app.py

import streamlit as st
import pandas as pd
from appStore.prep_data import process_giz_worldwide
from appStore.prep_utils import create_documents, get_client
from appStore.embed import hybrid_embed_chunks
from appStore.search import hybrid_search
from appStore.region_utils import load_region_data, get_country_name
from torch import cuda
import json

# get the device to be used eithe gpu or cpu
device = 'cuda' if cuda.is_available() else 'cpu'


st.set_page_config(page_title="SEARCH IATI",layout='wide')
st.title("GIZ Project Database")
var = st.text_input("Enter Search Query")

# Load the region lookup CSV
region_lookup_path = "docStore/regions_lookup.csv"
region_df = load_region_data(region_lookup_path)

####################  Create the embeddings collection and save ######################
# the steps below need to be performed only once and then commented out any unnecssary compute over-run
##### First we process and create the chunks for relvant data source
#chunks = process_giz_worldwide()
##### Convert to langchain documents
#temp_doc = create_documents(chunks,'chunks')
##### Embed and store docs, check if collection exist then you need to update the collection
collection_name = "giz_worldwide"
#hybrid_embed_chunks(docs= temp_doc, collection_name = collection_name)

################### Hybrid Search ######################################################
client = get_client()
print(client.get_collections())

# Fetch unique country codes from the metadata for the dropdown
@st.cache_data
def get_unique_countries_with_names(_client, collection_name, region_df):
    results = hybrid_search(_client, "", collection_name)
    country_set = set()
    for res in results[0] + results[1]:
        countries = res.payload.get('metadata', {}).get('countries', "[]")
        try:
            country_list = json.loads(countries.replace("'", '"'))
            country_set.update(country_list)
        except json.JSONDecodeError:
            pass
    
    # Map ISO codes to country names
    country_names = [get_country_name(code, region_df) for code in country_set]
    return sorted(country_names)

client = get_client()
unique_countries = get_unique_countries_with_names(client, collection_name, region_df)

# Layout filters in columns
col1, col2, col3 = st.columns([1, 1, 4])

with col1:
    country_filter = st.selectbox("Country Code", ["All"] + unique_countries)
with col2:
    end_year_range = st.slider("Project End Year", min_value=2010, max_value=2030, value=(2010, 2030))

# Checkbox to control whether to show only exact matches
show_exact_matches = st.checkbox("Show only exact matches", value=False)

button=st.button("Search")
#found_docs = vectorstore.similarity_search(var)
#print(found_docs)
# results= get_context(vectorstore, f"find the relvant paragraphs for: {var}")
if button:
    results = hybrid_search(client, var, collection_name)

    # Filter results based on the user's input
    def filter_results(results, country_filter, end_year_range):
        filtered = []
        for res in results:
            metadata = res.payload.get('metadata', {})
            countries = metadata.get('countries', "[]")
            end_year = float(metadata.get('end_year', 0))

            # Process countries string to a list
            try:
                country_list = json.loads(countries.replace("'", '"'))
            except json.JSONDecodeError:
                country_list = []

            # Apply country and year filters
            if (country_filter == "All" or country_filter in country_list) and (end_year_range[0] <= end_year <= end_year_range[1]):
                filtered.append(res)
        return filtered

    # Check user preference for exact matches
    if show_exact_matches:
        st.write(f"Showing **Top 10 Lexical Search results** for query: {var}")
        lexical_results = results[1]  # Lexical results are in index 1
        filtered_lexical_results = filter_results(lexical_results, country_filter, end_year_range)
        for res in filtered_lexical_results[:10]:
            project_name = res.payload['metadata'].get('project_name', 'Project Link')
            url = res.payload['metadata'].get('url', '#')
            st.markdown(f"#### [{project_name}]({url})")
            st.write(res.payload['page_content'])
            st.divider()
    else:
        st.write(f"Showing **Top 10 Semantic Search results** for query: {var}")
        semantic_results = results[0]  # Semantic results are in index 0
        filtered_semantic_results = filter_results(semantic_results, country_filter, end_year_range)
        for res in filtered_semantic_results[:10]:
            project_name = res.payload['metadata'].get('project_name', 'Project Link')
            url = res.payload['metadata'].get('url', '#')
            st.markdown(f"#### [{project_name}]({url})")
            st.write(res.payload['page_content'])
            st.divider()


    #  for i in results: 
    #      st.subheader(str(i.metadata['id'])+":"+str(i.metadata['title_main']))
    #      st.caption(f"Status:{str(i.metadata['status'])}, Country:{str(i.metadata['country_name'])}")
    #      st.write(i.page_content)
    #      st.divider()