app.py

import streamlit as st
import pandas as pd
import sys
import os
from datasets import load_from_disk, load_dataset
from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import time
from annotated_text import annotated_text

ORG_ID = "cornell-authorship"
TOKEN = st.secrets["token"]

@st.cache
def preprocess_text(s):
    return list(filter(lambda x: x!= '', (''.join(c if c.isalnum() or c == ' ' else ' ' for c in s)).split(' ')))

@st.cache
def get_pairwise_distances(model):
    dataset = load_dataset(f"{ORG_ID}/{model}_distance", use_auth_token=TOKEN)["train"]
    df = pd.DataFrame(dataset).set_index('index')
    return df

@st.cache
def get_pairwise_distances_chunked(model, chunk):
    # for df in pd.read_csv(f"{ASSETS_PATH}/{model}/pairwise_distances.csv", chunksize = 16):
    # 	print(df.iloc[0]['queries'])
    # 	if chunk == int(df.iloc[0]['queries']):
    # 		return df
    return get_pairwise_distances(model)

@st.cache
def get_query_strings():
    # df = pd.read_json(hf_hub_download(repo_id=repo_id, filename="IUR_Reddit_test_queries_english.jsonl"), lines = True)
    dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_queries_english", use_auth_token=TOKEN)["train"]
    df = pd.DataFrame(dataset)
    df['index'] = df.reset_index().index
    return df
    # df['partition'] = df['index']%100
    # df.to_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.parquet", index = 'index', partition_cols = 'partition')
    
    # return pd.read_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.parquet", columns=['fullText', 'index', 'authorIDs'])

@st.cache
def get_candidate_strings():
    # df = pd.read_json(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.jsonl", lines = True)
    dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_candidates_english", use_auth_token=TOKEN)["train"]
    df = pd.DataFrame(dataset)
    df['index'] = df.reset_index().index
    return df
    # df['partition'] = df['index']%100
    # df.to_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.parquet", index = 'index', partition_cols = 'partition')
    # return pd.read_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.parquet", columns=['fullText', 'index', 'authorIDs'])

@st.cache
def get_embedding_dataset(model):
    # data = load_from_disk(f"{ASSETS_PATH}/{model}/embedding")
    st.write(model)
    data = load_dataset(f"{ORG_ID}/{model}_embedding", use_auth_token=TOKEN)
    st.write(type(data))
    return data

@st.cache
def get_bad_queries(model):
    df = get_query_strings().iloc[list(get_pairwise_distances(model)['queries'].unique())][['fullText', 'index', 'authorIDs']]
    return df

@st.cache
def get_gt_candidates(model, author):
    gt_candidates = get_candidate_strings()
    df = gt_candidates[gt_candidates['authorIDs'].apply(lambda x: x[0]) == author]
    return df

@st.cache
def get_candidate_text(l):
    return get_candidate_strings().at[l,'fullText']

@st.cache
def get_annotated_text(text, word, pos):
    # print("here", word, pos)
    start= text.index(word, pos)
    end = start+len(word)
    return (text[:start], (text[start:end ], 'SELECTED'), text[end:]), end

class AgGridBuilder:
    __static_key = 0
    def build_ag_grid(table, display_columns):
        AgGridBuilder.__static_key += 1
        options_builder = GridOptionsBuilder.from_dataframe(table[display_columns])
        options_builder.configure_pagination(paginationAutoPageSize=False, paginationPageSize=10)
        options_builder.configure_selection(selection_mode= 'single', pre_selected_rows = [0])
        options = options_builder.build()
        return AgGrid(table, gridOptions = options, fit_columns_on_grid_load=True, key = AgGridBuilder.__static_key, reload_data = True, update_mode = GridUpdateMode.SELECTION_CHANGED | GridUpdateMode.VALUE_CHANGED)

if __name__ == '__main__':
    st.set_page_config(layout="wide")

