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dashboard-temp
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# Example taken from here - https://shinylive.io/py/examples/#map

import os
from shiny import reactive
from shiny.express import input, render, ui
from shinywidgets import render_widget
import ipyleaflet as ipyl

import pandas as pd
import geopandas as gpd
from ipyleaflet import Map, Rectangle, basemaps, basemap_to_tiles, LayersControl, ColorMap, FeatureGroup
from branca.colormap import linear

from shapely.geometry import box

def point_to_square(row, size=0.0009):
    x, y = row.geometry.x, row.geometry.y
    half = size / 2
    return box(x - half, y - half, x + half, y + half)



df = pd.read_csv("example.csv")

# Step 2: Create GeoDataFrame with UTM Zone 15N
gdf = gpd.GeoDataFrame(
    df,
    geometry=gpd.points_from_xy(df['x'], df['y']),
    crs="EPSG:32615"  # <- Your current system
)



# Step 3: Reproject to WGS84 (lat/lon)
gdf = gdf.to_crs("EPSG:4326")

# Convert points to small square polygons
gdf["geometry_box"] = gdf.apply(point_to_square, axis=1)

print(gdf.head())

# Step 4: Extract lat/lon columns for ipyleaflet
gdf["lat"] = gdf.geometry.y
gdf["lon"] = gdf.geometry.x

temp_colormap = linear.YlOrRd_09.scale(df['LST'].min(), df['LST'].max())

# def generate_temp_layer():
#     rectangles = []
#     for _, row in gdf.iterrows():
#         lat = row['lat']
#         lon = row['lon']
#         size_deg = 0.0009  # Approx ~100m (latitude degrees)

#         bounds = [
#             (lat - size_deg / 2, lon - size_deg / 2),
#             (lat + size_deg / 2, lon + size_deg / 2)
#         ]

#         color = temp_colormap(row['LST'])

#         rect = Rectangle(
#             bounds=bounds,
#             color=color,
#             fill_color=color,
#             fill_opacity=0.5,
#             weight=0
#         )
#         rectangles.append(rect)
#     return rectangles

city_centers = {
    "London": (51.5074, 0.1278),
    "Paris": (48.8566, 2.3522),
    "New York": (40.7128, -74.0060),
    "Guatemala city": (14.6349149, -90.5068824),
    "Zone 15": (14.61925, -90.49386)
}

ui.input_text("Text", "Some vector", "Zone 15")




@render_widget
def map():
    city = use_in_model(input.Text())
    center = city_centers.get(city, (0, 0))
    m = Map(center=center, zoom=13, basemap=basemaps.OpenStreetMap.Mapnik)
        # Add rectangle overlay

    geo_data = ipyl.GeoData(
        geo_dataframe=gdf,
            style={
                "color": "black",
                "fillColor": "#3366cc",
                "opacity": 0.05,
                "weight": 1.9,
                "dashArray": "2",
                "fillOpacity": 0.6,
            },
            hover_style={"fillColor": "red", "fillOpacity": 0.2},
            name="Temperature",
        )
    m.add(geo_data)
    m.add(ipyl.LayersControl())

    return m

# @render_widget
# def map():
#     city = use_in_model(input.Text())
#     center = city_centers.get(city, (0, 0))

#     # m = Map(center=center, zoom=13, basemap=basemaps.OpenStreetMap.Mapnik)
#     m = Map(center=city_centers["Guatemala city"], zoom=13, basemap=basemaps.OpenStreetMap.Mapnik)

#     print(f"Centering on city: {city}")

#     # Add rectangle overlay
#     for rect in generate_temp_layer():
#         m.add_layer(rect)

#     # Optional: Add color legend
#     # m.add_control(temp_colormap.legend(title= "Temp"))

#     # Add controls
#     m.add_control(LayersControl(position="topright"))

#     return m


def use_in_model(x):
    # TODO: Call some model loaded in huggingface_hub. 
    # Define the client once then just call the
    # client from here.

    # from huggingface_hub import InferenceClient 
    # client = InferenceClient(
    #    api_key=os.environ["HF_TOKEN"],
    #    provider="auto",
    # )
    #
    # completion = client.chat.completions.create(
    #    model="deepseek-ai/DeepSeek-V3-0324",
    #    messages=[{"role": "user", "content": "A story about hiking in the mountains"}]
    # )

    return x


@render.text
def text():
    return ("You entered: " + use_in_model(input.Text()))


@reactive.effect
def _():
    try:
        map.widget.center = city_centers[use_in_model(input.Text())]
    except:
        pass