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make_pdf.py — Maai's advocacy record generator.
Turns a build_record() output into a clinician-ready PDF:
her verbatim words, the clinical mappings beside them, and a visible
statement that this record contains no diagnosis or conclusions.
Reuses the fpdf2 patterns proven in Lagomy.
"""
from pathlib import Path
from fpdf import FPDF
# Noto Sans SC — free pan-Unicode font bundled in the repo, so PDFs
# render her words in ANY language, on any machine (incl. HF Spaces).
FONT_PATH = str(Path(__file__).parent / "fonts" / "NotoSansSC.ttf")
def make_pdf(record: dict, annotated: dict = None, output_path: str = "maai_record.pdf") -> str:
"""Render an advocacy record dict into a PDF. Returns the file path."""
if not Path(FONT_PATH).exists():
raise FileNotFoundError(
f"Unicode font not found at {FONT_PATH} — tell Claude, we'll pick another font."
)
pdf = FPDF()
pdf.add_page()
pdf.add_font("Unicode", "", FONT_PATH)
pdf.set_auto_page_break(auto=True, margin=20)
# --- Header ---
pdf.set_font("Unicode", size=22)
pdf.cell(w=0, h=12, text="Maai", new_x="LMARGIN", new_y="NEXT")
pdf.set_font("Unicode", size=11)
pdf.cell(w=0, h=8, text="Symptom advocacy record — prepared from the patient's own words",
new_x="LMARGIN", new_y="NEXT")
pdf.ln(2)
# --- Provenance ---
pdf.set_font("Unicode", size=9)
pdf.cell(w=0, h=6, text=f"Captured: {record['captured_at'][:10]}", new_x="LMARGIN", new_y="NEXT")
pdf.cell(w=0, h=6, text=f"Language detected: {record['language_detected']}", new_x="LMARGIN", new_y="NEXT")
pdf.ln(4)
# --- Her full description, verbatim ---
pdf.set_font("Unicode", size=12)
pdf.cell(w=0, h=8, text="In her own words", new_x="LMARGIN", new_y="NEXT")
pdf.set_font("Unicode", size=10)
pdf.multi_cell(w=0, h=6, text=record["verbatim_description"], new_x="LMARGIN", new_y="NEXT")
pdf.ln(4)
# --- Mapped items ---
pdf.set_font("Unicode", size=12)
pdf.cell(w=0, h=8, text="What she described, in clinical terms", new_x="LMARGIN", new_y="NEXT")
pdf.ln(1)
for item in record["items"]:
pdf.set_font("Unicode", size=10)
pdf.multi_cell(w=0, h=6, text=f'"{item["verbatim"]}"', new_x="LMARGIN", new_y="NEXT")
pdf.multi_cell(w=0, h=6, text=f' - {item["clinical"]}', new_x="LMARGIN", new_y="NEXT")
pdf.ln(2)
# --- SOCRATES gap-map (optional) ---
if annotated and annotated.get("not_yet_described"):
pdf.ln(2)
pdf.set_font("Unicode", size=12)
pdf.cell(w=0, h=8, text="Not yet described — the clinician may wish to ask about",
new_x="LMARGIN", new_y="NEXT")
pdf.set_font("Unicode", size=10)
for dim in annotated["not_yet_described"]:
pdf.multi_cell(w=0, h=6, text=f" - {dim}", new_x="LMARGIN", new_y="NEXT")
# --- Visible guardrail ---
pdf.ln(4)
pdf.set_font("Unicode", size=9)
pdf.multi_cell(
w=0, h=5,
text=(
"This record maps the patient's own words to clinical vocabulary. "
"It contains no diagnosis, assessment, or conclusion. "
"All interpretation rests with the clinician."
),
new_x="LMARGIN", new_y="NEXT",
)
pdf.output(output_path)
return output_path
if __name__ == "__main__":
from chain import build_record
test = (
"I keep waking up at 3am completely drenched in sweat, and I'm so "
"exhausted during the day I can't focus. My periods have gone all "
"over the place too."
)
path = make_pdf(build_record(test))
print(f"\nPDF written: {path}")
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