import streamlit as st
import streamlit.components.v1 as components
import os, json, re
from reportlab.lib.pagesizes import letter
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import inch
from reportlab.platypus import (
SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
HRFlowable, KeepTogether, PageBreak,
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_RIGHT
from reportlab.lib import colors
from io import BytesIO
from datetime import datetime
from config import *
# ── Shorthand color references
NAVY = BRAND["colors"]["navy"]
TEAL = BRAND["colors"]["teal"]
SKY = BRAND["colors"]["sky"]
ORANGE = BRAND["colors"]["orange"]
AMBER = BRAND["colors"]["amber"]
WHITE = BRAND["colors"]["white"]
# =============================================================================
# RECOMMENDATION ENGINE
# =============================================================================
def score_peptides(selected_goals):
"""Score every peptide against the patient's selected goals.
Returns a list of (peptide_key, score, matched_categories) sorted by score
descending, then by suggested-vial cost ascending (value-first tiebreaker).
"""
# Build category weights from selected goals
cat_scores = {}
for goal in selected_goals:
for cat_name, weight in GOAL_MAPPINGS.get(goal, []):
cat_scores[cat_name] = cat_scores.get(cat_name, 0) + weight
if not cat_scores:
return []
results = []
for pep_key, pep in PEPTIDES.items():
score = 0
matched = []
for cat in pep["categories"]:
if cat in cat_scores:
score += cat_scores[cat]
matched.append(cat)
if score > 0:
results.append((pep_key, score, matched))
# Sort: highest score first, then cheapest suggested-vial price as tiebreaker
results.sort(
key=lambda x: (
-x[1],
PEPTIDES[x[0]]["pricing"].get(PEPTIDES[x[0]]["suggested_vials"], 9999),
)
)
return results
def get_top_categories(selected_goals, limit=3):
"""Return the top N matched categories from selected goals."""
cat_scores = {}
for goal in selected_goals:
for cat_name, weight in GOAL_MAPPINGS.get(goal, []):
cat_scores[cat_name] = cat_scores.get(cat_name, 0) + weight
sorted_cats = sorted(cat_scores.items(), key=lambda x: -x[1])
return [c[0] for c in sorted_cats[:limit]]
def group_by_tier(scored_peptides):
"""Group scored peptides into branded blends, other blends, and singles.
Each tier is sorted high-to-low by score, then cheapest suggested price."""
branded = []
blends = []
singles = []
for pep_key, score, matched in scored_peptides:
pep = PEPTIDES[pep_key]
if pep.get("brand_name"):
branded.append((pep_key, score, matched))
elif pep.get("is_blend"):
blends.append((pep_key, score, matched))
else:
singles.append((pep_key, score, matched))
# Ensure each tier is explicitly sorted: highest score first, most expensive suggested price first as tiebreaker
_sort_key = lambda x: (-x[1], -PEPTIDES[x[0]]["pricing"].get(PEPTIDES[x[0]]["suggested_vials"], 0))
branded.sort(key=_sort_key)
blends.sort(key=_sort_key)
singles.sort(key=_sort_key)
return branded, blends, singles
# =============================================================================
# PDF GENERATOR
# =============================================================================
def generate_pdf(patient_info, selected_goals, top_categories, recommendations):
"""Generate a branded PDF of the peptide recommendation."""
