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import base64
from io import BytesIO
import numpy as np
import streamlit as st
from PIL import Image
import pandas as pd
from datasets import load_dataset
from grascii import GrasciiSearcher, InvalidGrascii, ReverseSearcher
from report import report_dialog
from vision import run_vision
# from save_image import save_image
MAX_GRASCII_LENGTH = 16
@st.cache_data(show_spinner="Loading shorthand images")
def load_images():
ds = load_dataset(
"grascii/gregg-preanniversary-words", split="train", token=st.secrets.HF_TOKEN
)
image_map = {}
for row in ds:
buffered = BytesIO()
row["image"].save(buffered, format="PNG")
b64 = base64.b64encode(buffered.getvalue())
image_map[row["longhand"]] = "data:image/png;base64," + b64.decode("utf-8")
return image_map
image_map = load_images()
def on_submit():
if "grascii_text_box" in st.session_state:
st.session_state["grascii"] = st.session_state["grascii_text_box"]
st.session_state["alternatives"] = {}
def write_grascii_search():
searcher = GrasciiSearcher()
grascii_results = []
search_by = st.radio("Search by", ["text", "image (beta)"], horizontal=True)
with st.form("Grascii Search"):
placeholder = st.empty()
if search_by == "text":
placeholder.text_input(
"Grascii",
value=st.session_state["grascii"],
key="grascii_text_box",
max_chars=MAX_GRASCII_LENGTH,
help="[Grascii Language Reference](https://grascii.readthedocs.io/en/stable/language.html)",
)
else:
with placeholder.container():
image_data = st.file_uploader(
"Image",
type=["png", "jpg"],
help="""
Upload an image of a shorthand form.
At this time, minimal preprocessing is performed on images
before running them through the model. For best results,
upload an image:
- of a closely cropped, single shorthand form
- with the shorthand written in black on a white background
- that does not contain marks beside the shorthand form
""",
)
# save = st.checkbox(
# "Save images I upload for potential inclusion in open-source datasets used to train and improve models",
# key="save_image",
# )
if image_data:
image = Image.open(image_data).convert("RGBA")
background = Image.new("RGBA", image.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, image)
arr = np.array([alpha_composite.convert("L")])
predictions = run_vision(arr)
alternatives = {"".join(p): True for p in predictions}
if st.session_state["alternatives"] != alternatives:
st.session_state["alternatives"] = alternatives
st.session_state["grascii"] = "".join(predictions[0])
# if save:
# save_image(image_data.getvalue(), "-".join(predictions[0]))
with st.expander("Options"):
interpretation = st.radio(
"Interpretation",
["best", "all"],
horizontal=True,
help="""
How to intepret ambiguous Grascii strings.
- best: Only search using the best interpretation.
- all: Search using all possible interpretations.
""",
)
uncertainty = st.slider(
"Uncertainty",
min_value=0,
max_value=2,
value=1,
help="""
The uncertainty of the strokes in the Grascii string.
A value of at least 1 is recommended for image searches.
""",
)
fix_first = st.checkbox(
"Fix First", help="Apply an uncertainty of 0 to the first token."
)
search_mode = st.selectbox(
"Search Mode",
["match", "start", "contain"],
help="""
The type of search to perform.
- match: Search for entries that closely match the Grascii string.
- start: Search for entries that start with the Grascii string.
- contain: Search for entries that contain the Grascii string.
""",
)
annotation_mode = st.selectbox(
"Annotation Mode",
["strict", "retain", "discard"],
index=2,
help="""
How to handle Grascii annotations.
- discard: Annotations are discarded.
Search results may contain annotations in any location.
- retain: Annotations in the input must appear in search results.
Other annotations may appear in the results.
- strict: Annotations in the input must appear in search results.
Other annotations may not appear in the results.
""",
)
aspirate_mode = st.selectbox(
"Aspirate Mode",
["strict", "retain", "discard"],
index=2,
help="""
How to handle Grascii asirates (').
- discard: Aspirates are discarded.
Search results may contain aspirates in any location.
- retain: Aspirates in the input must appear in search results.
Other aspirates may appear in the results.
- strict: Aspirates in the input must appear in search results.
Other aspirates may not appear in the results.
""",
)
disjoiner_mode = st.selectbox(
"Disjoiner Mode",
["strict", "retain", "discard"],
index=0,
help="""
How to handle Grascii disjoiners (^).
- discard: Disjoiners are discarded.
Search results may contain disjoiners in any location.
- retain: Disjoiners in the input must appear in search results.
Other disjoiners may appear in the results.
- strict: Disjoiners in the input must appear in search results.
Other disjoiners may not appear in the results.
""",
)
st.form_submit_button("Search", on_click=on_submit)
grascii = st.session_state["grascii"]
if len(grascii) > MAX_GRASCII_LENGTH:
st.error(f"Grascii too long. Max: {MAX_GRASCII_LENGTH} characters")
return
try:
grascii_results = searcher.sorted_search(
grascii=grascii,
interpretation=interpretation,
uncertainty=uncertainty,
fix_first=fix_first,
search_mode=search_mode,
annotation_mode=annotation_mode,
aspirate_mode=aspirate_mode,
disjoiner_mode=disjoiner_mode,
)
except InvalidGrascii as e:
if grascii:
st.error(f"Invalid Grascii\n```\n{e.context}\n```")
else:
if len(st.session_state["alternatives"]) > 1:
st.pills(
"Alternatives",
st.session_state["alternatives"],
key="alternative",
default=grascii,
on_change=on_alternative_selection,
)
write_results(grascii_results, grascii.upper(), "grascii")
def on_alternative_selection():
if st.session_state["alternative"] is None:
st.session_state["alternative"] = st.session_state["grascii"]
else:
st.session_state["grascii"] = st.session_state["alternative"]
@st.fragment
def write_results(results, term, key_prefix):
rows = map(
lambda r: [
r.entry.grascii,
r.entry.translation,
image_map.get(r.entry.translation),
],
results,
)
data = pd.DataFrame(rows)
r = "Results" if len(data) != 1 else "Result"
st.write(f'{len(data)} {r} for "{term}"')
event = st.dataframe(
data,
use_container_width=True,
column_config={
"0": "Grascii",
"1": "Longhand",
"2": st.column_config.ImageColumn("Shorthand", width="medium"),
},
selection_mode="multi-row",
on_select="rerun",
key=key_prefix + "_data_frame",
hide_index=True,
)
selected_rows = event.selection.rows
if st.button(
"Flag Selected Rows",
key=key_prefix + "_report_button",
disabled=len(selected_rows) == 0,
):
report_dialog(data.iloc[selected_rows])
def write_reverse_search():
searcher = ReverseSearcher()
reverse_results = []
with st.form("Reverse Search"):
word = st.text_input("Word(s)")
st.form_submit_button("Search")
if word:
reverse_results = searcher.sorted_search(
reverse=word,
)
if word:
write_results(reverse_results, word, "reverse")
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