0)
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- sug = float(r['sugars_100g'] or 0)
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- snippets.append(f"- {r['product_name']}: Protein {pro:.2f}g, Fat {fat:.2f}g, Carbs {carb:.2f}g, Sugars {sug:.2f}g (per 100g)")
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- return "\n".join(snippets)
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- except Exception as e:
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- return f"Database query failed: {e}"
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- finally:
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- conn.close()
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-
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-db_search_tool_schema = {
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- 'type': 'function',
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- 'function': {
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- 'name': 'search_nutrition_db',
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- 'description': 'Search the local medical nutrition database for product macros and ingredients. ALWAYS prioritize this over web search.',
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- 'parameters': {'type': 'object', 'properties': {'query': {'type': 'string', 'description': 'The product or food name to search for (e.g. apple, chicken, bread)'}}, 'required': ['query']},
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- },
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-}
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-
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-def get_db_connection(login_path):
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- try:
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- import os
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- db_host = os.environ.get('DB_HOST')
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- # Check if environment variables exist for this login path
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- db_user = os.environ.get(f'{login_path.upper()}_USER') or os.environ.get('DB_USER')
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- db_pass = os.environ.get(f'{login_path.upper()}_PASS') or os.environ.get('DB_PASS')
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-
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- if db_host and db_user and db_pass:
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- return pymysql.connect(
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- host=db_host,
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- user=db_user,
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- password=db_pass,
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- database='food_db',
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- cursorclass=pymysql.cursors.DictCursor
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- )
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-
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- conf = myloginpath.parse(login_path)
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- if not conf or not conf.get('user'):
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- 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.")
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- return None
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-
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- return pymysql.connect(
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- host=conf.get('host', '127.0.0.1'),
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- user=conf.get('user'),
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- password=conf.get('password'),
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- database='food_db',
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- cursorclass=pymysql.cursors.DictCursor
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- )
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- except Exception as e:
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- st.error(f"Connection Failed: {e}")
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- return None
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-
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-from contextlib import contextmanager
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-
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-@contextmanager
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-def db_cursor(login_path: str):
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- conn = get_db_connection(login_path)
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- if not conn:
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- yield None
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- return
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- try:
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- with conn.cursor() as cursor:
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- yield cursor
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- conn.commit()
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- except Exception as e:
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- conn.rollback()
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- st.error(f"Database query error: {e}")
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- raise e
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- finally:
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- conn.close()
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-
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-def verify_login(username: str, password: str) -> bool:
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- with db_cursor('app_auth') as cursor:
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- if not cursor: return False
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- cursor.execute("SELECT password_hash FROM users WHERE username = %s", (username,))
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- result = cursor.fetchone()
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- if result: return bcrypt.checkpw(password.encode('utf-8'), result['password_hash'].encode('utf-8'))
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- return False
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-
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-def get_user_id(username: str) -> Optional[int]:
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- with db_cursor('app_auth') as cursor:
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- if not cursor: return None
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- cursor.execute("SELECT id FROM users WHERE username = %s", (username,))
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- result = cursor.fetchone()
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- return result['id'] if result else None
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-
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-def get_eav_profile(username: str) -> List[Dict[str, Any]]:
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- uid = get_user_id(username)
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- if not uid: return []
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- with db_cursor('app_auth') as cursor:
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- if not cursor: return []
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- 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,))
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- return cursor.fetchall()
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-
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-def get_user_limit(username: str) -> str:
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- with db_cursor('app_auth') as cursor:
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- if not cursor: return "50"
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- cursor.execute("SELECT search_limit FROM users WHERE username = %s", (username,))
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- result = cursor.fetchone()
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- return result['search_limit'] if (result and result['search_limit']) else "50"
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-
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-def register_user(username: str, password: str, email: str) -> bool:
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- hashed = bcrypt.hashpw(password.encode('utf-8'), bcrypt.gensalt()).decode('utf-8')
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- try:
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- with db_cursor('app_auth') as cursor:
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- if not cursor: return False
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- cursor.execute("INSERT INTO users (username, password_hash, email) VALUES (%s, %s, %s)", (username, hashed, email))
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- send_email(email, "Welcome to Local Food AI", f"Hello {username}, your account was securely created!", to_name=username.title())
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- return True
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- except pymysql.err.IntegrityError:
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- return False
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-
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-def send_email(to_email: str, subject: str, body: str, to_name: str = "User") -> Any:
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- msg = EmailMessage()
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- msg.set_content(body)
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- msg['Subject'] = subject
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- msg['From'] = '"Clinical Food AI System" <security@localfoodai.com>'
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- msg['To'] = f'"{to_name}" <{to_email}>'
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-
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- for attempt in range(5):
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- try:
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- s = smtplib.SMTP('localhost', 25)
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- s.send_message(msg)
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- s.quit()
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- return True
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- except Exception as e:
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- if attempt == 4:
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- return f"SMTP Delivery Failed: {str(e)}"
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- time.sleep(2)
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- return "Unknown Error Occurred"
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-
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-def reset_password(username: str, email: str) -> Any:
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- with db_cursor('app_auth') as cursor:
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- if not cursor: return False
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- cursor.execute("SELECT id, email FROM users WHERE username = %s", (username,))
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- user = cursor.fetchone()
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- if user and user['email'] == email:
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- new_pass = ''.join(random.choices(string.ascii_letters + string.digits, k=10))
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- hashed = bcrypt.hashpw(new_pass.encode('utf-8'), bcrypt.gensalt()).decode('utf-8')
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- cursor.execute("UPDATE users SET password_hash = %s WHERE id = %s", (hashed, user['id']))
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- status = send_email(email, "Password Reset", f"Your new temporary password is: {new_pass}", to_name=username.title())
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- return True if status is True else status
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- return False
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-
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-# UI Theming
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-def render_version():
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- st.markdown("---")
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- try:
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- if os.path.exists('git_version.txt'):
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- with open('git_version.txt', 'r') as f: git_version = f.read().strip()
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- else:
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- git_version = subprocess.check_output(['git', 'describe', '--tags']).decode('utf-8').strip()
|
|
|
- except Exception:
|
|
|
- git_version = "v1.3.0"
|
|
|
- st.caption(f"🚀 Version: {git_version}")
|
|
|
-
|
|
|
- try:
|
|
|
- if os.path.exists('git_id.txt'):
|
|
|
- with open('git_id.txt', 'r') as f: git_id = f.read().strip()
|
|
|
- else:
|
|
|
- git_id = subprocess.check_output(['git', 'log', '-1', '--format=%cd %h', 'app.py']).decode('utf-8').strip()
|
|
|
- except Exception:
|
|
|
- git_id = "Unknown"
|
|
|
- st.caption(f"📅 Git ID: {git_id}")
|
|
|
-
|
|
|
-st.set_page_config(page_title="Food AI Explorer", page_icon="🍔", layout="wide")
|
|
|
-
|
|
|
-cookie_manager = stx.CookieManager(key="cookie_manager")
|
|
|
-
|
|
|
-# Wait for cookies to load
|
|
|
-cookies = cookie_manager.get_all()
|
|
|
-if cookies is None:
|
|
|
- st.stop()
|
|
|
-
|
|
|
-# If the cookie has auth_user, set/restore session state
|
|
|
-cookie_user = cookie_manager.get(cookie="auth_user")
|
|
|
-if cookie_user:
|
|
|
- st.session_state["authenticated_user"] = cookie_user
|
|
|
-elif "authenticated_user" not in st.session_state:
|
|
|
- st.session_state["authenticated_user"] = None
|
|
|
-
|
|
|
-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; caret-color: #f8fafc !important; }
|
|
|
-</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("ℹ️ Welcome"):
|
|
|
- st.info("Welcome to the secure Local Food AI environment.")
