app.py

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>&nbsp;<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")

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')



@st.cache_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


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.cache_data
def load_chunked_data():
    data = []
    with open("chunked_data.jsonl", "r") as f:
        for line in f:
            data.append(json.loads(line))
    # rename prompt column to text
    df = pd.DataFrame(data)
    df = df.rename(columns={"prompt": "text"})
    return df


def load_generated_data():
    with open("generated_data.json", "r") as fin:
        data = json.load(fin)["outputs"]

    new_insts = []
    for key, value in data.items():
        item = {
            "venue": key
        }
        if type(value) == str:
            value = ast.literal_eval(value)
        
        if type(value) == dict:
            for cur_key, cur_value in value.items():
                item[cur_key] = cur_value
        else:
            raise ValueError(f"Invalid type {type(value)}: {value}")
        new_insts.append(item)

    return pd.DataFrame(new_insts)

original_df = load_chunked_data()
generated_data = load_generated_data()


def combine_text(item):
    string_text = ""
    for key, value in item.items():
        if key == "venue" or value is None or value == "[]" or type(value) == float or len(value) == 0:
            continue
        string_text += f",{', '.join(value)}\n"
    if "," == string_text[0]:
        string_text = string_text[1:]
    return string_text


original_map = {item["venue"]: item["text"] for item in original_df.to_dict(orient="records")}
generated_map = {item["venue"]: combine_text(item) for item in generated_data.to_dict(orient="records")}
  

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

with st.sidebar:
    st.success("All files uploaded")

with col1:
    # breakpoint()
    ids = original_df["venue"].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)


        container = st.container()

        container.subheader(f"Venue: {inst_num}")

        container.divider()

        
        original_text = original_map[inst_num]
        generated_text = generated_map[inst_num]
        container.subheader(f"Original OCR Text")
        original_input = container.markdown(original_text)
        container.divider()

        container.subheader(f"Generated Text")
        generated_input = container.markdown(generated_text)
        container.divider()

        # print("Original text: ", original_text)
        # print("Generated text: ", generated_text)

        # Diff
        if original_text is not None and generated_input is not None:
            container.subheader("Diff")
            processed_diff = generate_diff_html_word_level(original_map[inst_num], generated_map[inst_num])
            with container.container(border=True):
                st.markdown(processed_diff, unsafe_allow_html=True)


        # editable text, starting from the generated text
        editable_text = container.text_area("Edit the generated text", value=generated_text, height=300)
    

       
        container.divider()
        # download the editable text and venue name
        st.download_button(
            f"Download {inst_num} as CSV",
            convert_df(pd.DataFrame([{"venue": inst_num, "text": editable_text}])),
            f"{inst_num}.csv",
            "text/csv",
            key=f"download_{inst_num}"
        )

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