similarity_page.py

"""
Page for similarities
"""

################
# DEPENDENCIES #
################
import streamlit as st
import pandas as pd
from scipy.sparse import load_npz
import pickle
import faiss
from sentence_transformers import SentenceTransformer
from modules.result_table import show_table
from functions.filter_projects import filter_projects
from functions.calc_matches import calc_matches
import psutil
import os
import gc

def get_process_memory():
    process = psutil.Process(os.getpid())
    return process.memory_info().rss / (1024 * 1024) 

# Catch DATA
# Load Similarity matrix
@st.cache_data
def load_sim_matrix():
    loaded_matrix = load_npz("src/extended_similarities.npz")
    dense_matrix = loaded_matrix.toarray()

    return dense_matrix

# Load Projects DFs
@st.cache_data
def load_projects():
    orgas_df = pd.read_csv("src/projects/project_orgas.csv")
    region_df = pd.read_csv("src/projects/project_region.csv")
    sector_df = pd.read_csv("src/projects/project_sector.csv")
    status_df = pd.read_csv("src/projects/project_status.csv")
    texts_df = pd.read_csv("src/projects/project_texts.csv")

    projects_df = pd.merge(orgas_df, region_df, on='iati_id', how='inner')
    projects_df = pd.merge(projects_df, sector_df, on='iati_id', how='inner')
    projects_df = pd.merge(projects_df, status_df, on='iati_id', how='inner')
    projects_df = pd.merge(projects_df, texts_df, on='iati_id', how='inner')

    return projects_df

# Load CRS 3 data
@st.cache_data
def getCRS3():
    # Read in CRS3 CODELISTS
    crs3_df = pd.read_csv('src/codelists/crs3_codes.csv')
    CRS3_CODES = crs3_df['code'].tolist()
    CRS3_NAME = crs3_df['name'].tolist()
    CRS3_MERGED = {f"{name} - {code}": code for name, code in zip(CRS3_NAME, CRS3_CODES)}

    return CRS3_MERGED

# Load CRS 5 data
@st.cache_data
def getCRS5():
    # Read in CRS3 CODELISTS
    crs5_df = pd.read_csv('src/codelists/crs5_codes.csv')
    CRS5_CODES = crs5_df['code'].tolist()
    CRS5_NAME = crs5_df['name'].tolist()
    CRS5_MERGED = {code: [f"{name} - {code}"] for name, code in zip(CRS5_NAME, CRS5_CODES)}

    return CRS5_MERGED

# Load SDG data
@st.cache_data
def getSDG():
    # Read in SDG CODELISTS
    sdg_df = pd.read_csv('src/codelists/sdg_goals.csv')
    SDG_NAMES = sdg_df['name'].tolist()

    return SDG_NAMES

# Load Country Data
@st.cache_data
def getCountry():
    # Read in countries from codelist
    country_df = pd.read_csv('src/codelists/country_codes_ISO3166-1alpha-2.csv')
    COUNTRY_CODES = country_df['Alpha-2 code'].tolist()
    COUNTRY_NAMES = country_df['Country'].tolist()

    COUNTRY_OPTION_LIST = [f"{COUNTRY_NAMES[i]} ({COUNTRY_CODES[i][-3:-1].upper()})"for i in range(len(COUNTRY_NAMES))]

    return COUNTRY_OPTION_LIST

# Load Sentence Transformer Model
@st.cache_resource
def load_model():
    model = SentenceTransformer('all-MiniLM-L6-v2')
    return model


# Load Embeddings
@st.cache_data 
def load_embeddings_and_index():
    # Load embeddings
    with open("src/embeddings.pkl", "rb") as fIn:
        stored_data = pickle.load(fIn)
    sentences = stored_data["sentences"]
    embeddings = stored_data["embeddings"]

    return sentences, embeddings

# USE CACHE FUNCTIONS 
sim_matrix = load_sim_matrix()
projects_df = load_projects()

CRS3_MERGED = getCRS3()
CRS5_MERGED = getCRS5()
SDG_NAMES = getSDG()

COUNTRY_OPTION_LIST = getCountry()

# LOAD MODEL FROM CACHE FO SEMANTIC SEARCH
model = load_model()
sentences, embeddings = load_embeddings_and_index()

def show_page():
    st.write(f"Current RAM usage of this app: {get_process_memory():.2f} MB")
    st.write("Similarities")

    st.session_state.crs5_option_disabled = True
    col1, col2 = st.columns([1, 1])
    with col1:
        # CRS 3 SELECTION
        crs3_option = st.multiselect(
                        'CRS 3',
                        CRS3_MERGED,
                        placeholder="Select"
                        )

        # CRS 5 SELECTION
        ## Only enable crs5 select field when crs3 code is selected
        if crs3_option != []:
            st.session_state.crs5_option_disabled = False

        ## define list of crs5 codes dependend on crs3 codes
        crs5_list = [txt[0].replace('"', "") for crs3_item in crs3_option for code, txt in CRS5_MERGED.items() if str(code)[:3] == str(crs3_item)[-3:]]

        ## crs5 select field
        crs5_option = st.multiselect(
            'CRS 5',
            crs5_list,
            placeholder="Select",
            disabled=st.session_state.crs5_option_disabled
            )
        
        # SDG SELECTION
        sdg_option = st.selectbox(
                label = 'SDG',
                index = None,
                placeholder = "Select SDG",
                options = SDG_NAMES[:-1],
                )

    
    with col2:
        # COUNTRY SELECTION
        country_option = st.multiselect(
                'Country / Countries',
                COUNTRY_OPTION_LIST,
                placeholder="Select"
                )
            
        # ORGA SELECTION
        orga_abbreviation = projects_df["orga_abbreviation"].unique()
        orga_full_names = projects_df["orga_full_name"].unique()
        orga_list = [f"{orga_full_names[i]} ({orga_abbreviation[i].upper()})"for i in range(len(orga_abbreviation))]

        orga_option = st.multiselect(
                'Development Bank / Organization',
                orga_list,
                placeholder="Select"
                )
        
        # SEARCH BOX
        query = st.text_input("Enter your search query:")


    # CRS CODE LIST
    crs3_list = [i[-3:] for i in crs3_option]
    crs5_list = [i[-5:] for i in crs5_option]

    # SDG CODE LIST
    if sdg_option != None:
        sdg_str = sdg_option[0]
    else:
        sdg_str = ""

    # COUNTRY CODES LIST
    country_code_list = [option[-3:-1] for option in country_option]

    # ORGANIZATION CODES LIST
    orga_code_list = [option.split("(")[1][:-1].lower() for option in orga_option]

    # FILTER DF WITH SELECTED FILTER OPTIONS
    
    TOP_X_PROJECTS = 30
    filtered_df = filter_projects(projects_df, crs3_list, crs5_list, sdg_str, country_code_list, orga_code_list, query, model, embeddings, TOP_X_PROJECTS)
    #with col2:
        # Semantic Search
        #searched_filtered_df = semantic_search.show_search(model, embeddings, sentences, filtered_df, TOP_X_PROJECTS)

    # FIND MATCHES
    p1_df, p2_df = calc_matches(filtered_df, projects_df, sim_matrix, TOP_X_PROJECTS)

    # SHOW THE RESULT
    show_table(p1_df, p2_df)

    del p1_df, p2_df, crs3_list, crs5_list, sdg_str, filtered_df
    gc.collect()