|
|
|
-
|
|
|
- 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
|
|
|
- cookie_manager.delete("auth_user")
|
|
|
- time.sleep(0.5)
|
|
|
- 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"])
|
|
|
-
|
|
|
- if new_cat == "Condition":
|
|
|
- new_val = st.selectbox("Value", ["Pregnant", "Breastfeeding", "Low Fat"])
|
|
|
- elif new_cat == "Illness":
|
|
|
- new_val = st.selectbox("Value", ["Diabetes", "Hypertension", "Kidney Disease", "Osteoporosis", "Scurvy", "Anemia"])
|
|
|
- elif new_cat == "Diet":
|
|
|
- new_val = st.selectbox("Value", ["Vegan", "Vegetarian", "Kosher", "Halal", "Christian", "Good Friday", "Ash Wednesday", "Keto", "Paleo"])
|
|
|
- else:
|
|
|
- new_val = st.text_input("Value (e.g. 'peanuts', 'broccoli')").strip()
|
|
|
-
|
|
|
- new_val_clean = new_val.lower()
|
|
|
-
|
|
|
- if st.button("Add to Profile") and new_val_clean 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.lower(), new_val_clean))
|
|
|
- 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
|
|
|
- import datetime
|
|
|
- # Set cookie with 30 days expiration
|
|
|
- cookie_manager.set(
|
|
|
- "auth_user",
|
|
|
- l_user,
|
|
|
- expires_at=datetime.datetime.now() + datetime.timedelta(days=30)
|
|
|
- )
|
|
|
- time.sleep(0.2)
|
|
|
- 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"
|
|
|
-
|
|
|
- db_context = search_nutrition_db(prompt, user_eav)
|
|
|
- searxng_context = ""
|
|
|
-
|
|
|
- if "No database records found" in db_context:
|
|
|
- try:
|
|
|
- searxng_url = os.environ.get("SEARXNG_HOST", "http://searxng:8080")
|
|
|
- resp = requests.get(f"{searxng_url}/search", params={'q': prompt, 'format': 'json'}, timeout=5)
|
|
|
- if resp.status_code == 200:
|
|
|
- results = resp.json().get('results', [])
|
|
|
- if results:
|
|
|
- snippets = [r.get('content', '') for r in results[:3]]
|
|
|
- searxng_context = "Web Search Context: " + " | ".join(snippets)
|
|
|
- except Exception as e:
|
|
|
- pass
|
|
|
-
|
|
|
- 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. Use Chain of Thought reasoning, and skip pleasantries.
|
|
|
- Local Database Context: {db_context}
|
|
|
- {searxng_context}
|
|
|
- """
|
|
|
-
|
|
|
- try:
|
|
|
- temp_messages = [{"role": "system", "content": sys_prompt}] + [m for m in st.session_state.messages if m["role"] != "tool"]
|
|
|
- start_llm = time.time()
|
|
|
- response_stream = ollama.chat(model=ACTIVE_MODEL, messages=temp_messages, stream=True)
|
|
|
-
|
|
|
- with st.chat_message("assistant"):
|
|
|
- ai_reply = st.write_stream(chunk['message']['content'] for chunk in response_stream)
|
|
|
- st.caption(f"⏱️ AI response generated in {time.time() - start_llm:.2f} seconds")
|
|
|
-
|
|
|
- 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)*
|
|
|
- """)
|
|
|
- with st.form("explore_search_form"):
|
|
|
- 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)
|
|
|
-
|
|
|
- submit_search = st.form_submit_button("Search Database")
|
|
|
- if submit_search:
|
|
|
- st.session_state["trigger_search"] = True
|
|
|
-
|
|
|
- if st.session_state.get("trigger_search", False) 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) OR product_name LIKE %s)
|
|
|
- AND product_name IS NOT NULL AND product_name != '' AND product_name != 'None'
|
|
|
- ORDER BY MATCH(product_name) AGAINST(%s IN BOOLEAN MODE) DESC, MATCH(ingredients_text) AGAINST(%s IN BOOLEAN MODE) DESC
|
|
|
- {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()])
|
|
|
- sq_like = f"%{sq}%"
|
|
|
- start_time = time.time()
|
|
|
- cursor.execute(query, (sq_bool, sq_like, sq_bool, 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)
|
|
|
- df.replace(r'^\s*$', None, regex=True, inplace=True)
|
|
|
- for col in df.columns:
|
|
|
- if col.endswith('_100g'):
|
|
|
- df[col] = pd.to_numeric(df[col], errors='coerce')
|
|
|
-
|
|
|
- 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)
|
|
|
- # Only fillna with empty string on object columns to avoid Arrow float64 conversion errors
|
|
|
- for col in df_display.columns:
|
|
|
- if df_display[col].dtype == 'object':
|
|
|
- df_display[col] = df_display[col].fillna("")
|
|
|
- df_display.index = range(1, len(df_display) + 1)
|
|
|
- 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, hide_index=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"
|
|
|
- minimal_records = df_display[['product_name', 'Medical Warning']].head(10).to_dict('records')
|
|
|
- eval_prompt = f"The user has this profile: {profile_text}. Evaluate these top foods and state which are highly recommended or strictly forbidden: {minimal_records}. Provide a direct, readable clinical summary. Do not output raw JSON."
|
|
|
- try:
|
|
|
- response = ollama.chat(model=ACTIVE_MODEL, messages=[{'role': 'user', 'content': eval_prompt}], stream=True)
|
|
|
- st.write_stream(chunk['message']['content'] for chunk in response)
|
|
|
- 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. `add White Rice use 150g then add Chicken Breast use 50g add Green Beans use 100g`
|
|
|
- 2. `add Potatoes use 200g then add Tomatoes use 100g add Beef use 100g`
|
|
|
- 3. `add Spinach Salad use 100g then add Feta Cheese use 50g`
|
|
|
- 4. `add Lentils use 200g then add Quinoa use 100g`
|
|
|
- 5. `add Apple use 100g then add Almonds use 30g`
|
|
|
- """)
|
|
|
- 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()
|
|
|
-
|
|
|
- st.markdown("#### ➕ Create a New Plate")
|
|
|
- col_p1, col_p2 = st.columns([3, 1])
|
|
|
- new_plate_name = col_p1.text_input("Plate Name (e.g., 'Spaghetti Bolognese')", key="new_plate")
|
|
|
- if col_p2.button("Create Plate", use_container_width=True) and new_plate_name:
|
|
|
- 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()
|
|
|
-
|
|
|
- st.markdown("---")
|
|
|
-
|
|
|
- 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 to Edit Ingredients", 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(p.ingredients_text) as ingredients_text,
|
|
|
- MAX(m.proteins_100g) as proteins_100g, MAX(m.fat_100g) as fat_100g, MAX(m.carbohydrates_100g) as carbohydrates_100g,
|
|
|
- MAX(m.sodium_100g) as sodium_100g, MAX(m.sugars_100g) as sugars_100g, MAX(m.fiber_100g) as fiber_100g,
|
|
|
- MAX(v.`vitamin-a_100g`) as vitamin_a_100g, MAX(v.`vitamin-b1_100g`) as vitamin_b1_100g,
|
|
|
- MAX(v.`vitamin-b2_100g`) as vitamin_b2_100g, MAX(v.`vitamin-pp_100g`) as vitamin_pp_100g,
|
|
|
- MAX(v.`vitamin-b6_100g`) as vitamin_b6_100g, MAX(v.`vitamin-b9_100g`) as vitamin_b9_100g,
|
|
|
- MAX(v.`vitamin-b12_100g`) as vitamin_b12_100g, MAX(v.`vitamin-c_100g`) as vitamin_c_100g,
|
|
|
- MAX(v.`vitamin-d_100g`) as vitamin_d_100g, MAX(v.`vitamin-e_100g`) as vitamin_e_100g,
|
|
|
- MAX(v.`vitamin-k_100g`) as vitamin_k_100g,
|
|
|
- MAX(min.calcium_100g) as calcium_100g, MAX(min.iron_100g) as iron_100g,
|
|
|
- MAX(min.magnesium_100g) as magnesium_100g, MAX(min.potassium_100g) as potassium_100g,
|
|
|
- MAX(min.zinc_100g) as zinc_100g,
|
|
|
- GROUP_CONCAT(DISTINCT a.allergens SEPARATOR ', ') as allergens
|
|
|
- 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
|
|
|
- LEFT JOIN products_vitamins v ON i.product_code = v.code
|
|
|
- LEFT JOIN products_minerals min ON i.product_code = min.code
|
|
|
