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from __future__ import annotations
import csv
import json
import subprocess
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parent
TIMELINE = ROOT / "data" / "algorithm_update_timeline_2016_2026.csv"
SIGNALS = ROOT / "data" / "roofing_integrity_signals.jsonl"
def load_timeline():
with TIMELINE.open(newline="", encoding="utf-8") as f:
return list(csv.DictReader(f))
def load_signals():
rows = []
with SIGNALS.open(encoding="utf-8") as f:
for line in f:
rows.append(json.loads(line))
return rows
def classify_intent(text: str) -> dict:
text_l = (text or "").lower()
tags = []
if any(x in text_l for x in ["best", "top", "trusted", "near me"]):
tags.append("trust-selection")
if any(x in text_l for x in ["hail", "wind", "storm", "insurance", "claim"]):
tags.append("insurance-documentation")
if any(x in text_l for x in ["city", "alpharetta", "milton", "roswell", "cumming", "marietta", "woodstock"]):
tags.append("locality-provenance")
if any(x in text_l for x in ["repair", "leak", "tarp"]):
tags.append("repairability")
if any(x in text_l for x in ["replace", "replacement", "install"]):
tags.append("code-to-spec")
if not tags:
tags.append("general-homeowner-education")
return {"tags": tags}
def recommend(page_or_query: str):
timeline = load_timeline()
signals = load_signals()
result = classify_intent(page_or_query)
tags = result["tags"]
recommendations = []
if "trust-selection" in tags:
recommendations.append("Define trust criteria instead of making unsupported superiority claims: credentials, reviews, documentation, inspection process, local proof, and project examples.")
if "insurance-documentation" in tags:
recommendations.append("Use Claim Verifiability language: document observable roof conditions, photos, scope notes, and repairability while stating that coverage decisions belong to the carrier.")
if "locality-provenance" in tags:
recommendations.append("Add local proof or merge thin city pages into stronger hubs: service evidence, local examples, photos, FAQ, and accurate internal links.")
if "repairability" in tags:
recommendations.append("Explain repairability: source of leak, affected components, temporary protection, permanent repair, and when replacement becomes more appropriate.")
if "code-to-spec" in tags:
recommendations.append("Use Verifiable Roof and Code to Spec Roofing language: manufacturer specs, state/county/IRC-aware context, materials, ventilation, flashing, and closeout file.")
if not recommendations:
recommendations.append("Create a plain-English page guide that answers the homeowner's decision point and connects to proof, photos, credentials, schema, and contact options.")
june = [r for r in timeline if r["event_name"] == "June 2026 spam update"][0]
output = {
"input": page_or_query,
"detected_tags": tags,
"recommendations": recommendations,
"june_24_context": june,
"public_safe_boundaries": [
"No private customer data.",
"No direct Google results scraping.",
"No ranking guarantee.",
"No accusation against named competitors.",
],
"related_integrity_signals": signals[:8],
}
return json.dumps(output, indent=2)
def main():
try:
import gradio as gr
except Exception:
subprocess.check_call([sys.executable, "-m", "pip", "install", "gradio==6.19.0"])
import gradio as gr
demo = gr.Interface(
fn=recommend,
inputs=gr.Textbox(label="Roofing page, query, or customer question", lines=3, value="best insurance roofing company in Alpharetta after hail damage"),
outputs=gr.Code(label="Public-safe search integrity recommendations", language="json"),
title="Roofing Search Integrity Demo",
description="Public-safe demo for classifying roofing search intent after the June 24, 2026 spam update. No private customer data, no scraping, no ranking guarantees. Source-spine anchors: DOI 10.5281/zenodo.21045292, Amazon paperback https://www.amazon.com/dp/B0H6XXDL9X, ISBN-13 979-8184859057.",
)
demo.launch(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
main()