    # models = filter(lambda file_name: os.path.isdir(f"{ASSETS_PATH}/{file_name}") and not file_name.endswith(".parquet"), os.listdir(ASSETS_PATH))
    models = ['luar_clone2_top_100']
 
    with st.sidebar:
        current_model = st.selectbox(
            "Select Model to analyze", 
            models
            )

    pairwise_distances = get_pairwise_distances(current_model)
    embedding_dataset = get_embedding_dataset(current_model)

    candidate_string_grid = None
    gt_candidate_string_grid  = None
    with st.container():
        t1 = time.time()
        st.title("Full Text")
        col1, col2 = st.columns([14, 2])
        t2 = time.time()
        query_table = get_bad_queries(current_model)
        t3 = time.time()
        # print(query_table)
        with col2:
            index = st.number_input('Enter Query number to inspect', min_value = 0, max_value = query_table.shape[0], step = 1)
            query_text = query_table.loc[index]['fullText']
            preprocessed_query_text = preprocess_text(query_text)
            text_highlight_index = st.number_input('Enter word #', min_value = 0, max_value = len(preprocessed_query_text), step = 1)
            query_index = int(query_table.iloc[index]['index'])

        with col1:
            if 'pos_highlight' not in st.session_state or text_highlight_index == 0:
                st.session_state['pos_highlight'] = text_highlight_index
                st.session_state['pos_history'] = [0]

            if st.session_state['pos_highlight'] > text_highlight_index:
                st.session_state['pos_history'] = st.session_state['pos_history'][:-2]
                if len(st.session_state['pos_history']) == 0:
                    st.session_state['pos_history'] = [0]
            # print("pos", st.session_state['pos_history'], st.session_state['pos_highlight'], text_highlight_index)
            anotated_text_, pos = get_annotated_text(query_text, preprocessed_query_text[text_highlight_index-1], st.session_state['pos_history'][-1]) if text_highlight_index >= 1 else ((query_text), 0)
            if st.session_state['pos_highlight'] < text_highlight_index:
                st.session_state['pos_history'].append(pos)
            st.session_state['pos_highlight'] = text_highlight_index
            annotated_text(*anotated_text_)
            # annotated_text("Lol, this" , ('guy', 'SELECTED') , "is such a PR chameleon. \n\n In the Chan Zuckerberg Initiative announcement, he made it sound like he was giving away all his money to charity <PERSON> or <PERSON>. http://www.businessinsider.in/Mark-Zuckerberg-says-hes-giving-99-of-his-Facebook-shares-45-billion-to-charity/articleshow/50005321.cms Apparently, its just a VC fund. And there are still people out there who believe Facebook.org was an initiative to bring Internet to the poor.")
            t4 = time.time()

        # print(f"query time query text: {t3-t2}, total time: {t4-t1}")
    with st.container():
        st.title("Top 16 Recommended Candidates")
        col1, col2, col3 = st.columns([10, 4, 2])	
        rec_candidates = pairwise_distances[pairwise_distances["queries"]==query_index]['candidates']
        # print(rec_candidates)
        l = list(rec_candidates)
        with col3:
            candidate_rec_index = st.number_input('Enter recommended candidate number to inspect', min_value = 0, max_value = len(l), step = 1)
            print("l:",l, query_index)
            pairwise_candidate_index = int(l[candidate_rec_index])
        with col1:
            st.header("Text")
            t1 = time.time()
            st.write(get_candidate_text(pairwise_candidate_index))
            t2 = time.time()
        with col2:
            st.header("Cosine Distance")
            st.write(float(pairwise_distances[\
                ( pairwise_distances['queries'] == query_index ) \
                &
                ( pairwise_distances['candidates'] == pairwise_candidate_index)]['distances']))
        print(f"candidate string retreival: {t2-t1}")
    with st.container():
        t1 = time.time()
        st.title("Candidates With Same Authors As Query")
        col1, col2, col3 = st.columns([10, 4, 2])
        t2 = time.time()
        gt_candidates = get_gt_candidates(current_model, query_table.iloc[query_index]['authorIDs'][0])
        t3 = time.time()

        with col3:
            candidate_index = st.number_input('Enter ground truthnumber to inspect', min_value = 0, max_value = gt_candidates.shape[0], step = 1)
            gt_candidate_index = int(gt_candidates.iloc[candidate_index]['index'])
        with col1:
            st.header("Text")
            st.write(gt_candidates.iloc[candidate_index]['fullText'])
        with col2:
            t4 = time.time()
            st.header("Cosine Distance")
            st.write(1-cosine_similarity(np.array([embedding_dataset['queries'][query_index]['embedding']]), np.array([embedding_dataset['candidates'][gt_candidate_index]['embedding']]))[0,0])
            t5 = time.time()
        print(f"find gt candidates: {t3-t2}, find cosine: {t5-t4}, total: {t5-t1}")