- LEFT JOIN products_allergens a ON i.product_code = a.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])
|
|
|
- pro = float(i['proteins_100g'] or 0) * (float(i['quantity_grams'])/100.0)
|
|
|
- fat = float(i['fat_100g'] or 0) * (float(i['quantity_grams'])/100.0)
|
|
|
- carb = float(i['carbohydrates_100g'] or 0) * (float(i['quantity_grams'])/100.0)
|
|
|
- c1.markdown(f"<li><b>{i['quantity_grams']}g</b> of {i['product_name']} (Pro: {pro:.2f}g | Fat: {fat:.2f}g | Carb: {carb:.2f}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()
|
|
|
-
|
|
|
- totals = {
|
|
|
- "Total Protein (g)": sum((float(i['proteins_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Total Fat (g)": sum((float(i['fat_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Total Carbs (g)": sum((float(i['carbohydrates_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Sodium (g)": sum((float(i['sodium_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Sugars (g)": sum((float(i['sugars_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Fiber (g)": sum((float(i['fiber_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin A (g)": sum((float(i['vitamin_a_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin B1 (g)": sum((float(i['vitamin_b1_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin B2 (g)": sum((float(i['vitamin_b2_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin B3/PP (g)": sum((float(i['vitamin_pp_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin B6 (g)": sum((float(i['vitamin_b6_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin B9 (g)": sum((float(i['vitamin_b9_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin B12 (g)": sum((float(i['vitamin_b12_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin C (g)": sum((float(i['vitamin_c_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin D (g)": sum((float(i['vitamin_d_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin E (g)": sum((float(i['vitamin_e_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Vitamin K (g)": sum((float(i['vitamin_k_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Calcium (g)": sum((float(i['calcium_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Iron (g)": sum((float(i['iron_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Magnesium (g)": sum((float(i['magnesium_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Potassium (g)": sum((float(i['potassium_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- "Zinc (g)": sum((float(i['zinc_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
- }
|
|
|
-
|
|
|
- st.markdown("---")
|
|
|
- st.markdown("### Plate Totals")
|
|
|
- metrics = list(totals.items())
|
|
|
- for idx in range(0, len(metrics), 3):
|
|
|
- cols = st.columns(3)
|
|
|
- for j in range(3):
|
|
|
- if idx + j < len(metrics):
|
|
|
- name, val = metrics[idx + j]
|
|
|
- cols[j].metric(name, f"{val:.5f}" if val < 0.1 else f"{val:.2f}")
|
|
|
-
|
|
|
- all_allergens = set()
|
|
|
- for i in items:
|
|
|
- # 1. Parse database allergens if available
|
|
|
- if i.get('allergens'):
|
|
|
- for alg in str(i['allergens']).split(','):
|
|
|
- alg_clean = alg.strip().lower()
|
|
|
- if alg_clean.startswith('en:'):
|
|
|
- alg_clean = alg_clean[3:]
|
|
|
- if alg_clean and alg_clean != 'none':
|
|
|
- all_allergens.add(alg_clean.replace('-', ' ').title())
|
|
|
-
|
|
|
- # 2. Text heuristics fallback for common allergens
|
|
|
- prod_name = str(i.get('product_name') or '')
|
|
|
- ing_text = str(i.get('ingredients_text') or '')
|
|
|
- heuristics = detect_allergens_from_text(prod_name, ing_text)
|
|
|
- all_allergens.update(heuristics)
|
|
|
-
|
|
|
- st.markdown("---")
|
|
|
- if all_allergens:
|
|
|
- st.warning(f"⚠️ **Plate Allergens Detected:** {', '.join(all_allergens)}")
|
|
|
- else:
|
|
|
- st.success("✅ **No Allergens Detected**")
|
|
|
-
|
|
|
- st.markdown("---")
|
|
|
- st.markdown("#### ➕ Add Food to Plate")
|
|
|
- with st.form("plate_add_form"):
|
|
|
- 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"])
|
|
|
-
|
|
|
- submit_add_search = st.form_submit_button("Search Food")
|
|
|
-
|
|
|
- if add_search and submit_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'
|
|
|
- ORDER BY LENGTH(product_name) ASC
|
|
|
- ) 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}
|
|
|
- """
|
|
|
- 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")
|
|
|
- st.session_state['plate_search_res'] = search_res
|
|
|
-
|
|
|
- if st.session_state.get('plate_search_res'):
|
|
|
- search_res = st.session_state['plate_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.session_state.pop('plate_search_res', None)
|
|
|
- st.rerun()
|
|
|
- else:
|
|
|
- st.error("Could not parse unit. Please use format like '100g' or '1 cup'.")
|
|
|
- elif add_search and submit_add_search:
|
|
|
- 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", 1, 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)
|
|
|
-
|
|
|
- meal_names = ["Breakfast", "Lunch", "Dinner", "Morning Snack", "Afternoon Snack", "Evening Snack"]
|
|
|
- selected_meals = ", ".join(meal_names[:int(meal_count)])
|
|
|
-
|
|
|
- sys_prompt = f"""You are a professional clinical Dietitian planner. Target: {target_cal}kcal.
|
|
|
- You MUST generate EXACTLY {meal_count} meals and NO MORE. Failure to respect the meal count is a critical clinical error.
|
|
|
- The allowed meal(s) are strictly: {selected_meals}.
|
|
|
- Dietary constraint: {diet_pref}. Additional notes: {extra_notes}.
|
|
|
- Health profile: {profile_text}.
|
|
|
-
|
|
|
- COGNITIVE SCRATCHPAD INSTRUCTIONS:
|
|
|
- - You MUST perform all your intermediate thinking, unit conversions (e.g. converting cups, tablespoons, or ounces to exact metric grams based on food density), and calorie/protein mathematical additions inside a `<scratchpad>` tag.
|
|
|
- - Format:
|
|
|
- <scratchpad>
|
|
|
- Calculations:
|
|
|
- - 1.5 cups of Cheese = X grams (density Y). Calories = A, Protein = B, Carbs = C, Fat = D.
|
|
|
- - 2 tbsp of Peanut Butter = Z grams (density C). Calories = D, Protein = E, Carbs = F, Fat = G.
|
|
|
- - Summation: Total Calories = A + D = Z kcal (vs target {target_cal}kcal). Total Protein = B + E = Fg.
|
|
|
- </scratchpad>
|
|
|
- | Meal Time | Exact Food | Portion Size | Calories | Protein | Carbs | Fat |
|
|
|
- | --- | --- | --- | --- | --- | --- | --- |
|
|
|
- ...
|
|
|
- | Global Total | All Meals | | Total Calories | Total Protein | Total Carbs | Total Fat |
|
|
|
-
|
|
|
- CRITICAL FORMATTING INSTRUCTIONS:
|
|
|
- - After the </scratchpad> closing tag, you MUST strictly output the menu formatted as a Markdown Table.
|
|
|
- - The table MUST contain exactly 7 columns separated by pipes (|): | Meal Time | Exact Food | Portion Size | Calories | Protein | Carbs | Fat |
|
|
|
- - The Portion Size MUST be reported in exactly metric grams (e.g. 200g) and NEVER in cups or oz.
|
|
|
- - The items in the table MUST be selected strictly from: {db_context}
|
|
|
- - Do NOT output JSON. Do NOT use tool calls. Skip pleasantries.
|
|
|
- """
|
|
|
-
|
|
|
- st.info("🧠 AI is analyzing nutritional synergies and generating your plan...")
|
|
|
-
|
|
|
- # Stream the response instantly!
