app.py

import streamlit as st
import json
import matplotlib.pyplot as plt
import time
from PIL import Image
from streamlit_image_comparison import image_comparison

st.set_page_config(layout="wide")

# HIN Number +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
from SPARQLWrapper import SPARQLWrapper, JSON
from streamlit_agraph import agraph, TripleStore, Node, Edge, Config
import json

# Function to load JSON data
def load_data(filename):
    with open(filename, 'r') as file:
        data = json.load(file)
    return data

# Dictionary for color codes
color_codes = {
    "residential": "#ADD8E6",
    "commercial": "#90EE90",
    "community_facilities": "#FFFF00",
    "school": "#FFFF00",
    "healthcare_facility": "#FFFF00",
    "green_space": "#90EE90",
    "utility_infrastructure": "#90EE90",
    "emergency_services": "#FF0000",
    "cultural_facilities": "#D8BFD8",
    "recreational_facilities": "#D8BFD8",
    "innovation_center": "#90EE90",
    "elderly_care_home": "#FFFF00",
    "childcare_centers": "#FFFF00",
    "places_of_worship": "#D8BFD8",
    "event_spaces": "#D8BFD8",
    "guest_housing": "#FFA500",
    "pet_care_facilities": "#FFA500",
    "public_sanitation_facilities": "#A0A0A0",
    "environmental_monitoring_stations": "#90EE90",
    "disaster_preparedness_center": "#A0A0A0",
    "outdoor_community_spaces": "#90EE90",
    # Add other types with their corresponding colors
}

# Function to draw the grid with optional highlighting

def draw_grid(data, highlight_coords=None):
    fig, ax = plt.subplots(figsize=(12, 12))
    nrows, ncols = data['size']['rows'], data['size']['columns']
    ax.set_xlim(0, ncols)
    ax.set_ylim(0, nrows)
    ax.set_xticks(range(ncols+1))
    ax.set_yticks(range(nrows+1))
    ax.grid(True)

    # Draw roads with a specified grey color
    road_color = "#606060"  # Light grey; change to "#505050" for dark grey
    for road in data.get('roads', []):  # Check for roads in the data
        start, end = road['start'], road['end']
        # Determine if the road is vertical or horizontal based on start and end coordinates
        if start[0] == end[0]:  # Vertical road
            for y in range(min(start[1], end[1]), max(start[1], end[1]) + 1):
                ax.add_patch(plt.Rectangle((start[0], nrows-y-1), 1, 1, color=road['color']))
        else:  # Horizontal road
            for x in range(min(start[0], end[0]), max(start[0], end[0]) + 1):
                ax.add_patch(plt.Rectangle((x, nrows-start[1]-1), 1, 1, color=road['color']))

    # Draw buildings
    for building in data['buildings']:
        coords = building['coords']
        b_type = building['type']
        size = building['size']
        color = color_codes.get(b_type, '#FFFFFF')  # Default color is white if not specified
        
        if highlight_coords and (coords[0], coords[1]) == tuple(highlight_coords):
            highlighted_color = "#FFD700"  # Gold for highlighting
            ax.add_patch(plt.Rectangle((coords[1], nrows-coords[0]-size), size, size, color=highlighted_color, edgecolor='black', linewidth=2))
        else:
            ax.add_patch(plt.Rectangle((coords[1], nrows-coords[0]-size), size, size, color=color, edgecolor='black', linewidth=1))
            ax.text(coords[1]+0.5*size, nrows-coords[0]-0.5*size, b_type, ha='center', va='center', fontsize=8, color='black')

    ax.set_xlabel('Columns')
    ax.set_ylabel('Rows')
    ax.set_title('Village Layout with Color Coding')
    return fig

  
# Tabs +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

# Create the main app with three tabs
tab1, tab2, tab3 = st.tabs(["Interpretive Number","Green Village", "Control Room"])


with tab1:
    
    st.header("Engineering Tools")

    # Divide the page into three columns
    col1, col2 = st.columns(2)

    with col1:
        st.header("Human Interpretive Number (HIN)")
        st.write("")
        "How do LLMs interpret us and over “a period” of time what is the result of our interaction with them?"
        st.write("")
      
        image_comparison(
            img1="./data/robot.jpg",
            img2="./data/life.jpg",
            label1="What the LMM Sees (Simulated Life)",
            label2="What we See (Real Life)",
        )
    with col2:
        st.header("Custom GPT Engineering Tools")


with tab2:

    st.header("Green Smart Village Application")

    # Divide the page into three columns
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.header("Today's Agenda")
        st.write("1. Morning Meeting\n2. Review Project Plans\n3. Lunch Break\n4. Site Visit\n5. Evening Wrap-up")
        
        st.header("Agent Advisors")
        st.write("Would you like to optimize your HIN number?")
        
        st.header("My Incentive")
        st.write("Total incentive for HIN optimization")
    
    with col2:
        st.header("Green Smart Village Layout")
        data = load_data('grid.json')  # Ensure this path is correct
        
        # Dropdown for selecting a building
        building_options = [f"{bld['type']} at ({bld['coords'][0]}, {bld['coords'][1]})" for bld in data['buildings']]
        selected_building = st.selectbox("Select a building to highlight:", options=building_options)
        selected_index = building_options.index(selected_building)
        selected_building_coords = data['buildings'][selected_index]['coords']

        # Draw the grid with the selected building highlighted
        fig = draw_grid(data, highlight_coords=selected_building_coords)
        st.pyplot(fig)
        
        # Assuming sensors are defined in your data, display them
        sensors = data['buildings'][selected_index].get('sensors', [])
        st.write(f"Sensors in selected building: {', '.join(sensors)}")
        
    with col3:
        st.header("Check Your HIN Number")
        # config = Config(height=400, width=400, nodeHighlightBehavior=True, highlightColor="#F7A7A6", directed=True, collapsible=True)


        if sensors:  # Check if there are sensors to display
            graph_store = TripleStore()
            building_name = f"{data['buildings'][selected_index]['type']} ({selected_building_coords[0]}, {selected_building_coords[1]})"
            
            # Iterate through each sensor and create a triple linking it to the building
            for sensor in sensors:
                sensor_id = f"Sensor: {sensor}"  # Label for sensor nodes
                # Correctly add the triple without named arguments
                graph_store.add_triple(building_name, "has_sensor", sensor_id)
        
            # Configuration for the graph visualization
            agraph_config = Config(height=300, width=300, nodeHighlightBehavior=True, highlightColor="#F7A7A6", directed=True, collapsible=True)
        
            # Display the graph
            agraph(nodes=graph_store.getNodes(), edges=graph_store.getEdges(), config=agraph_config)

    

        hin_number = st.text_input("Enter your HIN number:")
        if hin_number:
            st.write("HIN number details...")  # Placeholder for actual HIN number check

with tab3:
   
    st.header("Control Room")