# Fix OMP_NUM_THREADS BEFORE any imports
import os
if os.environ.get('OMP_NUM_THREADS', '').endswith('m'):
os.environ['OMP_NUM_THREADS'] = '4'
# import libraries
import pandas as pd
import numpy as np
import time
import math
import streamlit as st
# from streamlit_chat import message
from streamlit_extras.colored_header import colored_header
from streamlit_extras.add_vertical_space import add_vertical_space
from PIL import Image
# import modules
from src.backend.chatbot import *
from src.backend.optimization_algo import *
from src.frontend.visualizations import *
# import compatibilities matrix
# make plant_compatibility.csv into a matrix. it currently has indexes as rows and columns for plant names and then compatibility values as the values
st.session_state.raw_plant_compatibility = pd.read_csv(
"src/data/plant_compatibility.csv", index_col=0
)
# fill NaN values with 0
st.session_state.raw_plant_compatibility = (
st.session_state.raw_plant_compatibility.fillna(0)
)
# get list of plants
st.session_state.plant_list = st.session_state.raw_plant_compatibility.index.tolist()
# set version
st.session_state.demo_lite = False
# set default model
st.session_state.model = "Llama3.2-1b_CPP"
# setup keys and api info
# OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
# os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
# chat = ChatOpenAI()
# UI page config
st.set_page_config(
page_title="GRDN.AI - Companion Gardening Optimizer",
page_icon="🌱",
layout="wide",
initial_sidebar_state="expanded",
)
# Function to display chat message with an icon
def chat_message(message, is_user=False):
if is_user:
icon = Image.open("src/assets/cool.png")
side = "left"
else:
icon = Image.open("src/assets/bot.png")
side = "right"
chat_container = st.container()
with chat_container:
col1, col2, col3, col4 = st.columns([1, 7, 7, 1])
with col1:
if is_user == True:
st.image(icon, width=50)
with col2:
if is_user == True:
st.markdown(
f'{message}
',
unsafe_allow_html=True,
)
with col3:
if is_user == False:
st.markdown(
f'{message}
',
unsafe_allow_html=True,
)
with col4:
if is_user == False:
st.image(icon, width=50)
st.image(
"src/assets/logo_title_transparent.png",
use_column_width=True,
)
st.write("AI and optimization powered companion gardening")
colored_header(label="", description="", color_name="green-30")
# Sidebar
# st.sidebar.title("Navigation")
# page = st.sidebar.radio("Select a page:", ("Home", "Companion Gardening", "Optimization", "About"))
# add vertical space
with st.sidebar:
add_vertical_space(2)
# Sidebar
st.sidebar.title("Navigation")
# Define the page options
pages = ["Garden Optimization", "About"]
# Render the selected page content
page = st.sidebar.selectbox("Select a page:", pages)
if page == "Garden Optimization":
st.sidebar.subheader("Companion Gardening")
st.write(
"GRDN is a companion gardening app that helps you plan your garden and maximize your harvest. It uses optimization and AI to predict the best plants to grow together and optimization algorithms to optimize how you build your garden."
)
st.write("This app is currently in beta. Please report any bugs.")