|
|
|
- try:
|
|
|
- start_llm = time.time()
|
|
|
- response = ollama.chat(model=ACTIVE_MODEL, messages=[
|
|
|
- {'role': 'system', 'content': sys_prompt},
|
|
|
- {'role': 'user', 'content': 'Generate my meal plan as a markdown table.'}
|
|
|
- ], stream=True)
|
|
|
- raw_chunks = []
|
|
|
- clean_stream = filter_scratchpad_stream(response, raw_chunks)
|
|
|
- ai_reply = st.write_stream(clean_stream)
|
|
|
- raw_reply = "".join(raw_chunks)
|
|
|
- st.caption(f"⏱️ AI Meal Plan generated in {time.time() - start_llm:.2f} seconds")
|
|
|
-
|
|
|
- # PDF Generation
|
|
|
- def generate_pdf(text):
|
|
|
- import re
|
|
|
- # Aggressive sanitization: if a table row has 4 columns and the last contains a comma or space before 'g', split it
|
|
|
- sanitized_lines = []
|
|
|
- for line in text.split('\\n'):
|
|
|
- line = line.strip()
|
|
|
- if line.startswith('|') and line.endswith('|') and '---' not in line:
|
|
|
- cols = [c.strip() for c in line.strip('|').split('|')]
|
|
|
- # If exactly 4 columns and the last one contains calories and protein merged
|
|
|
- if len(cols) == 4 and any(char.isdigit() for char in cols[3]):
|
|
|
- # Attempt to split by comma or 'kcal'
|
|
|
- if ',' in cols[3]:
|
|
|
- split_last = cols[3].split(',', 1)
|
|
|
- cols = cols[:3] + [split_last[0].strip(), split_last[1].strip()]
|
|
|
- elif 'kcal' in cols[3].lower():
|
|
|
- split_last = re.split(r'(?<=kcal)\s+', cols[3], flags=re.IGNORECASE, maxsplit=1)
|
|
|
- if len(split_last) == 2:
|
|
|
- cols = cols[:3] + [split_last[0].strip(), split_last[1].strip()]
|
|
|
- sanitized_lines.append('| ' + ' | '.join(cols) + ' |')
|
|
|
- else:
|
|
|
- sanitized_lines.append(line)
|
|
|
- text = '\\n'.join(sanitized_lines)
|
|
|
-
|
|
|
- pdf = FPDF()
|
|
|
- pdf.add_page()
|
|
|
- pdf.set_font("Helvetica", 'B', 16)
|
|
|
- pdf.cell(0, 10, "Strict Clinical Meal Plan", new_x="LMARGIN", new_y="NEXT", align='C')
|
|
|
- pdf.ln(h=5)
|
|
|
- in_table = False
|
|
|
- table_data = []
|
|
|
-
|
|
|
- def flush_table():
|
|
|
- if not table_data: return
|
|
|
- pdf.set_font("Helvetica", size=9)
|
|
|
- # Auto-calculate col_widths based on 5 columns if present
|
|
|
- cw = (20, 40, 15, 10, 15) if len(table_data[0]) == 5 else (20, 30, 15, 10, 10, 10, 10) if len(table_data[0]) >= 7 else None
|
|
|
- try:
|
|
|
- with pdf.table(text_align="LEFT", col_widths=cw) as table:
|
|
|
- for row_data in table_data:
|
|
|
- row = table.row()
|
|
|
- for datum in row_data:
|
|
|
- row.cell(str(datum).encode('latin-1', 'replace').decode('latin-1'))
|
|
|
- except Exception as e:
|
|
|
- pdf.multi_cell(0, 8, "Table Render Error: " + str(e))
|
|
|
- table_data.clear()
|
|
|
- pdf.ln(h=5)
|
|
|
-
|
|
|
- for line in text.split('\n'):
|
|
|
- line = line.strip()
|
|
|
- if not line:
|
|
|
- flush_table()
|
|
|
- pdf.ln(h=2)
|
|
|
- continue
|
|
|
-
|
|
|
- if line.startswith('|') or ('|' in line and 'Total' in line):
|
|
|
- if not line.endswith('|'): line += ' |'
|
|
|
- if not line.startswith('|'): line = '| ' + line
|
|
|
-
|
|
|
- if '---' in line: continue
|
|
|
- cols = [col.strip() for col in line.strip('|').split('|')]
|
|
|
-
|
|
|
- # Normalize column length to prevent FPDF table crashing
|
|
|
- if table_data:
|
|
|
- target_len = len(table_data[0])
|
|
|
- while len(cols) < target_len: cols.append("")
|
|
|
- cols = cols[:target_len]
|
|
|
-
|
|
|
- table_data.append(cols)
|
|
|
- else:
|
|
|
- flush_table()
|
|
|
- pdf.set_font("Helvetica", size=11)
|
|
|
- clean_line = str(line).encode('latin-1', 'replace').decode('latin-1')
|
|
|
- pdf.multi_cell(0, 8, clean_line)
|
|
|
-
|
|
|
- flush_table()
|
|
|
-
|
|
|
- pdf_path = "/tmp/meal_plan.pdf"
|
|
|
- pdf.output(pdf_path)
|
|
|
- with open(pdf_path, "rb") as f:
|
|
|
- return f.read()
|
|
|
-
|
|
|
- st.download_button(
|
|
|
- label="📄 Download PDF Export",
|
|
|
- data=generate_pdf(strip_scratchpad(raw_reply)),
|
|
|
- file_name="Clinical_Meal_Plan.pdf",
|
|
|
- mime="application/pdf",
|
|
|
- type="primary"
|
|
|
- )
|
|
|
-
|
|
|
- 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()
|
|
|
+#ident "@(#)$Format:LocalFoodAI:app.py:%an:%ae:%ad:%cn:%ce:%cd:%H:%D:%N$"
|
|
|
+# $Id$
|
|
|
+# $Author$
|
|
|
+# $log$
|
|
|
+#ident "@(#)LocalFoodAI:app.py:$Format:%D:%ci:%cN:%h$"
|
|
|
+#ident "@(#)$Format:LocalFoodAI:app.py:%an:%ae:%ad:%cn:%ce:%cd:%H:%D:%N$"
|
|
|
+import streamlit as st
|
|
|
+import extra_streamlit_components as stx
|
|
|
+import subprocess
|
|
|
+import pymysql
|
|
|
+import bcrypt
|
|
|
+import random
|
|
|
+import string
|
|
|
+import time
|
|
|
+import os
|
|
|
+import pandas as pd
|
|
|
+import html
|
|
|
+from snmp_notifier import notifier
|
|
|
+from unit_converter import UnitConverter
|
|
|
+from fpdf import FPDF
|
|
|
+import myloginpath
|
|
|
+import ollama
|
|
|
+import requests
|
|
|
+import smtplib
|
|
|
+from email.message import EmailMessage
|
|
|
+from typing import Optional, List, Dict, Any, Tuple
|
|
|
+import threading
|
|
|
+import os
|
|
|
+
|
|
|
+ACTIVE_MODEL = os.environ.get('LLM_MODEL', 'llama3.2-vision:11b')
|
|
|
+
|
|
|
+def strip_scratchpad(text: str) -> str:
|
|
|
+ import re
|
|
|
+ # Strip out the XML <scratchpad> tag and everything in between, non-greedily
|
|
|
+ clean_text = re.sub(r'<scratchpad>.*?</scratchpad>', '', text, flags=re.DOTALL)
|
|
|
+ return clean_text.strip()
|
|
|
+
|
|
|
+def detect_allergens_from_text(name: str, ingredients: str) -> set:
|
|
|
+ import re
|
|
|
+ detected = set()
|
|
|
+ text = (name + " " + ingredients).lower()
|
|
|
+ mappings = {
|
|
|
+ "peanut": "Peanuts",
|
|
|
+ "cacahuète": "Peanuts",
|
|
|
+ "cacahuete": "Peanuts",
|
|
|
+ "egg": "Eggs",
|
|
|
+ "oeuf": "Eggs",
|
|
|
+ "œuf": "Eggs",
|
|
|
+ "milk": "Milk",
|
|
|
+ "lait": "Milk",
|
|
|
+ "butter": "Milk",
|
|
|
+ "beurre": "Milk",
|
|
|
+ "cheese": "Milk",
|
|
|
+ "fromage": "Milk",
|
|
|
+ "cream": "Milk",
|
|
|
+ "crème": "Milk",
|
|
|
+ "creme": "Milk",
|
|
|
+ "wheat": "Wheat",
|
|
|
+ "blé": "Wheat",
|
|
|
+ "ble": "Wheat",
|
|
|
+ "gluten": "Gluten",
|
|
|
+ "soy": "Soy",
|
|
|
+ "soja": "Soy",
|
|
|
+ "almond": "Tree Nuts",
|
|
|
+ "amande": "Tree Nuts",
|
|
|
+ "cashew": "Tree Nuts",
|
|
|
+ "walnut": "Tree Nuts",
|
|
|
+ "noix": "Tree Nuts",
|
|
|
+ "hazelnut": "Tree Nuts",
|
|
|
+ "noisette": "Tree Nuts",
|
|
|
+ "pecan": "Tree Nuts",
|
|
|
+ "pistachio": "Tree Nuts",
|
|
|
+ "pistache": "Tree Nuts",
|
|
|
+ "fish": "Fish",
|
|
|
+ "poisson": "Fish",
|
|
|
+ "salmon": "Fish",
|
|
|
+ "saumon": "Fish",
|
|
|
+ "tuna": "Fish",
