app.py

import streamlit as st
import os
import pathlib
import pandas as pd
from collections import defaultdict
import json
import copy
import plotly.express as px

from dataset_loading import load_local_qrels, load_local_corpus, load_local_queries


os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
st.set_page_config(layout="wide")

current_checkboxes = []
query_input = None

@st.cache_data
def convert_df(df):
    # IMPORTANT: Cache the conversion to prevent computation on every rerun
    return df.to_csv(path_or_buf=None, index=False, quotechar='"').encode('utf-8')


def create_histogram_relevant_docs(relevant_df):
    # turn results into a dataframe and then plot
    fig = px.histogram(relevant_df, x="relevant_docs")
    # make it fit in one column
    fig.update_layout(
        height=400,
        width=250
    )
    return fig


def get_current_data():
    cur_query_data = []
    cur_query = query_input.replace("\n", "\\n")
    for doc_id, checkbox in current_checkboxes:
        if checkbox:
            cur_query_data.append({
                "new_narrative": cur_query,
                "qid": st.session_state.selectbox_instance,
                "doc_id": doc_id,
                "is_relevant": 0
            })

    # return the data as a CSV pandas
    return convert_df(pd.DataFrame(cur_query_data))




if 'cur_instance_num' not in st.session_state:
    st.session_state.cur_instance_num = -1


def validate(config_option, file_loaded):
    if config_option != "None" and file_loaded is None:
        st.error("Please upload a file for " + config_option)
        st.stop()


with st.sidebar:
    st.title("Options")
    st.header("Upload corpus")
    corpus_file = st.file_uploader("Choose a file", key="corpus")
    corpus = load_local_corpus(corpus_file)
    st.header("Upload queries")
    queries_file = st.file_uploader("Choose a file", key="queries")
    queries = load_local_queries(queries_file)
    st.header("Upload qrels")
    qrels_file = st.file_uploader("Choose a file", key="qrels")
    qrels = load_local_qrels(qrels_file)

    ## make sure all qids in qrels are in queries and write out a warning if not
    if queries is not None and qrels is not None:
        missing_qids = set(qrels.keys()) - set(queries.keys()) | set(queries.keys()) - set(qrels.keys())
        if len(missing_qids) > 0:
            st.warning(f"The following qids in qrels are not in queries and will be deleted: {missing_qids}")
            # remove them from qrels and queries
            for qid in missing_qids:
                if qid in qrels:
                    del qrels[qid]
                if qid in queries:
                    del queries[qid]

    data = []
    for key, value in qrels.items():
        data.append({"relevant_docs": len(value)})
    relevant_df = pd.DataFrame(data)

    z = st.header("Analysis Options")
    # sliderbar of how many Top N to choose
    n_relevant_docs = st.slider("Number of relevant docs", 1, 999, 20)


col1, col2 = st.columns([1, 3], gap="large")

if corpus is not None and queries is not None and qrels is not None:
    with st.sidebar:
        st.success("All files uploaded")

    with col1:
        # breakpoint()
        set_of_cols =  set(qrels.keys())
        container_for_nav = st.container()
        name_of_columns = sorted([item for item in set_of_cols])
        instances_to_use = name_of_columns
        st.title("Instances")
        
        def sync_from_drop():
            if st.session_state.selectbox_instance == "Overview":
                st.session_state.number_of_col = -1
                st.session_state.cur_instance_num = -1
            else:
                index_of_obj = name_of_columns.index(st.session_state.selectbox_instance)
                # print("Index of obj: ", index_of_obj, type(index_of_obj)) 
                st.session_state.number_of_col = index_of_obj
                st.session_state.cur_instance_num = index_of_obj

        def sync_from_number():
            st.session_state.cur_instance_num = st.session_state.number_of_col
            # print("Session state number of col: ", st.session_state.number_of_col, type(st.session_state.number_of_col))
            if st.session_state.number_of_col == -1:
                st.session_state.selectbox_instance = "Overview"
            else:
                st.session_state.selectbox_instance = name_of_columns[st.session_state.number_of_col]


        number_of_col = container_for_nav.number_input(min_value=-1, step=1, max_value=len(instances_to_use) - 1, on_change=sync_from_number, label=f"Select instance by index (up to **{len(instances_to_use) - 1}**)", key="number_of_col")
        selectbox_instance = container_for_nav.selectbox("Select instance by ID", ["Overview"] + name_of_columns, on_change=sync_from_drop, key="selectbox_instance")
        st.divider()  
        # make pie plot showing how many relevant docs there are per query histogram
        st.header("Relevant Docs Per Query")
        plotly_chart = create_histogram_relevant_docs(relevant_df)
        st.plotly_chart(plotly_chart)
        st.divider()
        # now show the number with relevant docs less than `n_relevant_docs`
        st.header("Relevant Docs Less Than {}:".format(n_relevant_docs))
        st.subheader(f'{relevant_df[relevant_df["relevant_docs"] < n_relevant_docs].shape[0]} Queries')


    with col2:
        # get instance number
        inst_index = number_of_col

        if inst_index >= 0:
            inst_num = instances_to_use[inst_index]
            
            st.markdown("<h1 style='text-align: center; color: black;text-decoration: underline;'>Editor</h1>", unsafe_allow_html=True)


            container = st.container()


            container.divider()

            container.subheader(f"Query")
            
            query_text = queries[str(inst_num)].strip()
            query_input = container.text_area(f"QID: {inst_num}", query_text)
            container.divider()

            ## Documents
            # relevant
            relevant_docs = list(qrels[str(inst_num)].keys())[:n_relevant_docs]
            doc_texts = [(doc_id, corpus[doc_id]["title"] if "title" in corpus[doc_id] else "", corpus[doc_id]["text"]) for doc_id in relevant_docs]
            container.subheader(f"Relevant Documents ({len(list(qrels[str(inst_num)].keys()))})")
            current_checkboxes = []
            for (docid, title, text) in doc_texts:
                current_checkboxes.append((docid, container.checkbox(f'{docid} is Non-Relevant', key=docid)))
                container.text_area(f"{docid}:", text)


            container.divider()
            if st.checkbox("Download data as CSV"):
                st.download_button(
                    label="Download data as CSV",
                    data=get_current_data(),
                    file_name=f'annotation_query_{inst_num}.csv',
                    mime='text/csv',
                )

        # none checked
        elif inst_index < 0:
            st.title("Overview")

    

else:
    st.warning("Please choose a dataset and upload a run file. If you chose \"custom\" be sure that you uploaded all files (queries, corpus, qrels)")