companion_planting_info = """
Key Benefits
- **Pest control:**
- **Improved pollination:**
- **Maximized space:**
- **Nutrient enhancement:**
- **Complementary growth:**
"""
st.sidebar.markdown(companion_planting_info)
# dropdown with advanced algorithm options- LLM agent base model and optimization algorithm type (constrained genetic algorithm or constrained community detection mixed integer programming model)
# with st.sidebar.expander("Advanced Options: LLM, optimization algorithm"):
# select LLM agent base model
st.sidebar.write("\n\n\n")
st.sidebar.write("\n\n\n")
# Display GPU status
env_config = detect_gpu_and_environment()
if env_config["gpu_available"]:
st.sidebar.markdown(
"""
🚀 GPU Acceleration: ENABLED
""",
unsafe_allow_html=True
)
if env_config["is_hf_space"]:
st.sidebar.info("Running on HuggingFace Spaces with Nvidia T4")
else:
st.sidebar.warning("⚠️ GPU Acceleration: DISABLED (CPU mode)")
st.sidebar.subheader("LLM agent base model")
# radio buttons for LLM used throughout the app
st.session_state.model = st.sidebar.radio(
"Select an open-source LLM :",
(
"Llama3.2-1b_CPP ⚡ ACTIVE",
"lite_demo (no LLM)",
),
)
st.sidebar.markdown("""
Legacy models (disabled):
Llama2-7b (too large)
Qwen2.5-7b (too large)
deci-7b (too large)
""", unsafe_allow_html=True)
# Strip the labels for internal use
if "⭐" in st.session_state.model or "⚡" in st.session_state.model or "(legacy)" in st.session_state.model:
st.session_state.model = st.session_state.model.split()[0]
# # radio buttons for optimization algorithm used throughout the app ('constrained_genetic_algorithm', 'constrained_community_detection_mip')
# st.session_state.optimization_algo = st.radio("Select an optimization algorithm :", (
# 'constrained_genetic_algorithm',
# 'constrained_community_detection_mip'))
# override model if lite demo is selected
if (
st.session_state.model == "lite_demo (no LLM)"
or st.session_state.model == "deci-7b_CPP"
):
st.session_state.demo_lite = True
# Set the initial value of user_name
if "user_name" not in st.session_state:
st.session_state.user_name = ""
# add in some vertical space
add_vertical_space(2)
# Add custom teal and purple (#6000FA) color scheme
st.markdown("""
""", unsafe_allow_html=True)
# Display the welcome message
st.title("Let's get started! Decide on your garden parameters")
# add in some vertical space
add_vertical_space(1)
# Determine current step for progress indicator
current_step = 1
if st.session_state.get("user_name", "") != "":
current_step = 2
if st.session_state.get("submitted_plant_list", False):
current_step = 3
if st.session_state.get("full_mat") is not None:
current_step = 4
# Display step progress
step_class = lambda n: "step-active" if n == current_step else ("step-completed" if n < current_step else "step-pending")
st.markdown(f"""
① Enter Name
② Select Plants
③ Generate Matrix
④ Optimize Garden
""", unsafe_allow_html=True)
add_vertical_space(1)
# make a container for this section
container1 = st.container()
with container1:
# Modify the user_name variable based on user input
if st.session_state["user_name"] == "":
st.markdown("### Step 1: Enter Your Name")
col1, col2, col3 = st.columns([1, 2, 1])
with col1:
st.session_state["user_name_input"] = st.text_input(
"Enter your name", st.session_state.user_name, key="name_input"
)
if "user_name_input" in st.session_state:
st.session_state.user_name = st.session_state.user_name_input
if st.session_state.user_name != "":
st.write(
"Hello "
+ st.session_state["user_name"]
+ "! Let's optimize your garden. "
)
# # add in some vertical space
add_vertical_space(2)
print("")
print("____________________")
print("start of session")
st.markdown("### Step 2: Select Your Plants")
add_vertical_space(1)
col1a, col2a = st.columns([1, 2])
enable_max_species = False
enable_min_species = False
# make a form to get the plant list from the user
with col1a:
with st.form(key="plant_list_form"):
input_plants_raw = st.multiselect(
"Select plants for your garden",
st.session_state.plant_list,
help="Choose the plants you want to grow"
)
# Add CSS class to highlight button
if not st.session_state.get("submitted_plant_list", False):
st.markdown('', unsafe_allow_html=True)
submit_button = st.form_submit_button(label="Submit Plant List")
if submit_button:
st.session_state["input_plants_raw"] = input_plants_raw
st.session_state.submitted_plant_list = True
# add in some vertical space
add_vertical_space(1)
with col2a:
col1, col2, col3 = st.columns([1, 1, 1])
if "input_plants_raw" in st.session_state:
print("BP1")
# first question is what plants would you like to plant
plants_response = st.session_state.input_plants_raw
# Initialize session state variables if they don't exist
if "n_plant_beds" not in st.session_state:
st.session_state["n_plant_beds"] = 1
if "min_species" not in st.session_state:
st.session_state["min_species"] = 1
