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| import streamlit as st | |
| import os | |
| import pathlib | |
| import pandas as pd | |
| from collections import defaultdict | |
| import json | |
| import ast | |
| import copy | |
| import re | |
| import tqdm | |
| import pandas as pd | |
| from collections import Counter | |
| import string | |
| import os | |
| import streamlit as st | |
| import difflib | |
| from html import escape | |
| def generate_diff_html_word_level(text1, text2): | |
| """ | |
| Generates word-level difference between text1 and text2 as HTML, correctly handling spaces. | |
| """ | |
| # Splitting texts into words | |
| words1 = text1.split() | |
| words2 = text2.split() | |
| diff = [] | |
| matcher = difflib.SequenceMatcher(None, words1, words2) | |
| for opcode in matcher.get_opcodes(): | |
| tag, i1, i2, j1, j2 = opcode | |
| if tag == 'replace': | |
| diff.append('<del style="background-color: #fbb6ce;">' + escape(' '.join(words1[i1:i2])) + '</del>') | |
| diff.append('<ins style="background-color: #b7e4c7;">' + escape(' '.join(words2[j1:j2])) + '</ins>') | |
| elif tag == 'delete': | |
| diff.append('<del style="background-color: #fbb6ce;">' + escape(' '.join(words1[i1:i2])) + '</del>') | |
| elif tag == 'insert': | |
| diff.append('<ins style="background-color: #b7e4c7;">' + escape(' '.join(words2[j1:j2])) + '</ins>') | |
| elif tag == 'equal': | |
| diff.append(escape(' '.join(words1[i1:i2]))) | |
| # Construct final HTML string | |
| final_html = ' '.join(diff).replace('</del> <ins', '</del> <ins') | |
| return f'<pre style="white-space: pre-wrap;">{final_html}</pre>' | |
| os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" | |
| st.set_page_config(layout="wide") | |
| query_input = None | |
| 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 get_current_data(): | |
| cur_query_data = [] | |
| for id_num, checkbox in current_checkboxes: | |
| if checkbox: | |
| qid, pid = id_num.split("-----") | |
| cur_query_data.append({ | |
| "qid": qid, | |
| "pid": pid, | |
| "is_relevant": 0 | |
| }) | |
| return convert_df(pd.DataFrame(cur_query_data)) | |
| def escape_markdown(text): | |
| # List of characters to escape | |
| # Adding backslash to the list of special characters to escape itself as well | |
| text = text.replace("``", "\"") | |
| text = text.replace("$", "\$") | |
| special_chars = ['\\', '`', '*', '_', '{', '}', '[', ']', '(', ')', '#', '+', '-', '.', '!', '|', "$"] | |
| # Escaping each special character | |
| escaped_text = "".join(f"\\{char}" if char in special_chars else char for char in text) | |
| return escaped_text | |
| if 'cur_instance_num' not in st.session_state: | |
| st.session_state.cur_instance_num = -1 | |
| with st.sidebar: | |
| st.title("Options") | |
| def load_data(): | |
| data = [] | |
| with open("diffs.jsonl", "r") as f: | |
| for line in f: | |
| data.append(json.loads(line)) | |
| df = pd.DataFrame(data) | |
| return df | |
| df = load_data() | |
| df["id_combined"] = df.apply(lambda x: str(x["qid"]), axis=1) | |
| # aggregate the df so that each qid is a row and the rest is a list of instances | |
| df = df.groupby("id_combined").agg(lambda x: x.tolist()).reset_index() | |
| # print details about the diff type | |
| # diff_types = df["diff_type"].value_counts() | |
| # st.write(diff_types) | |
| # # print how many titles differ by looping through each instances | |
| # diff_titles = 0 | |
| # for index, row in df.iterrows(): | |
| # if row["old"] is not None and row["new"] is not None and row["old"][0]["title"] != row["new"][0]["title"]: | |
| # diff_titles += 1 | |
| # st.write(f" Number of titles that differ: {diff_titles}") | |
| original_map = {item["id_combined"]: item for item in df.to_dict(orient="records")} | |
| col1, col2 = st.columns([1, 3], gap="large") | |
| with st.sidebar: | |
| st.success("All files uploaded") | |
| with col1: | |
| # breakpoint() | |
| ids = df["id_combined"].tolist() | |
| set_of_cols = set(ids) | |
| 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() | |
