297 lines
12 KiB
Python
297 lines
12 KiB
Python
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"""
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app.py — SHEQ Analysis Tool — Flask web application.
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Run:
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python app.py
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Then open http://localhost:5000
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The sidebar has two sections:
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1. Events Explorer — filter and chart Events data interactively.
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2. Generate Report — run the full analysis across Events, Safety Energy,
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and LLC Data and download a comprehensive DOCX report.
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"""
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from __future__ import annotations
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import logging
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import os
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from datetime import datetime
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import pandas as pd
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from flask import Flask, jsonify, render_template, request, send_file
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from config import (
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EVENTS_FILE, LLC_FILE, SAFETY_ENERGY_FILE,
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DEFAULT_PD1_NAME, DEFAULT_PD2_NAME,
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DEFAULT_START_DATE, DEFAULT_SPLIT_DATE,
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OUTPUT_DIR,
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)
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from data_loader import load_all, get_body_parts, load_and_prepare
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from analysis_engine import run_full_analysis
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from report_builder import build_report
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from ppt_builder import build_presentation
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# ── Logging ──────────────────────────────────────────────────────────────────
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s %(levelname)-8s %(name)s %(message)s",
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datefmt="%H:%M:%S",
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)
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log = logging.getLogger("app")
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app = Flask(__name__)
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# ── Cached raw DataFrames (loaded on first request) ──────────────────────────
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_CACHE: dict[str, pd.DataFrame | None] = {
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"events": None,
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}
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def _get_events_df() -> pd.DataFrame:
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"""Return the raw Events DataFrame, loading from disk on first call."""
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if _CACHE["events"] is None:
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log.info("Loading Events from %s", EVENTS_FILE)
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df = pd.read_excel(EVENTS_FILE)
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# Normalise date column — handle "Monday, 25 March 2024" and ISO formats
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date_col = "EventDate" if "EventDate" in df.columns else "Event Date"
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df["_date"] = df[date_col].apply(_parse_one_date)
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_CACHE["events"] = df
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return _CACHE["events"].copy()
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def _parse_one_date(val) -> pd.Timestamp:
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if pd.isna(val):
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return pd.NaT
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s = str(val).strip()
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if "," in s and len(s.split(",")[0].split()) == 1:
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s = s.split(",", 1)[1].strip()
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try:
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return pd.to_datetime(s, dayfirst=True)
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except Exception:
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return pd.NaT
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# ─────────────────────────────────────────────────────────────────────────────
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# Web UI
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# ─────────────────────────────────────────────────────────────────────────────
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@app.route("/")
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def index():
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df = _get_events_df()
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min_date = df["_date"].min().strftime("%Y-%m-%d")
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max_date = df["_date"].max().strftime("%Y-%m-%d")
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# Handle both column name variants
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evt_col = "EventType" if "EventType" in df.columns else "Event Type"
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cons_col = "Actual Consequence"
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event_types = sorted(df[evt_col].dropna().unique().tolist())
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consequences = sorted(df[cons_col].dropna().unique().tolist())
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return render_template(
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"index.html",
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min_date=min_date,
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max_date=max_date,
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event_types=event_types,
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consequences=consequences,
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total_events=len(df),
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)
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# ─────────────────────────────────────────────────────────────────────────────
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# Events Explorer API
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# ─────────────────────────────────────────────────────────────────────────────
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@app.route("/api/filter", methods=["POST"])
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def api_filter():
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"""Return filtered summary stats as JSON for the Events Explorer."""
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params = request.json or {}
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df = _get_events_df()
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evt_col = "EventType" if "EventType" in df.columns else "Event Type"
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cons_col = "Actual Consequence"
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crp_col = "CRP Involved" if "CRP Involved" in df.columns else "CRPInvolved"
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rc_col = "Root Cause Category"
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inj_col = "Ventia Injury Classification"
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bp_col = "Bodily Location"
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# Filters
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if params.get("start_date"):
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df = df[df["_date"] >= pd.Timestamp(params["start_date"])]
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if params.get("end_date"):
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df = df[df["_date"] <= pd.Timestamp(params["end_date"])]
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if params.get("event_types"):
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df = df[df[evt_col].isin(params["event_types"])]
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if params.get("consequences"):
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df = df[df[cons_col].isin(params["consequences"])]
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if len(df) == 0:
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return jsonify({"error": "No events match the selected filters.", "total": 0})
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# Summary stats
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evt_counts = df[evt_col].value_counts().to_dict()
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cons_counts = df[cons_col].value_counts().to_dict()
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inj_class = (
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df[inj_col].value_counts().to_dict()
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if inj_col in df.columns else {}
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)
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dow_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
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dow = (
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df["_date"].dt.day_name().value_counts()
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.reindex(dow_order, fill_value=0).to_dict()
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)
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monthly = df.groupby(df["_date"].dt.to_period("M")).size()
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monthly_data = {str(k): int(v) for k, v in monthly.items()}
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bp = (
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get_body_parts(df[bp_col]).value_counts().head(10).to_dict()
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if bp_col in df.columns else {}
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)
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rc = (
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df[rc_col].value_counts().to_dict()