|
|
|
+ "thon": "Fish",
|
|
|
+ "shrimp": "Shellfish",
|
|
|
+ "crevette": "Shellfish",
|
|
|
+ "crab": "Shellfish",
|
|
|
+ "crabe": "Shellfish",
|
|
|
+ "lobster": "Shellfish",
|
|
|
+ "homard": "Shellfish",
|
|
|
+ "mussel": "Shellfish",
|
|
|
+ "moule": "Shellfish",
|
|
|
+ "oyster": "Shellfish",
|
|
|
+ "huître": "Shellfish",
|
|
|
+ "huitre": "Shellfish",
|
|
|
+ "sesame": "Sesame",
|
|
|
+ "sésame": "Sesame",
|
|
|
+ "mustard": "Mustard",
|
|
|
+ "moutarde": "Mustard",
|
|
|
+ "celery": "Celery",
|
|
|
+ "céleri": "Celery",
|
|
|
+ "celeri": "Celery",
|
|
|
+ "lupin": "Lupin",
|
|
|
+ "mollusc": "Molluscs",
|
|
|
+ "mollusque": "Molluscs",
|
|
|
+ "sulphite": "Sulphites",
|
|
|
+ "sulfite": "Sulphites"
|
|
|
+ }
|
|
|
+ for keyword, allergen in mappings.items():
|
|
|
+ pattern = r'\b' + re.escape(keyword) + r's?\b'
|
|
|
+ if re.search(pattern, text):
|
|
|
+ detected.add(allergen)
|
|
|
+ return detected
|
|
|
+
|
|
|
+def filter_scratchpad_stream(stream, raw_accumulator=None):
|
|
|
+ buffer = ""
|
|
|
+ in_scratchpad = False
|
|
|
+ for chunk in stream:
|
|
|
+ content = chunk['message']['content']
|
|
|
+ if raw_accumulator is not None:
|
|
|
+ raw_accumulator.append(content)
|
|
|
+ buffer += content
|
|
|
+
|
|
|
+ while True:
|
|
|
+ if not in_scratchpad:
|
|
|
+ start_idx = buffer.find("<scratchpad>")
|
|
|
+ if start_idx != -1:
|
|
|
+ if start_idx > 0:
|
|
|
+ yield buffer[:start_idx]
|
|
|
+ yield "\n\n> 💭 **AI Thinking Process:**\n> "
|
|
|
+ buffer = buffer[start_idx + 12:]
|
|
|
+ in_scratchpad = True
|
|
|
+ else:
|
|
|
+ yield_len = len(buffer) - 11
|
|
|
+ if yield_len > 0:
|
|
|
+ yield buffer[:yield_len]
|
|
|
+ buffer = buffer[yield_len:]
|
|
|
+ break
|
|
|
+ else:
|
|
|
+ end_idx = buffer.find("</scratchpad>")
|
|
|
+ if end_idx != -1:
|
|
|
+ scratch_content = buffer[:end_idx]
|
|
|
+ scratch_content_formatted = scratch_content.replace("\n", "\n> ")
|
|
|
+ yield scratch_content_formatted
|
|
|
+ yield "\n\n"
|
|
|
+ buffer = buffer[end_idx + 13:]
|
|
|
+ in_scratchpad = False
|
|
|
+ else:
|
|
|
+ yield_len = len(buffer) - 12
|
|
|
+ if yield_len > 0:
|
|
|
+ scratch_content = buffer[:yield_len]
|
|
|
+ scratch_content_formatted = scratch_content.replace("\n", "\n> ")
|
|
|
+ yield scratch_content_formatted
|
|
|
+ buffer = buffer[yield_len:]
|
|
|
+ break
|
|
|
+ if buffer:
|
|
|
+ if in_scratchpad:
|
|
|
+ yield buffer.replace("\n", "\n> ")
|
|
|
+ else:
|
|
|
+ yield buffer
|
|
|
+
|
|
|
+def pull_model_bg():
|
|
|
+ try: ollama.pull(ACTIVE_MODEL)
|
|
|
+ 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}
|
|
|
+ """
|
|
|
+ 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:
|
|
|
+ pro = float(r['proteins_100g'] or 0)
|
|
|
+ fat = float(r['fat_100g'] or 0)
|
|
|
+ carb = float(r['carbohydrates_100g'] or 0)
|
|
|
+ sug = float(r['sugars_100g'] or 0)
|
|
|
+ snippets.append(f"- {r['product_name']}: Protein {pro:.2f}g, Fat {fat:.2f}g, Carbs {carb:.2f}g, Sugars {sug:.2f}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
|
|
|
+
|
|
|
+from contextlib import contextmanager
|
|
|
+
|
|
|
+@contextmanager
|
|
|
+def db_cursor(login_path: str):
|
|
|
+ conn = get_db_connection(login_path)
|
|
|
+ if not conn:
|
|
|
+ yield None
|
|
|
+ return
|
|
|
+ try:
|
|
|
+ with conn.cursor() as cursor:
|
|
|
+ yield cursor
|
|
|
+ conn.commit()
|
|
|
+ except Exception as e:
|
|
|
+ conn.rollback()
|
|
|
+ st.error(f"Database query error: {e}")
|
|
|
+ raise e
|
|
|
+ finally:
|
|
|
+ conn.close()
|
|
|
+
|
|
|
+def verify_login(username: str, password: str) -> bool:
|
|
|
+ with db_cursor('app_auth') as cursor:
|
|
|
+ if not cursor: return False
|
|
|
+ cursor.execute("SELECT password_hash FROM users WHERE username = %s", (username,))
|
|
|
+ result = cursor.fetchone()
|
|
|
+ if result: return bcrypt.checkpw(password.encode('utf-8'), result['password_hash'].encode('utf-8'))
|
|
|
+ return False
|
|
|
+
|
|
|
+def get_user_id(username: str) -> Optional[int]:
|
|
|
+ with db_cursor('app_auth') as cursor:
|
|
|
+ if not cursor: return None
|
|
|
+ cursor.execute("SELECT id FROM users WHERE username = %s", (username,))
|
|
|
+ result = cursor.fetchone()
|
|
|
+ return result['id'] if result else None
|
|
|
+
|
|
|
+def get_eav_profile(username: str) -> List[Dict[str, Any]]:
|
|
|
+ uid = get_user_id(username)
|
|
|
+ if not uid: return []
|
|
|
+ with db_cursor('app_auth') as cursor:
|
|
|
+ if not cursor: return []
|
|
|
+ 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,))
|
|
|
+ return cursor.fetchall()
|
|
|
+
|
|
|
+def get_user_limit(username: str) -> str:
|
|
|
+ with db_cursor('app_auth') as cursor:
|
|
|
+ if not cursor: return "50"
|
|
|
+ cursor.execute("SELECT search_limit FROM users WHERE username = %s", (username,))
|
|
|
+ result = cursor.fetchone()
|
|
|
+ return result['search_limit'] if (result and result['search_limit']) else "50"
|
|
|
+
|
|
|
+def register_user(username: str, password: str, email: str) -> bool:
|
|
|
+ hashed = bcrypt.hashpw(password.encode('utf-8'), bcrypt.gensalt()).decode('utf-8')
|
|
|
+ try:
|
|
|
+ with db_cursor('app_auth') as cursor:
|
|
|
+ if not cursor: return False
|
|
|
+ cursor.execute("INSERT INTO users (username, password_hash, email) VALUES (%s, %s, %s)", (username, hashed, email))
|
|
|
+ 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: str, subject: str, body: str, to_name: str = "User") -> Any:
|
|
|
+ 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: str, email: str) -> Any:
|
|
|
+ with db_cursor('app_auth') as cursor:
|
|
|
+ if not cursor: return False
|
|
|
+ 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']))
|
|
|
+ status = send_email(email, "Password Reset", f"Your new temporary password is: {new_pass}", to_name=username.title())
|
|
|
+ return True if status is True else status
|
|
|
+ return False
|
|
|
+
|
|
|
+# UI Theming
|
|
|
+def reformat_git_date(date_str):
|
|
|
+ from datetime import datetime
|
|
|
+ try:
|
|
|
+ import email.utils
|
|
|
+ dt = email.utils.parsedate_to_datetime(date_str)
|
|
|
+ return dt.strftime("%Y/%m/%d %H:%M:%S")
|
|
|
+ except Exception:
|
|
|
+ try:
|
|
|
+ dt = datetime.strptime(date_str.strip(), "%a %b %d %H:%M:%S %Y %z")
|
|
|
+ return dt.strftime("%Y/%m/%d %H:%M:%S")
|
|
|
+ except Exception:
|
|
|
+ return date_str
|
|
|
+
|
|
|
+def render_version():
|
|
|
+ st.markdown("---")
|
|
|
+ try:
|
|
|
+ if os.path.exists('git_version.txt'):
|
|
|
+ with open('git_version.txt', 'r') as f: git_version = f.read().strip()
|
|
|