buffer = BytesIO()
doc = SimpleDocTemplate(
buffer, pagesize=letter,
topMargin=0.55 * inch, bottomMargin=0.6 * inch,
leftMargin=0.65 * inch, rightMargin=0.65 * inch,
)
story = []
pw = letter[0] - 1.3 * inch
def ps(name, **kwargs):
return ParagraphStyle(name, **kwargs)
C_NAVY = colors.HexColor(NAVY)
C_TEAL = colors.HexColor(TEAL)
C_ORANGE = colors.HexColor(ORANGE)
C_AMBER = colors.HexColor(AMBER)
C_SKY = colors.HexColor(SKY)
C_LIGHT = colors.HexColor("#eef4f8")
C_CARD = colors.HexColor("#f0f6fa")
C_BORDER = colors.HexColor("#b8d0de")
brand_s = ps("Br", fontName="Helvetica-Bold", fontSize=9, textColor=C_ORANGE, spaceAfter=0, alignment=TA_CENTER)
title_s = ps("T", fontName="Helvetica-Bold", fontSize=20, leading=26, textColor=colors.white, spaceAfter=0, alignment=TA_CENTER)
sub_s = ps("Su", fontName="Helvetica", fontSize=10, textColor=C_SKY, spaceAfter=0, alignment=TA_CENTER)
meta_s = ps("Me", fontName="Helvetica", fontSize=9, leading=14, textColor=C_NAVY, spaceAfter=3)
h2_s = ps("H2", fontName="Helvetica-Bold", fontSize=11, textColor=colors.white, spaceBefore=0, spaceAfter=0)
h3_s = ps("H3", fontName="Helvetica-Bold", fontSize=10, textColor=C_NAVY, spaceBefore=4, spaceAfter=3)
body_s = ps("B", fontName="Helvetica", fontSize=9.5, leading=15, textColor=C_NAVY, spaceAfter=5)
bullet_s = ps("Bu", fontName="Helvetica", fontSize=9.5, leading=15, textColor=C_NAVY, leftIndent=14, spaceAfter=3)
disc_s = ps("Di", fontName="Helvetica-Oblique", fontSize=8, leading=12, textColor=colors.HexColor("#4a6070"), spaceAfter=4)
price_s = ps("Pr", fontName="Helvetica-Bold", fontSize=10, textColor=C_ORANGE, spaceAfter=2)
pep_hdr_s = ps("Ph", fontName="Helvetica-Bold", fontSize=10, textColor=colors.white, spaceBefore=0, spaceAfter=0)
ft_s = ps("Ft", fontName="Helvetica", fontSize=8, leading=12, textColor=C_NAVY, spaceAfter=0, alignment=TA_CENTER)
def banner(para, bg, line_below=None, pad_v=7, pad_h=12):
t = Table([[para]], colWidths=[pw])
cmds = [
("BACKGROUND", (0, 0), (-1, -1), bg),
("LEFTPADDING", (0, 0), (-1, -1), pad_h),
("RIGHTPADDING", (0, 0), (-1, -1), pad_h),
("TOPPADDING", (0, 0), (-1, -1), pad_v),
("BOTTOMPADDING", (0, 0), (-1, -1), pad_v),
]
if line_below:
cmds.append(("LINEBELOW", (0, 0), (-1, -1), 3, line_below))
t.setStyle(TableStyle(cmds))
return t
def section_hdr(text):
return KeepTogether([
Spacer(1, 8),
banner(Paragraph(text.upper(), h2_s), C_NAVY, line_below=C_ORANGE, pad_v=8),
Spacer(1, 6),
])
# ── COVER ────────────────────────────────────────────────────────────────
hdr_tbl = Table([
[Paragraph("IGNITE PERFORMANCE & HEALTH", brand_s)],
[Paragraph("Peptide Protocol Recommendation", title_s)],
[Paragraph("Physician-Supervised Peptide Therapy", sub_s)],
], colWidths=[pw])
hdr_tbl.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, -1), C_NAVY),
("LEFTPADDING", (0, 0), (-1, -1), 20),
("RIGHTPADDING", (0, 0), (-1, -1), 20),
("TOPPADDING", (0, 0), (0, 0), 14),
("BOTTOMPADDING", (0, 0), (0, 0), 4),
("TOPPADDING", (0, 1), (0, 1), 4),
("BOTTOMPADDING", (0, 1), (0, 1), 4),
("TOPPADDING", (0, 2), (0, 2), 2),
("BOTTOMPADDING", (0, 2), (0, 2), 14),
("ALIGN", (0, 0), (-1, -1), "CENTER"),
]))
story.append(hdr_tbl)
story.append(HRFlowable(width="100%", thickness=5, color=C_ORANGE, spaceAfter=12))
# ── Patient info ──
name = patient_info.get("name", "")
