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import streamlit as st
import numpy as np
import pandas as pd
import os
import json
import altair as alt
from pathlib import Path
class Dashboard:
class Model:
pageTitle = "Dashboard"
documentsTitle = "Pages"
documentsCount = "10.5K"
documentsDelta = "125"
annotationsTitle = "Documents"
annotationsCount = "510"
annotationsDelta = "-2"
accuracyTitle = "Accuracy"
accuracyCount = "87.9%"
accuracyDelta = "0.1%"
trainingTitle = "Training Time"
trainingCount = "1.5 hrs"
trainingDelta = "10 mins"
processingTitle = "Processing Time"
processingCount = "3 secs"
processingDelta = "-0.1 secs"
titleDataExtraction = "## Data Extraction"
titleModelTraining = "## Model Training"
titleDataAnnotation = "## Data Annotation"
titleDocumentTypes = "## Document Types"
status_file = "docs/status.json"
annotation_files_dir = "docs/json"
def view(self, model):
# st.title(model.pageTitle)
with st.container():
col1, col2, col3, col4, col5 = st.columns(5)
with col1:
st.metric(label=model.documentsTitle, value=model.documentsCount, delta=model.documentsDelta)
with col2:
st.metric(label=model.annotationsTitle, value=model.annotationsCount, delta=model.annotationsDelta)
with col3:
st.metric(label=model.accuracyTitle, value=model.accuracyCount, delta=model.accuracyDelta)
with col4:
st.metric(label=model.trainingTitle, value=model.trainingCount, delta=model.trainingDelta, delta_color="inverse")
with col5:
st.metric(label=model.processingTitle, value=model.processingCount, delta=model.processingDelta, delta_color="inverse")
st.markdown("---")
with st.container():
st.write(model.titleDataExtraction)
chart_data = pd.DataFrame(
np.random.randn(20, 3),
columns=['a', 'b', 'c'])
st.line_chart(chart_data)
st.markdown("---")
with st.container():
col1, col2, col3 = st.columns(3)
with col1:
with st.container():
st.write(model.titleDataAnnotation)
total, completed, in_progress = self.calculate_annotation_stats(model)
source = pd.DataFrame({"Status": ["Completed", "In Progress"], "value": [completed, in_progress]})
c = alt.Chart(source).mark_arc(innerRadius=50).encode(
theta=alt.Theta(field="value", type="quantitative"),
color=alt.Color(field="Status", type="nominal"),
)
st.altair_chart(c, use_container_width=True)
with col2:
with st.container():
st.write(model.titleModelTraining)
source = pd.DataFrame({"Status": ["Running", "Failed", "Successful"], "value": [2, 10, 14]})
c = alt.Chart(source).mark_arc(innerRadius=50).encode(
theta=alt.Theta(field="value", type="quantitative"),
color=alt.Color(field="Status", type="nominal"),
)
st.altair_chart(c, use_container_width=True)
with col3:
with st.container():
st.write(model.titleDocumentTypes)
source = pd.DataFrame({"Types": ["Receipt", "Invoice", "General Form", "Claim"], "value": [22, 130, 5, 44]})
c = alt.Chart(source).mark_arc(innerRadius=50).encode(
theta=alt.Theta(field="value", type="quantitative"),
color=alt.Color(field="Types", type="nominal"),
)
st.altair_chart(c, use_container_width=True)
def calculate_annotation_stats(self, model):
completed = 0
in_progress = 0
data_dir_path = Path(model.annotation_files_dir)
for file_name in data_dir_path.glob("*.json"):
with open(file_name, "r") as f:
data = json.load(f)
v = data['meta']['version']
if v == 'v0.1':
in_progress += 1
else:
completed += 1
total = completed + in_progress
status_json = {
"annotations": [
{
"completed": completed,
"in_progress": in_progress,
"total": total
}
]
}
with open(model.status_file, "w") as f:
json.dump(status_json, f, indent=2)
return total, completed, in_progress
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