+ else:
|
|
|
+ git_version = subprocess.check_output(['git', 'describe', '--tags']).decode('utf-8').strip()
|
|
|
+ except Exception:
|
|
|
+ git_version = "v1.3.0"
|
|
|
+
|
|
|
+ formatted_version = reformat_git_date(git_version)
|
|
|
+ st.caption(f"🚀 Version: {formatted_version}")
|
|
|
+
|
|
|
+ try:
|
|
|
+ if os.path.exists('git_id.txt'):
|
|
|
+ with open('git_id.txt', 'r') as f: git_id = f.read().strip()
|
|
|
+ else:
|
|
|
+ git_id = subprocess.check_output(['git', 'log', '-1', '--format=%cd %h', 'app.py']).decode('utf-8').strip()
|
|
|
+ except Exception:
|
|
|
+ git_id = "Unknown"
|
|
|
+
|
|
|
+ parts = git_id.strip().split()
|
|
|
+ if len(parts) >= 6:
|
|
|
+ date_part = " ".join(parts[:6])
|
|
|
+ hash_part = parts[6] if len(parts) > 6 else ""
|
|
|
+ formatted_date = reformat_git_date(date_part)
|
|
|
+ formatted_id = f"{formatted_date} {hash_part}".strip()
|
|
|
+ else:
|
|
|
+ formatted_id = git_id
|
|
|
+
|
|
|
+ st.caption(f"📅 Git ID: {formatted_id}")
|
|
|
+
|
|
|
+st.set_page_config(page_title="Food AI Explorer", page_icon="🍔", layout="wide")
|
|
|
+
|
|
|
+cookie_manager = stx.CookieManager(key="cookie_manager")
|
|
|
+
|
|
|
+# Wait for cookies to load
|
|
|
+cookies = cookie_manager.get_all()
|
|
|
+if cookies is None:
|
|
|
+ st.stop()
|
|
|
+
|
|
|
+# If the cookie has auth_user, set/restore session state
|
|
|
+cookie_user = cookie_manager.get(cookie="auth_user")
|
|
|
+if cookie_user:
|
|
|
+ st.session_state["authenticated_user"] = cookie_user
|
|
|
+elif "authenticated_user" not in st.session_state:
|
|
|
+ st.session_state["authenticated_user"] = None
|
|
|
+
|
|
|
+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; caret-color: #f8fafc !important; }
|
|
|
+</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("ℹ️ Welcome"):
|
|
|
+ st.info("Welcome to the secure Local Food AI environment.")
|
|
|
+
|
|
|
+ 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
|
|
|
+ cookie_manager.delete("auth_user")
|
|
|
+ time.sleep(0.5)
|
|
|
+ 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"])
|
|
|
+
|
|
|
+ if new_cat == "Condition":
|
|
|
+ new_val = st.selectbox("Value", ["Pregnant", "Breastfeeding", "Low Fat"])
|
|
|
+ elif new_cat == "Illness":
|
|
|
+ new_val = st.selectbox("Value", ["Diabetes", "Hypertension", "Kidney Disease", "Osteoporosis", "Scurvy", "Anemia"])
|
|
|
+ elif new_cat == "Diet":
|
|
|
+ new_val = st.selectbox("Value", ["Vegan", "Vegetarian", "Kosher", "Halal", "Christian", "Good Friday", "Ash Wednesday", "Keto", "Paleo"])
|
|
|
+ else:
|
|
|
+ new_val = st.text_input("Value (e.g. 'peanuts', 'broccoli')").strip()
|
|
|
+
|
|
|
+ new_val_clean = new_val.lower()
|
|
|
+
|
|
|
+ if st.button("Add to Profile") and new_val_clean 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.lower(), new_val_clean))
|
|
|
+ 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
|
|
|
+ import datetime
|
|
|
+ # Set cookie with 30 days expiration
|
|
|
+ cookie_manager.set(
|
|
|
+ "auth_user",
|
|
|
+ l_user,
|
|
|
+ expires_at=datetime.datetime.now() + datetime.timedelta(days=30)
|
|
|
+ )
|
|
|
+ time.sleep(0.2)
|
|
|
+ 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"
|
|
|
+
|
|
|
+ db_context = search_nutrition_db(prompt, user_eav)
|
|
|
+ searxng_context = ""
|
|
|
+
|
|
|
+ if "No database records found" in db_context:
|
|
|
+ try:
|
|
|
+ searxng_url = os.environ.get("SEARXNG_HOST", "http://searxng:8080")
|
|
|
+ resp = requests.get(f"{searxng_url}/search", params={'q': prompt, 'format': 'json'}, timeout=5)
|
|
|
+ if resp.status_code == 200:
|
|
|
+ results = resp.json().get('results', [])
|
|
|
+ if results:
|
|
|
+ snippets = [r.get('content', '') for r in results[:3]]
|
|
|
+ searxng_context = "Web Search Context: " + " | ".join(snippets)
|
|
|
+ except Exception as e:
|
|
|
+ pass
|
|
|
+
|
|
|
+ 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. Use Chain of Thought reasoning, and skip pleasantries.
|
|
|
+ Local Database Context: {db_context}
|
|
|
+ {searxng_context}
|
|
|
+ """
|
|
|
+
|
|
|
+ try:
|
|
|
+ temp_messages = [{"role": "system", "content": sys_prompt}] + [m for m in st.session_state.messages if m["role"] != "tool"]
|
|
|
+ start_llm = time.time()
|
|
|
+ response_stream = ollama.chat(model=ACTIVE_MODEL, messages=temp_messages, stream=True)
|
|
|
+
|
|
|
+ with st.chat_message("assistant"):
|
|
|
+ ai_reply = st.write_stream(chunk['message']['content'] for chunk in response_stream)
|
|
|
+ st.caption(f"⏱️ AI response generated in {time.time() - start_llm:.2f} seconds")
|
|
|
+
|
|
|
+ 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)*
|
|
|
+ """)
|
|
|
+ with st.form("explore_search_form"):
|
|
|
+ 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)
|
|
|
+
|
|
|
+ submit_search = st.form_submit_button("Search Database")
|
|
|
+ if submit_search:
|
|
|
+ st.session_state["trigger_search"] = True
|
|
|
+
|
|
|
+ if st.session_state.get("trigger_search", False) 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) OR product_name LIKE %s)
|
|
|
+ AND product_name IS NOT NULL AND product_name != '' AND product_name != 'None'
|
|
|
+ ORDER BY MATCH(product_name) AGAINST(%s IN BOOLEAN MODE) DESC, MATCH(ingredients_text) AGAINST(%s IN BOOLEAN MODE) DESC
|
|
|
+ {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()])
|
|
|
+ sq_like = f"%{sq}%"
|
|
|
+ start_time = time.time()
|
|
|
+ cursor.execute(query, (sq_bool, sq_like, sq_bool, 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)
|
|
|
+ df.replace(r'^\s*$', None, regex=True, inplace=True)
|
|
|
+ for col in df.columns:
|
|
|
+ if col.endswith('_100g'):
|
|
|
+ df[col] = pd.to_numeric(df[col], errors='coerce')
|
|
|
+
|
|
|
+ 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)
|
|
|
+ # Only fillna with empty string on object columns to avoid Arrow float64 conversion errors
|
|
|
+ for col in df_display.columns:
|
|
|
+ if df_display[col].dtype == 'object':
|
|
|
+ df_display[col] = df_display[col].fillna("")
|
|
|
+ df_display.index = range(1, len(df_display) + 1)
|
|
|
+ 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, hide_index=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"
|
|
|
+ minimal_records = df_display[['product_name', 'Medical Warning']].head(10).to_dict('records')
|
|
|
+ eval_prompt = f"The user has this profile: {profile_text}. Evaluate these top foods and state which are highly recommended or strictly forbidden: {minimal_records}. Provide a direct, readable clinical summary. Do not output raw JSON."