| 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) | |
| current_checkboxes = [] | |
| diff_types = original_map[inst_num]['diff_type'] | |
| num_new = Counter(diff_types)["new"] | |
| instances = original_map[inst_num] | |
| container = st.container() | |
| container.subheader(f"Combined ID: {inst_num} with {len(instances['pid'])} docs ({num_new} are new)") | |
| # container.markdown(f"Diff Type: **{**") | |
| container.divider() | |
| container.subheader(f"Query Info") | |
| container.markdown(f"Query ID: {instances['query_info'][0]['_id']}") | |
| container.markdown(f"**Query**:{instances['query_info'][0]['text']}") | |
| # container.markdown(f"Query Instruction OG: {original_map[inst_num]['query_info']['instruction_og']}") | |
| # container.markdown(f"Query Instruction New: {original_map[inst_num]['query_info']['instruction_changed']}") | |
| # container.subheader("Instruction ") | |
| processed_diff = generate_diff_html_word_level(instances['query_info'][0]['instruction_og'], instances['query_info'][0]['instruction_changed']) | |
| with container.container(): | |
| st.markdown("**Instruction**: " + processed_diff, unsafe_allow_html=True) | |
| container.divider() | |
| for i in range(len(instances["pid"])): | |
| container.markdown(f"Doc {instances['pid'][i]}: **{diff_types[i] if diff_types[i] == 'new' else 'changed'}**") | |
| # previous qrel score was either relevant (>0) or non-relevant | |
| qrel_score = instances["qrel_score"][i] | |
| # if relevant highlight blue, if non-relevant make orange with html tags | |
| if qrel_score > 0: | |
| container.markdown(f"<h3>Previous: <span style='color: blue;'>Relevant</span></h3>", unsafe_allow_html=True) | |
| else: | |
| container.markdown(f"<h3>Previous: <span style='color: orange;'>Not Relevant</span></h3>", unsafe_allow_html=True) | |
| combined_id = str(instances["qid"][i]) + "-----" + instances["pid"][i] | |
| container.subheader(f"Title") | |
| if instances['old'][i] is not None and instances['new'][i] is not None and instances['old'][i][0]['title'] == instances['new'][i][0]['title']: | |
| container.markdown(f"{instances['old'][i][0]['title']}") | |
| elif instances['old'][i] is None and instances['new'][i] is None: | |
| container.markdown("") | |
| else: | |
| if instances['old'][i] is not None: | |
| container.markdown(f"{instances['old'][i][0]['title']}") | |
| elif instances['new'][i] is not None: | |
| container.markdown(f"{instances['new'][i][0]['title']}") | |
| else: | |
| assert False | |
| if instances['old'][i] is not None and instances['new'][i] is not None: | |
| container.subheader("Title Diff") | |
| processed_diff = generate_diff_html_word_level(instances['old'][i][0]['title'], instances['new'][i][0]['title']) | |
| with container.container(): | |
| st.markdown(processed_diff, unsafe_allow_html=True) | |
| # if both are none, say that: | |
| on = container.toggle('Show original text', key="toggle" + combined_id) | |
| if on: | |
| container.subheader(f"Original Text") | |
| if instances['old'][i] is not None: | |
| original_input = container.markdown(instances['old'][i][0]['text']) | |
| else: | |
| original_input = None | |
| container.markdown("") | |
| container.subheader(f"New Text") | |
| # generated_input = container.markdown(instances['new'][i][0]['text']) | |
| # # Diff | |
| # if original_input is not None and generated_input is not None: | |
| # container.subheader("Diff") | |
| # processed_diff = generate_diff_html_word_level(instances['old'][i][0]['text'], instances['new'][i][0]['text']) | |
| # with container.container(): | |
| # st.markdown(processed_diff, unsafe_allow_html=True) | |
| # container.subheader("Full Doc") | |
| container.markdown(instances['full_doc'][i]) | |
| current_checkboxes.append((combined_id, container.checkbox(f'{combined_id} is Non-Relevant', key=combined_id))) | |
| container.divider() | |
| # download the editable text and venue name | |
| 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}_double_check.csv', | |
| mime='text/csv', | |
| ) | |
| # none checked | |
| elif inst_index < 0: | |
| st.title("Overview") |