if "max_species" not in st.session_state:
st.session_state["max_species"] = 2
# Number of plant beds input
with col1:
n_plant_beds = st.number_input(
"Number of plant beds \n",
min_value=1,
max_value=20,
value=st.session_state.n_plant_beds,
step=1,
)
st.session_state.n_plant_beds = n_plant_beds
with col2:
# Minimum species per plant bed input
min_species = st.number_input(
"Minimum number of species per plant bed",
min_value=1,
max_value=len(st.session_state.input_plants_raw),
value=st.session_state.min_species,
step=1,
)
st.session_state.min_species = min_species
# Maximum species per plant bed input
# It will be enabled only if min_species is set
enable_max_species = st.session_state.min_species > 0
with col3:
max_species = st.number_input(
"Maximum number of species per plant bed",
min_value=st.session_state.min_species,
max_value=len(st.session_state.input_plants_raw),
value=max(
st.session_state.min_species,
st.session_state.max_species,
),
step=1,
disabled=not enable_max_species,
)
if enable_max_species:
st.session_state.max_species = max_species
# extract the compatibility matrix from the user's input
if "extracted_mat" not in st.session_state:
valid = False
if (
"submitted_plant_list" in st.session_state
and st.session_state.submitted_plant_list
):
# check if the user's input is valid
# min species per bed must be less than or equal to max species per bed
if (
(
st.session_state.min_species
<= st.session_state.max_species
)
and (
# max species per bed must be less than or equal to the number of plants
st.session_state.max_species
<= len(st.session_state.input_plants_raw)
)
and (
# max species per bed must be greater than or equal to the min species per bed
st.session_state.max_species
>= st.session_state.min_species
)
and (
# min species per bed must be less than or equal to the number of plants
st.session_state.min_species
<= len(st.session_state.input_plants_raw)
)
and (
# number of plant beds multiplied by min species per bed must be less than or equal to the number of plants
len(st.session_state.input_plants_raw)
>= st.session_state.n_plant_beds
* st.session_state.min_species
)
and (
# number of plant beds multiplied by max species per bed must be greater than or equal to the number of plants
len(st.session_state.input_plants_raw)
<= st.session_state.n_plant_beds
* st.session_state.max_species
)
):
valid = True
else:
# add a warning message
st.warning(
"Please enter valid parameters. The minimum number of species per plant bed must be less than or equal to the maximum number of species per plant bed. The maximum number of species per plant bed must be less than or equal to the number of plants. The maximum number of species per plant bed must be greater than or equal to the minimum number of species per plant bed. The minimum number of species per plant bed must be less than or equal to the number of plants. The number of plant beds multiplied by the minimum number of species per plant bed must be less than or equal to the number of plants. The number of plant beds multiplied by the maximum number of species per plant bed must be greater than or equal to the number of plants."
)
if valid:
# add in some vertical space
add_vertical_space(1)
st.markdown("### Step 3: Generate Compatibility Matrix")
st.info("Click the button below to analyze plant compatibilities based on your selections")
# Highlight button if matrix not yet generated
if "full_mat" not in st.session_state:
st.markdown('', unsafe_allow_html=True)
if st.button(
"Generate Companion Plant Compatibility Matrix"
):
with st.spinner(
"generating companion plant compatibility matrix..."
):
st.session_state["generating_mat"] = True
# now get compatibility matrix for companion planting
time.sleep(1)
(
extracted_mat,
full_mat,
plant_index_mapping,
) = get_compatibility_matrix_2(
st.session_state.input_plants_raw
)
print(extracted_mat)
st.session_state.extracted_mat = extracted_mat
st.session_state.full_mat = full_mat
st.session_state.plant_index_mapping = (
plant_index_mapping
)
# add in some vertical space
add_vertical_space(4)
# display the companion plant compatibility matrix
if "extracted_mat" in st.session_state:
# add a title for the next section- companion plant compatibility matrix based on user input
st.title("Your companion plant compatibility matrix")
# make a container for this section
container2 = st.container()
with container2:
col1, col2 = st.columns([8, 4])
# display the companion plant compatibility matrix
with col2:
st.write("Here is your companion plant compatibility matrix:")
with st.expander("Show ugly compatibility matrix of 1's 0's and -1's"):
st.write(st.session_state.extracted_mat)
with col1:
st.write(
"Here is a network visualization of your companion plant compatibility matrix. It is color coded to show which plants are companions (green), antagonists (violetred), or neutral (grey)."