|
|
|
+ try:
|
|
|
+ response = ollama.chat(model=ACTIVE_MODEL, messages=[{'role': 'user', 'content': eval_prompt}], stream=True)
|
|
|
+ st.write_stream(chunk['message']['content'] for chunk in response)
|
|
|
+ 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. `add White Rice use 150g then add Chicken Breast use 50g add Green Beans use 100g`
|
|
|
+ 2. `add Potatoes use 200g then add Tomatoes use 100g add Beef use 100g`
|
|
|
+ 3. `add Spinach Salad use 100g then add Feta Cheese use 50g`
|
|
|
+ 4. `add Lentils use 200g then add Quinoa use 100g`
|
|
|
+ 5. `add Apple use 100g then add Almonds use 30g`
|
|
|
+ """)
|
|
|
+ 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()
|
|
|
+
|
|
|
+ st.markdown("#### ➕ Create a New Plate")
|
|
|
+ col_p1, col_p2 = st.columns([3, 1])
|
|
|
+ new_plate_name = col_p1.text_input("Plate Name (e.g., 'Spaghetti Bolognese')", key="new_plate")
|
|
|
+ if col_p2.button("Create Plate", use_container_width=True) and new_plate_name:
|
|
|
+ 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()
|
|
|
+
|
|
|
+ st.markdown("---")
|
|
|
+
|
|
|
+ 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 to Edit Ingredients", 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(p.ingredients_text) as ingredients_text,
|
|
|
+ MAX(m.proteins_100g) as proteins_100g, MAX(m.fat_100g) as fat_100g, MAX(m.carbohydrates_100g) as carbohydrates_100g,
|
|
|
+ MAX(m.sodium_100g) as sodium_100g, MAX(m.sugars_100g) as sugars_100g, MAX(m.fiber_100g) as fiber_100g,
|
|
|
+ MAX(v.`vitamin-a_100g`) as vitamin_a_100g, MAX(v.`vitamin-b1_100g`) as vitamin_b1_100g,
|
|
|
+ MAX(v.`vitamin-b2_100g`) as vitamin_b2_100g, MAX(v.`vitamin-pp_100g`) as vitamin_pp_100g,
|
|
|
+ MAX(v.`vitamin-b6_100g`) as vitamin_b6_100g, MAX(v.`vitamin-b9_100g`) as vitamin_b9_100g,
|
|
|
+ MAX(v.`vitamin-b12_100g`) as vitamin_b12_100g, MAX(v.`vitamin-c_100g`) as vitamin_c_100g,
|
|
|
+ MAX(v.`vitamin-d_100g`) as vitamin_d_100g, MAX(v.`vitamin-e_100g`) as vitamin_e_100g,
|
|
|
+ MAX(v.`vitamin-k_100g`) as vitamin_k_100g,
|
|
|
+ MAX(min.calcium_100g) as calcium_100g, MAX(min.iron_100g) as iron_100g,
|
|
|
+ MAX(min.magnesium_100g) as magnesium_100g, MAX(min.potassium_100g) as potassium_100g,
|
|
|
+ MAX(min.zinc_100g) as zinc_100g,
|
|
|
+ GROUP_CONCAT(DISTINCT a.allergens SEPARATOR ', ') as allergens
|
|
|
+ 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
|
|
|
+ LEFT JOIN products_vitamins v ON i.product_code = v.code
|
|
|
+ LEFT JOIN products_minerals min ON i.product_code = min.code
|
|
|
+ LEFT JOIN products_allergens a ON i.product_code = a.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])
|
|
|
+ pro = float(i['proteins_100g'] or 0) * (float(i['quantity_grams'])/100.0)
|
|
|
+ fat = float(i['fat_100g'] or 0) * (float(i['quantity_grams'])/100.0)
|
|
|
+ carb = float(i['carbohydrates_100g'] or 0) * (float(i['quantity_grams'])/100.0)
|
|
|
+ c1.markdown(f"<li><b>{i['quantity_grams']}g</b> of {i['product_name']} (Pro: {pro:.2f}g | Fat: {fat:.2f}g | Carb: {carb:.2f}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()
|
|
|
+
|
|
|
+ totals = {
|
|
|
+ "Total Protein (g)": sum((float(i['proteins_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Total Fat (g)": sum((float(i['fat_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Total Carbs (g)": sum((float(i['carbohydrates_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Sodium (g)": sum((float(i['sodium_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Sugars (g)": sum((float(i['sugars_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Fiber (g)": sum((float(i['fiber_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin A (g)": sum((float(i['vitamin_a_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin B1 (g)": sum((float(i['vitamin_b1_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin B2 (g)": sum((float(i['vitamin_b2_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin B3/PP (g)": sum((float(i['vitamin_pp_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin B6 (g)": sum((float(i['vitamin_b6_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin B9 (g)": sum((float(i['vitamin_b9_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin B12 (g)": sum((float(i['vitamin_b12_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin C (g)": sum((float(i['vitamin_c_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin D (g)": sum((float(i['vitamin_d_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin E (g)": sum((float(i['vitamin_e_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Vitamin K (g)": sum((float(i['vitamin_k_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Calcium (g)": sum((float(i['calcium_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Iron (g)": sum((float(i['iron_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Magnesium (g)": sum((float(i['magnesium_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Potassium (g)": sum((float(i['potassium_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ "Zinc (g)": sum((float(i['zinc_100g'] or 0) * (float(i['quantity_grams'])/100.0)) for i in items),
|
|
|
+ }
|
|
|
+
|
|
|
+ st.markdown("---")
|
|
|
+ st.markdown("### Plate Totals")
|
|
|
+ metrics = list(totals.items())
|
|
|
+ for idx in range(0, len(metrics), 3):
|
|
|
+ cols = st.columns(3)
|
|
|
+ for j in range(3):
|
|
|
+ if idx + j < len(metrics):
|
|
|
+ name, val = metrics[idx + j]
|
|
|
+ cols[j].metric(name, f"{val:.5f}" if val < 0.1 else f"{val:.2f}")
|
|
|
+
|
|
|
+ all_allergens = set()
|
|
|
+ for i in items:
|
|
|
+ # 1. Parse database allergens if available
|
|
|
+ if i.get('allergens'):
|
|
|
+ for alg in str(i['allergens']).split(','):
|
|
|
+ alg_clean = alg.strip().lower()
|
|
|
+ if alg_clean.startswith('en:'):
|
|
|
+ alg_clean = alg_clean[3:]
|
|
|
+ if alg_clean and alg_clean != 'none':
|
|
|
+ all_allergens.add(alg_clean.replace('-', ' ').title())
|
|
|
+
|
|
|
+ # 2. Text heuristics fallback for common allergens
|
|
|
+ prod_name = str(i.get('product_name') or '')
|
|
|
+ ing_text = str(i.get('ingredients_text') or '')
|
|
|
+ heuristics = detect_allergens_from_text(prod_name, ing_text)
|
|
|
+ all_allergens.update(heuristics)
|
|
|
+
|
|
|
+ st.markdown("---")
|
|
|
+ if all_allergens:
|
|
|
+ st.warning(f"⚠️ **Plate Allergens Detected:** {', '.join(all_allergens)}")
|
|
|
+ else:
|
|
|
+ st.success("✅ **No Allergens Detected**")
|
|
|
+
|
|
|
+ st.markdown("---")
|
|
|
+ st.markdown("#### ➕ Add Food to Plate")
|
|
|
+ with st.form("plate_add_form"):
|
|
|
+ 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"])
|
|
|
+
|
|
|
+ submit_add_search = st.form_submit_button("Search Food")
|
|
|
+
|
|
|
+ if add_search and submit_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'
|
|
|
+ ORDER BY LENGTH(product_name) ASC
|
|
|
+ ) 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}
|
|
|
+ """
|
|
|
+ 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")
|
|
|
+ st.session_state['plate_search_res'] = search_res
|
|
|
+
|
|
|
+ if st.session_state.get('plate_search_res'):
|
|
|
+ search_res = st.session_state['plate_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.session_state.pop('plate_search_res', None)
|
|
|
+ st.rerun()
|
|
|
+ else:
|
|
|
+ st.error("Could not parse unit. Please use format like '100g' or '1 cup'.")
|
|
|
+ elif add_search and submit_add_search:
|
|
|
+ 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", 1, 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)
|
|
|
+
|
|
|
+ meal_names = ["Breakfast", "Lunch", "Dinner", "Morning Snack", "Afternoon Snack", "Evening Snack"]
|
|
|
+ selected_meals = ", ".join(meal_names[:int(meal_count)])
|
|
|
+
|
|
|
+ sys_prompt = f"""You are a professional clinical Dietitian planner. Target: {target_cal}kcal.