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if rc_col in df.columns else {}
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)
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crp: dict = {}
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if crp_col in df.columns:
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crp = df[crp_col].value_counts().to_dict()
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crp.pop("None Identified", None)
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crp.pop("Under Investigation", None)
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# Investigation performance — use available columns
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lag_col = next((c for c in ("Days to Investigate", "Event Lag", "Days to Enter")
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if c in df.columns), None)
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close_col = "Days to Close" if "Days to Close" in df.columns else None
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inv_med = df[lag_col].dropna().median() if lag_col else None
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close_med = df[close_col].dropna().median() if close_col else None
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return jsonify({
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"total": len(df),
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"date_range": (
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f"{df['_date'].min().strftime('%d %b %Y')} "
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f"\u2013 {df['_date'].max().strftime('%d %b %Y')}"
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),
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"event_types": evt_counts,
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"consequences": cons_counts,
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"injury_classification": inj_class,
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"day_of_week": dow,
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"monthly": monthly_data,
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"body_parts": bp,
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"root_causes": rc,
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"crp": crp,
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"median_investigate_days": round(inv_med, 1) if inv_med and pd.notna(inv_med) else None,
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"median_close_days": round(close_med, 1) if close_med and pd.notna(close_med) else None,
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"closed_pct": round(
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(df["Status"] == "Closed").sum() / len(df) * 100, 1
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) if "Status" in df.columns else None,
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})
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# ─────────────────────────────────────────────────────────────────────────────
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# Comprehensive Report API
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# ─────────────────────────────────────────────────────────────────────────────
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@app.route("/api/generate_full_report", methods=["POST"])
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def api_generate_full_report():
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"""
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Load all three data sources, run the full analysis pipeline, build the
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DOCX report, and return it as a file download.
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"""
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params = request.json or {}
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start_date = params.get("start_date", DEFAULT_START_DATE)
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export_format = str(params.get("export_format", "docx")).lower()
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if export_format not in {"docx", "pptx"}:
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return jsonify({"success": False, "error": "Supported export formats are DOCX and PPTX."}), 400
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events_path = params.get("events_file", EVENTS_FILE)
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se_path = params.get("safety_energy_file", SAFETY_ENERGY_FILE)
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llc_path = params.get("llc_file", LLC_FILE)
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run_id = datetime.now().strftime("%Y%m%d_%H%M%S")
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run_dir = os.path.join(OUTPUT_DIR, run_id)
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try:
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log.info("Starting full report generation (run_id=%s)", run_id)
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data = load_all(events_path, se_path, llc_path)
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results = run_full_analysis(
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events = data["events"],
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safety_energy = data["safety_energy"],
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llc = data["llc"],
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start_date = start_date,
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split_date = DEFAULT_SPLIT_DATE,
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pd1_name = DEFAULT_PD1_NAME,
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pd2_name = DEFAULT_PD2_NAME,
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output_dir = run_dir,
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)
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if export_format == "pptx":
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report_path = build_presentation(results, run_dir)
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download_name = f"SHEQ_Safety_Performance_{run_id}.pptx"
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mimetype = "application/vnd.openxmlformats-officedocument.presentationml.presentation"
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else:
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report_path = build_report(results, run_dir)
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download_name = f"SHEQ_Safety_Performance_{run_id}.docx"
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mimetype = "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
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log.info("Report ready: %s", report_path)
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return send_file(
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report_path,
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as_attachment=True,
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download_name=download_name,
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mimetype=mimetype,
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)
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except FileNotFoundError as e:
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log.error("File not found: %s", e)
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return jsonify({"success": False, "error": str(e)}), 404
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except Exception as e:
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log.exception("Report generation failed")
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return jsonify({"success": False, "error": str(e)}), 500
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# ── Legacy endpoint (kept for backwards compatibility) ────────────────────────
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@app.route("/api/download_report", methods=["POST"])
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def api_download_report():
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"""Legacy Events-only PD comparison report (preserved from v1)."""
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params = request.json or {}
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start_date = params.get("start_date", DEFAULT_START_DATE)
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split_date = params.get("split_date", DEFAULT_SPLIT_DATE)
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pd1_name = params.get("pd1_name", DEFAULT_PD1_NAME)
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pd2_name = params.get("pd2_name", DEFAULT_PD2_NAME)
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run_dir = os.path.join(OUTPUT_DIR, datetime.now().strftime("%Y%m%d_%H%M%S"))
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try:
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from analysis import run_analysis
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docx_path = run_analysis(
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EVENTS_FILE, start_date, split_date, pd1_name, pd2_name, run_dir
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)
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return send_file(
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docx_path, as_attachment=True,
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download_name="SHEQ_PD_Comparison.docx",
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)
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except Exception as e:
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log.exception("Legacy report generation failed")
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return jsonify({"success": False, "error": str(e)}), 500
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# ─────────────────────────────────────────────────────────────────────────────
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# Entry point
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# ─────────────────────────────────────────────────────────────────────────────
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if __name__ == "__main__":
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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log.info("SHEQ Analysis Tool starting on http://localhost:5000")
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log.info(" Events: %s", EVENTS_FILE)
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log.info(" Safety Energy: %s", SAFETY_ENERGY_FILE)
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log.info(" LLC Data: %s", LLC_FILE)
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app.run(debug=True, port=5000)
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