Spaces:
Sleeping
Sleeping
File size: 3,153 Bytes
42bcde6 727ab43 19032c9 42bcde6 663f4dc 42bcde6 c0d03d4 42bcde6 c0d03d4 255d57d c0d03d4 42bcde6 b206342 663f4dc 42bcde6 663f4dc 42bcde6 b206342 42bcde6 727ab43 0cb983a b206342 42bcde6 663f4dc 42bcde6 b206342 42bcde6 83ebe75 663f4dc 42bcde6 663f4dc 42bcde6 663f4dc 42bcde6 663f4dc 727ab43 b46a94b 42bcde6 663f4dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import streamlit as st
import pymongo
from pymongo.errors import PyMongoError
import pandas as pd
import pickle
import os
#### Handling the db
@st.cache_resource()
def get_database_client():
# try:
# client = pymongo.MongoClient(st.secrets["mongodb_string"])
# except PyMongoError as e:
# st.error(
# "We are sorry, we are unable to connect to our database. Please try again later."
# )
# st.stop()
# return client
mongo_uri = os.environ.get("mongodb_uri") or st.secrets.get("mongodb_string")
client = pymongo.MongoClient(mongo_uri, uuidRepresentation='standard')
return client
mongo_client = get_database_client()
# st.toast("Database connected!")
#### Loading the Mappatura database
@st.cache_resource()
def load_database(_mongo_client):
try:
col = _mongo_client["poverta_educativa"]["database_mappatura_anni"]
mappatura_tot = pd.DataFrame(list(col.find()))
mappatura_tot["_id"] = mappatura_tot["_id"].astype(str)
mappatura_data = mappatura_tot
#mappatura_data = mappatura_tot.drop(
# [ "_id",
# "Livello di analisi",
# "Budget",
# "Budget completo di cofinanziamento (*se noto)",
# "Link 2 ",
# ],
# axis=1,
#)
#mappatura_data["Data inizio"] = mappatura_data["Data inizio"].astype(str)
#mappatura_data["Data fine (prevista o effettiva)"] = mappatura_data["Data fine (prevista o effettiva)"].astype(str)
except PyMongoError as e:
st.error(
"We are sorry, we are unable to connect to our database. Please try again later."
)
st.stop()
return mappatura_data
mappatura_data.columns = mappatura_data.columns.str.strip()
mappatura_data = load_database(mongo_client)
# st.toast("Database loaded!")
##### Loading the color data
@st.cache_resource()
def get_color_data():
try:
color_data_regioni = pd.read_json("./data/color_data_regioni.json")
color_data_province = pd.read_json("./data/color_data_province.json")
color_data_comuni = pd.read_json("./data/color_data_comuni.json")
except:
st.error(
"We are sorry, we are unable to load colormaps. Please try again later."
)
color_data_comuni = pd.DataFrame({"name": [], "color": []})
color_data_regioni = pd.DataFrame({"reg_name": [], "color": []})
color_data_province = pd.DataFrame({"prov_name": [], "color": []})
return color_data_regioni, color_data_province, color_data_comuni
presence_regioni = pickle.load(open("./data/presence_regioni_vector.pkl", "rb"))
presence_province = pickle.load(open("./data/presence_province_vector.pkl", "rb"))
presence_comuni = pickle.load(open("./data/presence_comuni_vector.pkl", "rb"))
all_regions = pickle.load(open("./data/all_regions.pkl", "rb"))
all_provinces = pickle.load(open("./data/all_provinces.pkl", "rb"))
all_munis = pickle.load(open("./data/all_munis.pkl", "rb"))
color_data_regioni, color_data_province, color_data_comuni = get_color_data()
# st.toast("Colormaps loaded.")
|