date_str = datetime.now().strftime("%B %d, %Y")
pi_tbl = Table([[
Paragraph(f"Prepared for: {name}", meta_s),
Paragraph(f"Date: {date_str}", meta_s),
]], colWidths=[pw * 0.6, pw * 0.4])
pi_tbl.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, -1), C_LIGHT),
("BOX", (0, 0), (-1, -1), 0.75, C_BORDER),
("LINEBELOW", (0, 0), (-1, -1), 2, C_ORANGE),
("LEFTPADDING", (0, 0), (-1, -1), 10),
("RIGHTPADDING", (0, 0), (-1, -1), 10),
("TOPPADDING", (0, 0), (-1, -1), 9),
("BOTTOMPADDING", (0, 0), (-1, -1), 9),
("VALIGN", (0, 0), (-1, -1), "MIDDLE"),
]))
story.append(pi_tbl)
story.append(Spacer(1, 14))
# ── Goals & Categories ──
story.append(section_hdr("Your Goals"))
goals_text = " · ".join(selected_goals)
story.append(Paragraph(f"Selected goals: {goals_text}", body_s))
cats_text = " · ".join(top_categories)
story.append(Paragraph(f"Top therapeutic areas: {cats_text}", body_s))
story.append(Spacer(1, 8))
# ── Recommendations ──
story.append(section_hdr("Recommended Peptides"))
branded, blends, singles = group_by_tier(recommendations)
def render_pep_block(pep_key, score, matched):
pep = PEPTIDES[pep_key]
display_name = pep.get("brand_name") or pep_key
sv = pep["suggested_vials"]
sv_price = pep["pricing"].get(sv, 0)
per_week = sv_price / max((sv * 4), 1) # rough per-week estimate
items = [
Spacer(1, 4),
banner(Paragraph(f" {display_name}", pep_hdr_s), C_TEAL, line_below=C_NAVY, pad_v=5),
]
if pep.get("brand_name") and pep_key != pep["brand_name"]:
items.append(Paragraph(f"{pep_key} ", disc_s))
items.append(Paragraph(pep["description"], body_s))
# ── Layered MOA ──
moa_scott = pep.get("moa_scott", "")
moa_clinical = pep.get("moa_clinical", "")
if moa_scott:
items.append(Paragraph(f'How it works: {moa_clinical} ", disc_s))
items.append(Paragraph(
f"Dosage: {pep['dosage']} | Cycle: {pep['cycle']} | "
f"Suggested vials: {sv}", bullet_s))
items.append(Paragraph(
f"Suggested cycle cost: ${sv_price:,.2f} ({sv} vials) | "
f"~${per_week:,.2f}/week ", price_s))
items.append(Paragraph(
f"Categories: {', '.join(matched)}", disc_s))
items.append(Spacer(1, 6))
return KeepTogether(items)
if branded:
story.append(Paragraph("Ignite Branded Blends — curated multi-peptide protocols", h3_s))
for pep_key, score, matched in branded[:6]:
story.append(render_pep_block(pep_key, score, matched))
if blends:
story.append(Paragraph("Combination Blends ", h3_s))
for pep_key, score, matched in blends[:4]:
story.append(render_pep_block(pep_key, score, matched))
if singles:
story.append(Paragraph("Individual Peptides ", h3_s))
for pep_key, score, matched in singles[:4]:
story.append(render_pep_block(pep_key, score, matched))
# ── Disclaimer ──
story.append(Spacer(1, 12))
story.append(HRFlowable(width="100%", thickness=1, color=C_BORDER, spaceAfter=8))
story.append(Paragraph(BRAND["disclaimer"], disc_s))
story.append(Spacer(1, 6))
# ── Footer ──
story.append(HRFlowable(width="100%", thickness=3, color=C_ORANGE, spaceAfter=0))
ft_brand_s = ps("Fb", fontName="Helvetica-Bold", fontSize=10, textColor=C_ORANGE, spaceAfter=1, alignment=TA_CENTER)
ft_info_s = ps("Fi", fontName="Helvetica", fontSize=8, leading=12, textColor=colors.white, spaceAfter=1, alignment=TA_CENTER)
ft_web_s = ps("Fw", fontName="Helvetica-Bold", fontSize=8, textColor=C_SKY, spaceAfter=0, alignment=TA_CENTER)
