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125# === Open Source Software ===
# This program is maintained by the University of Illinois Chicago Accessibility
# Engineering Team (https://uic.edu/accessibility/engineering).
# Copyright (C) 2025 University of Illinois Chicago.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program.
# === Library imports ===
import streamlit as st
import json
import os
import glob
import pandas as pd
import plotly.express as px
# Path to folder with JSON files
FOLDER = "./results/"
st.title("Equalify-UIC PDF Analysis Dashboard")
@st.cache_data
def load_data():
records = []
for path in glob.glob(os.path.join(FOLDER, "job_eq-*.json")):
print(f"Loading {path}") # Debug output
with open(path) as f:
try:
data = json.load(f)
job_id = data.get("jobID")
job = data["PDFresults"]["report"]["jobs"][0]["validationResult"][0]
passed = job["details"]["passedChecks"]
failed = job["details"]["failedChecks"]
compliant = job["compliant"]
file_name = job["details"].get("fileName") or job.get("object") or "Unknown"
records.append({
"Job ID": job_id,
"File": file_name,
"Passed Checks": passed,
"Failed Checks": failed,
"Compliant": compliant
})
except Exception as e:
records.append({
"Job ID": os.path.basename(path),
"File": "Error",
"Passed Checks": None,
"Failed Checks": None,
"Compliant": f"Error: {e}"
})
return pd.DataFrame(records)
df = load_data()
total_failed_checks = df["Failed Checks"].sum()
st.metric(label="Total Failed Checks", value=int(total_failed_checks))
st.metric(label="Total Passed Checks", value=int(df["Passed Checks"].sum()))
st.metric(label="Total Files Processed", value=len(df))
total_passed_checks = df["Passed Checks"].sum()
check_summary = pd.DataFrame({
"Result": ["Passed", "Failed"],
"Count": [total_passed_checks, total_failed_checks]
})
st.subheader("Check Results Overview")
st.plotly_chart(
px.pie(check_summary, names="Result", values="Count", title="Passed vs Failed Checks"),
use_container_width=True
)
if df.empty:
st.warning("No valid data loaded. Check your file paths and JSON structure.")
st.stop()
st.dataframe(df)
# Add issue category summary
st.subheader("Issue Descriptions and Failed Checks")
def extract_issue_descriptions():
description_counts = {}
file_appearance = {}
for path in glob.glob(os.path.join(FOLDER, "job_eq-*.json")):
with open(path) as f:
try:
data = json.load(f)
job_id = data.get("jobID", os.path.basename(path))
rule_summaries = data["PDFresults"]["report"]["jobs"][0]["validationResult"][0]["details"].get("ruleSummaries", [])
seen_descriptions = set()
for rule in rule_summaries:
if rule["status"] == "failed":
desc = rule.get("description", "Unknown issue")
description_counts[desc] = description_counts.get(desc, 0) + rule.get("failedChecks", 1)
if desc not in seen_descriptions:
file_appearance.setdefault(desc, set()).add(job_id)
seen_descriptions.add(desc)
except Exception:
continue
total_files = len(df)
rows = []
for desc, count in description_counts.items():
percent = (len(file_appearance.get(desc, [])) / total_files) * 100
rows.append((desc, count, f"{percent:.1f}%"))
return pd.DataFrame(rows, columns=["Description", "Failed Checks", "Percent of Files"]).sort_values("Failed Checks", ascending=False)
desc_df = extract_issue_descriptions()
if not desc_df.empty:
st.dataframe(desc_df)
else:
st.info("No detailed rule description data available.")