)
plot_compatibility_with_agraph(
st.session_state.input_plants_raw, st.session_state.full_mat, key_suffix="main"
)
st.session_state["got_mat"] = True
if "got_mat" in st.session_state:
# add in some vertical space
add_vertical_space(4)
# make a container for this section
container3 = st.container()
with container3:
st.title(
"Optimizing companion planting with the genetic algorithm and AI"
)
st.write(
"Now that we have your companion plant compatibility matrix, we can use optimization to maximize your harvest. We will use a genetic algorithm to determine the best way to plant your garden. The genetic algorithm will determine the best way to plant your garden by maximizing the number of companion plants and minimizing the number of antagonists."
)
st.write(
"Set the parameters for the genetic algorithm. Here is more info for your reference:"
)
with st.form(key="genetic_algorithm_form"):
col1, col2 = st.columns([1, 1])
with col2:
with st.expander(
"Show more information about the genetic algorithm parameters"
):
st.subheader("Plant Optimization Heuristic Performance")
st.write(
"The genetic algorithm parameters impact the performance of the plant optimization heuristic in the following ways:"
)
st.markdown(
"- **Population Size**: A larger population size allows for a more diverse exploration of the solution space. However, it also increases computational complexity."
)
st.markdown(
"- **Number of Generations**: Increasing the number of generations provides more opportunities for the algorithm to converge towards an optimal solution."
)
st.markdown(
"- **Tournament Size**: A larger tournament size promotes stronger selection pressure and can lead to faster convergence, but it may also increase the risk of premature convergence."
)
st.markdown(
"- **Crossover Rate**: A higher crossover rate increases the exploration capability by creating diverse offspring, potentially improving the algorithm's ability to escape local optima."
)
st.markdown(
"- **Mutation Rate**: Mutation introduces random changes in individuals, helping to maintain diversity in the population and preventing premature convergence."
)
# seed population rate
st.markdown(
"- **Seed Population Rate**: The seed population rate is the percentage of the population that is generated based on the LLM's interpretation of compatibility. The remaining percentage of the population is generated randomly. A higher seed population rate increases the likelihood that the genetic algorithm will converge towards a solution that is compatible."
)
# Run the Genetic Algorithm
st.markdown("### Step 4: Optimize Your Garden Layout")
st.markdown(
"""
✓ Matrix generated! Now configure and run the optimization algorithm
""",
unsafe_allow_html=True
)
add_vertical_space(1)
with col1:
st.subheader("Genetic Algorithm Parameters")
st.write(
"These parameters control the behavior of the genetic algorithm."
)
st.info("Quick start: The default values (150/150) run in ~20-30 seconds for optimal results. Decrease for faster results, increase for maximum quality.")