ft_tbl = Table([
[Paragraph("IGNITE PERFORMANCE & HEALTH", ft_brand_s)],
[Paragraph("14830 Clayton Rd, Chesterfield, MO 63017 | (314) 887-0858 | info@ignitepah.com", ft_info_s)],
[Paragraph("www.ignitepah.com", ft_web_s)],
], colWidths=[pw])
ft_tbl.setStyle(TableStyle([
("BACKGROUND", (0, 0), (-1, -1), C_NAVY),
("TOPPADDING", (0, 0), (0, 0), 8),
("BOTTOMPADDING", (0, -1), (0, -1), 8),
("TOPPADDING", (0, 1), (-1, -1), 1),
("BOTTOMPADDING", (0, 1), (-1, -1), 1),
("ALIGN", (0, 0), (-1, -1), "CENTER"),
]))
story.append(ft_tbl)
doc.build(story)
buffer.seek(0)
return buffer
# =============================================================================
# FOOTER RENDERER
# =============================================================================
def render_footer():
st.markdown(
'',
unsafe_allow_html=True,
)
# =============================================================================
# PAGE CONFIG & CSS
# =============================================================================
st.set_page_config(
page_title="Ignite Performance & Health — Peptide Protocol Selector",
layout="wide",
initial_sidebar_state="collapsed",
)
st.markdown(
''
f'{"
" if LOGO_B64 else ""}'
'
Peptide Protocol Selector
'
'
Your Personalized Peptide Recommendations
'
f'
Prepared for {pname}
'
'
Your Health Goals
', unsafe_allow_html=True)
goals_html = " · ".join(selected_goals)
st.markdown(f'{goals_html}
', unsafe_allow_html=True)
cats_html = " ".join(f'{c} ' for c in top_cats)
st.markdown(f'{cats_html}
', unsafe_allow_html=True)
# ── Group and display ──
branded, blends, singles = group_by_tier(results)
def render_card(pep_key, score, matched, is_branded=False):
pep = PEPTIDES[pep_key]
display_name = pep.get("brand_name") or pep_key
card_class = "pep-card-branded" if is_branded else "pep-card"
badge_class = "pep-badge-branded" if is_branded else "pep-badge"
# Only show badge on singles and non-branded blends
show_badge = not is_branded
badge_text = "BLEND" if pep["is_blend"] else "SINGLE"
components_str = " + ".join(pep["components"]) if pep["is_blend"] else ""
cat_pills = " ".join(f'{c} ' for c in matched)
sv = pep["suggested_vials"]
price_cells = ""
for vials in range(1, 7):
price = pep["pricing"][vials]
is_suggested = vials == sv
cell_class = "price-cell-suggested" if is_suggested else "price-cell"
suggested_label = '✓ Recommended
' if is_suggested else ""
per_week = price / max(vials * 4, 1)
price_cells += (
f''
f'
{vials} Vial{"s" if vials > 1 else ""}
'
f'
${per_week:,.2f}
'
f'
per week
'
f'
${price:,.2f} total
'
f'{suggested_label}'
f'
{badge_text}
' if show_badge else ""
st.markdown(
f''
f'{badge_html}'
f'
{display_name}
'
f'{"
" + components_str + "
" if components_str else ""}'
f'
{pep["description"]}
'
f'
How it works: {pep.get("moa_scott", "")}
'
f'
{pep.get("moa_clinical", "")}
'
f'
{cat_pills}
'
f'
'
f'
'
f'
Dosage
'
f'
{pep["dosage"]}
'
f'
'
f'
'
f'
Cycle
'
f'
{pep["cycle"]}
'
f'
'
f'
'
f'
Vial Amount: {pep["vial_amount"]} | Source: {pep["source"]}
'
f'
Pricing
'
f'
{price_cells}
'
f'
Ignite Branded Blends — Recommended
', unsafe_allow_html=True)
st.markdown(
''
'Curated multi-peptide protocols from our proprietary blend line. These combine complementary peptides '
'into a single vial for convenience and synergistic benefit.