|
|
|
+ You MUST generate EXACTLY {meal_count} meals and NO MORE. Failure to respect the meal count is a critical clinical error.
|
|
|
+ The allowed meal(s) are strictly: {selected_meals}.
|
|
|
+ Dietary constraint: {diet_pref}. Additional notes: {extra_notes}.
|
|
|
+ Health profile: {profile_text}.
|
|
|
+
|
|
|
+ COGNITIVE SCRATCHPAD INSTRUCTIONS:
|
|
|
+ - You MUST perform all your intermediate thinking, unit conversions (e.g. converting cups, tablespoons, or ounces to exact metric grams based on food density), and calorie/protein mathematical additions inside a `<scratchpad>` tag.
|
|
|
+ - Format:
|
|
|
+ <scratchpad>
|
|
|
+ Calculations:
|
|
|
+ - 1.5 cups of Cheese = X grams (density Y). Calories = A, Protein = B, Carbs = C, Fat = D.
|
|
|
+ - 2 tbsp of Peanut Butter = Z grams (density C). Calories = D, Protein = E, Carbs = F, Fat = G.
|
|
|
+ - Summation: Total Calories = A + D = Z kcal (vs target {target_cal}kcal). Total Protein = B + E = Fg.
|
|
|
+ </scratchpad>
|
|
|
+ | Meal Time | Exact Food | Portion Size | Calories | Protein | Carbs | Fat |
|
|
|
+ | --- | --- | --- | --- | --- | --- | --- |
|
|
|
+ ...
|
|
|
+ | Global Total | All Meals | | Total Calories | Total Protein | Total Carbs | Total Fat |
|
|
|
+
|
|
|
+ CRITICAL FORMATTING INSTRUCTIONS:
|
|
|
+ - After the </scratchpad> closing tag, you MUST strictly output the menu formatted as a Markdown Table.
|
|
|
+ - The table MUST contain exactly 7 columns separated by pipes (|): | Meal Time | Exact Food | Portion Size | Calories | Protein | Carbs | Fat |
|
|
|
+ - The Portion Size MUST be reported in exactly metric grams (e.g. 200g) and NEVER in cups or oz.
|
|
|
+ - The items in the table MUST be selected strictly from: {db_context}
|
|
|
+ - Do NOT output JSON. Do NOT use tool calls. Skip pleasantries.
|
|
|
+ """
|
|
|
+
|
|
|
+ st.info("🧠 AI is analyzing nutritional synergies and generating your plan...")
|
|
|
+
|
|
|
+ # Stream the response instantly!
|
|
|
+ try:
|
|
|
+ start_llm = time.time()
|
|
|
+ response = ollama.chat(model=ACTIVE_MODEL, messages=[
|
|
|
+ {'role': 'system', 'content': sys_prompt},
|
|
|
+ {'role': 'user', 'content': 'Generate my meal plan as a markdown table.'}
|
|
|
+ ], stream=True)
|
|
|
+ raw_chunks = []
|
|
|
+ clean_stream = filter_scratchpad_stream(response, raw_chunks)
|
|
|
+ ai_reply = st.write_stream(clean_stream)
|
|
|
+ raw_reply = "".join(raw_chunks)
|
|
|
+ st.caption(f"⏱️ AI Meal Plan generated in {time.time() - start_llm:.2f} seconds")
|
|
|
+
|
|
|
+ # PDF Generation
|
|
|
+ def generate_pdf(text):
|
|
|
+ import re
|
|
|
+ # Aggressive sanitization: if a table row has 4 columns and the last contains a comma or space before 'g', split it
|
|
|
+ sanitized_lines = []
|
|
|
+ for line in text.split('\\n'):
|
|
|
+ line = line.strip()
|
|
|
+ if line.startswith('|') and line.endswith('|') and '---' not in line:
|
|
|
+ cols = [c.strip() for c in line.strip('|').split('|')]
|
|
|
+ # If exactly 4 columns and the last one contains calories and protein merged
|
|
|
+ if len(cols) == 4 and any(char.isdigit() for char in cols[3]):
|
|
|
+ # Attempt to split by comma or 'kcal'
|
|
|
+ if ',' in cols[3]:
|
|
|
+ split_last = cols[3].split(',', 1)
|
|
|
+ cols = cols[:3] + [split_last[0].strip(), split_last[1].strip()]
|
|
|
+ elif 'kcal' in cols[3].lower():
|
|
|
+ split_last = re.split(r'(?<=kcal)\s+', cols[3], flags=re.IGNORECASE, maxsplit=1)
|
|
|
+ if len(split_last) == 2:
|
|
|
+ cols = cols[:3] + [split_last[0].strip(), split_last[1].strip()]
|
|
|
+ sanitized_lines.append('| ' + ' | '.join(cols) + ' |')
|
|
|
+ else:
|
|
|
+ sanitized_lines.append(line)
|
|
|
+ text = '\\n'.join(sanitized_lines)
|
|
|
+
|
|
|
+ pdf = FPDF()
|
|
|
+ pdf.add_page()
|
|
|
+ pdf.set_font("Helvetica", 'B', 16)
|
|
|
+ pdf.cell(0, 10, "Strict Clinical Meal Plan", new_x="LMARGIN", new_y="NEXT", align='C')
|
|
|
+ pdf.ln(h=5)
|
|
|
+ in_table = False
|
|
|
+ table_data = []
|
|
|
+
|
|
|
+ def flush_table():
|
|
|
+ if not table_data: return
|
|
|
+ pdf.set_font("Helvetica", size=9)
|
|
|
+ # Auto-calculate col_widths based on 5 columns if present
|
|
|
+ cw = (20, 40, 15, 10, 15) if len(table_data[0]) == 5 else (20, 30, 15, 10, 10, 10, 10) if len(table_data[0]) >= 7 else None
|
|
|
+ try:
|
|
|
+ with pdf.table(text_align="LEFT", col_widths=cw) as table:
|
|
|
+ for row_data in table_data:
|
|
|
+ row = table.row()
|
|
|
+ for datum in row_data:
|
|
|
+ row.cell(str(datum).encode('latin-1', 'replace').decode('latin-1'))
|
|
|
+ except Exception as e:
|
|
|
+ pdf.multi_cell(0, 8, "Table Render Error: " + str(e))
|
|
|
+ table_data.clear()
|
|
|
+ pdf.ln(h=5)
|
|
|
+
|
|
|
+ for line in text.split('\n'):
|
|
|
+ line = line.strip()
|
|
|
+ if not line:
|
|
|
+ flush_table()
|
|
|
+ pdf.ln(h=2)
|
|
|
+ continue
|
|
|
+
|
|
|
+ if line.startswith('|') or ('|' in line and 'Total' in line):
|
|
|
+ if not line.endswith('|'): line += ' |'
|
|
|
+ if not line.startswith('|'): line = '| ' + line
|
|
|
+
|
|
|
+ if '---' in line: continue
|
|
|
+ cols = [col.strip() for col in line.strip('|').split('|')]
|
|
|
+
|
|
|
+ # Normalize column length to prevent FPDF table crashing
|
|
|
+ if table_data:
|
|
|
+ target_len = len(table_data[0])
|
|
|
+ while len(cols) < target_len: cols.append("")
|
|
|
+ cols = cols[:target_len]
|
|
|
+
|
|
|
+ table_data.append(cols)
|
|
|
+ else:
|
|
|
+ flush_table()
|
|
|
+ pdf.set_font("Helvetica", size=11)
|
|
|
+ clean_line = str(line).encode('latin-1', 'replace').decode('latin-1')
|
|
|
+ pdf.multi_cell(0, 8, clean_line)
|
|
|
+
|
|
|
+ flush_table()
|
|
|
+
|
|
|
+ pdf_path = "/tmp/meal_plan.pdf"
|
|
|
+ pdf.output(pdf_path)
|
|
|
+ with open(pdf_path, "rb") as f:
|
|
|
+ return f.read()
|
|
|
+
|
|
|
+ st.download_button(
|
|
|
+ label="📄 Download PDF Export",
|
|
|
+ data=generate_pdf(strip_scratchpad(raw_reply)),
|
|
|
+ file_name="Clinical_Meal_Plan.pdf",
|
|
|
+ mime="application/pdf",
|
|
|
+ type="primary"
|
|
|
+ )
|
|
|
+
|
|
|
+ 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()
|