# Genetic Algorithm parameters - Optimized defaults for quality and speed
st.session_state.population_size = st.slider(
"Population Size",
min_value=30,
max_value=500,
value=150,
step=10,
help="The number of individuals in each generation. Recommended: 150-200 for optimal results.",
)
st.session_state.num_generations = st.slider(
"Number of Generations",
min_value=30,
max_value=500,
value=150,
step=10,
help="The total number of generations to evolve through. Recommended: 150-200 for optimal results.",
)
st.session_state.tournament_size = st.slider(
"Tournament Size",
min_value=5,
max_value=20,
value=10,
help="The number of individuals competing in each tournament selection round.",
)
st.session_state.crossover_rate = st.slider(
"Crossover Rate",
min_value=0.1,
max_value=1.0,
step=0.1,
value=0.8,
help="The probability of two individuals undergoing crossover to create offspring.",
)
st.session_state.mutation_rate = st.slider(
"Mutation Rate",
min_value=0.01,
max_value=0.9,
step=0.01,
value=0.3,
help="The probability of an individual undergoing mutation.",
)
st.session_state.seed_population_rate = st.slider(
"Seed Population Rate (WARNING: slow if > 0)",
min_value=0.0,
max_value=0.1,
step=0.01,
value=0.0,
help="The percentage of the population that is generated using the LLM (VERY SLOW - adds 30+ seconds). Set to 0 for fast results. Only increase if you want LLM-suggested initial groupings.",
)
#
# Highlight button if algorithm hasn't run yet
if "grouping" not in st.session_state:
st.markdown('', unsafe_allow_html=True)
# Run the genetic algorithm
if st.form_submit_button(label="Run Genetic Algorithm"):
# Calculate estimated time based on parameters
est_time = (st.session_state.population_size * st.session_state.num_generations) / 500
est_time_str = f"{est_time:.0f} seconds" if est_time < 60 else f"{est_time/60:.1f} minutes"
with st.spinner(
f"Running genetic algorithm... (estimated time: {est_time_str})"
):
grouping = genetic_algorithm_plants(
st.session_state.model, st.session_state.demo_lite
)
st.session_state.grouping = grouping
# visualize the groupings
# add in some vertical space
add_vertical_space(4)
# make a container for this section
st.title(st.session_state.user_name + "'s optimized garden")
st.header("Here are the optimized groupings of plants for your garden")
container4 = st.container()
with container4:
if "grouping" in st.session_state:
visualize_groupings()
if "best_fitness" in st.session_state:
# embed score.png
col1b, col2b = st.columns([2, 11])
with col1b:
st.image(
"src/assets/score.png",
width=160,
)
with col2b:
# st.write("\n")
st.header("| " + str(st.session_state.best_fitness))
st.write(
"The genetic algorithm converged towards a solution with a fitness score of "
+ str(st.session_state.best_fitness)
+ "."
)
# Add vertical space
add_vertical_space(4)
# show plant care tips
st.header("Plant Care Tips")
with st.spinner("Generating plant care tips..."):
if st.session_state.demo_lite:
st.session_state.plant_care_tips = "Plant care tips are not available in lite demo mode. Select Llama3.2-1b_CPP for full functionality."
else:
# if 'plant_care_tips' not in st.session_state:
st.session_state.plant_care_tips = get_plant_care_tips(
st.session_state.input_plants_raw,
st.session_state.model,
st.session_state.demo_lite,
)
# Use markdown container with teal background for better formatting
st.markdown(f"""
{st.session_state.plant_care_tips}
""", unsafe_allow_html=True)
if page == "About":
st.sidebar.subheader("About")
st.sidebar.write(
"GRDN is a companion gardening app that helps you plan your garden and maximize your harvest. It uses AI to predict the best plants to grow together and optimization algorithms to optimize how you build your garden."
)
st.sidebar.write(
"Companion gardening is the practice of planting different plants together to maximize their growth. Companion gardening can help to increase the yield of your garden, improve the health of your plants, and reduce the need for pesticides."
)
st.write("This app is currently in beta. Please report any bugs to the team.")
add_vertical_space(1)
st.subheader("Tech Stack Diagram")
st.image(
"src/assets/GRDN_AI_techstack_.png",
use_column_width=True,
)
add_vertical_space(4)
col1, col2 = st.columns([1, 1])
with col1:
st.subheader("Contact Information")
st.write("Author: Danielle Heymann")
st.write("Email: dheymann314@gmail.com")
st.write("LinkedIn: https://www.linkedin.com/in/danielle-heymann/")
with col2:
st.subheader("Software, data, and libraries used")
st.write("Libraries and Software")
st.markdown(
"""
- Python
- streamlit
- openai
- plotly
- pandas
- numpy
- PIL
- langchain
- streamlit_chat
- github copilot
- Llama2
- Deci AI
- HuggingFace
- LlamaIndex
- chatGPT
- GPT family of models
- DALL·E 3 (in preprocessing script for image generation)
"""
)
st.write(
"Data sources in addition to what LLMs were trained on: \n https://waldenlabs.com/the-ultimate-companion-planting-guide-chart/ "
)
# st.write("avatars from: https://www.flaticon.com/free-icons/bot")