',
unsafe_allow_html=True,
)
for pep_key, score, matched in branded[:6]:
render_card(pep_key, score, matched, is_branded=True)
if blends:
st.markdown('Combination Blends
', unsafe_allow_html=True)
for pep_key, score, matched in blends[:4]:
render_card(pep_key, score, matched, is_branded=False)
if singles:
st.markdown('Individual Peptides
', unsafe_allow_html=True)
st.markdown(
''
'Single-peptide options for targeted protocols or for patients who prefer to build their stack one component at a time.
',
unsafe_allow_html=True,
)
for pep_key, score, matched in singles[:4]:
render_card(pep_key, score, matched, is_branded=False)
# ── CTA ──
st.markdown(
f''
f'
Ready to Get Started, {pname}? '
f'
These recommendations are based on your stated goals. All peptide protocols require '
f'physician evaluation before we begin. Our team will walk through each option during your consultation, '
f'answer your questions, and build a protocol that fits your situation.
'
f'
What happens next: our team reaches out, we schedule your consultation, '
f'and we go from there.
'
f'
Contact: {pemail} | {pphone}
'
f'
Schedule Your Consultation '
f'
'
f'{"
" if LOGO_B64 else ""}'
'
Peptide Protocol Selector
'
'
Find the Right Peptides for Your Goals
'
'
Physician-Supervised · Evidence-Informed · Personalized
'
'
Your Information
', unsafe_allow_html=True)
c1, c2 = st.columns(2)
with c1:
first_name = st.text_input("First Name *")
email = st.text_input("Email Address *")
with c2:
last_name = st.text_input("Last Name *")
phone = st.text_input("Cell Phone *")
st.markdown('What Are You Looking to Address?
', unsafe_allow_html=True)
st.caption("Select all goals that apply. We will match peptides to your priorities.")
# Build flat goal list grouped by category
goal_checks = {}
cat_keys = list(CATEGORIES.keys())
mid = (len(cat_keys) + 1) // 2
c1, c2 = st.columns(2)
with c1:
for cat_name in cat_keys[:mid]:
cat = CATEGORIES[cat_name]
st.markdown(
f''
f'{cat_name}
',
unsafe_allow_html=True,
)
for goal in cat["goals"]:
goal_checks[goal] = st.checkbox(goal, key=f"g_{goal}")
with c2:
for cat_name in cat_keys[mid:]:
cat = CATEGORIES[cat_name]
st.markdown(
f''
f'{cat_name}
',
unsafe_allow_html=True,
)
for goal in cat["goals"]:
goal_checks[goal] = st.checkbox(goal, key=f"g_{goal}")
st.markdown('Additional Context
', unsafe_allow_html=True)
additional = st.text_area(
"Anything else we should know? (injuries, current medications, previous peptide experience, etc.)",
placeholder="Optional — helps our physician team customize your protocol.",
)
# ── Consent ──
st.markdown('Consent & Privacy
', unsafe_allow_html=True)
st.markdown(
''
'Important: This platform is '
'not HIPAA-compliant . Do not share sensitive medical records, '
'Social Security numbers, or information you would not disclose on a non-secure platform.
',
unsafe_allow_html=True,
)
consent = st.checkbox(
"I understand this is an informational tool, not a prescription. "
"All peptide protocols require physician evaluation and approval.",
key="consent",
)
submitted = st.form_submit_button("Find My Peptides", use_container_width=True)
# ── Processing ──
if submitted:
selected_goals = [g for g, checked in goal_checks.items() if checked]
errors = []
if not first_name: errors.append("First Name")
if not last_name: errors.append("Last Name")
if not email: errors.append("Email Address")
if not phone: errors.append("Cell Phone")
if not selected_goals: errors.append("At least one health goal")
if not consent: errors.append("Consent acknowledgment")
if errors:
st.error(f"Please complete: **{', '.join(errors)}**")
st.stop()
results = score_peptides(selected_goals)
top_cats = get_top_categories(selected_goals)
if not results:
st.warning("No peptides matched your selected goals. Please try different selections.")
st.stop()
st.session_state["pep_results"] = results
st.session_state["pep_goals"] = selected_goals
st.session_state["pep_top_cats"] = top_cats
st.session_state["pep_patient_name"] = f"{first_name} {last_name}"
st.session_state["pep_patient_email"] = email
st.session_state["pep_patient_phone"] = phone
st.rerun()
render_footer()