app.py

import streamlit as st
import random
import numpy as np
import matplotlib.pyplot as plt
from typing import List, Dict, Tuple, Set
import io, datetime

st.set_page_config(page_title="Pharmacodynamics Review", page_icon="💊", layout="wide")

# ==============================================
# Session state
# ==============================================
if "unlock" not in st.session_state:
    st.session_state.unlock = {"effects": False, "combos": False}
if "student_name" not in st.session_state:
    st.session_state.student_name = ""
if "combo_answers" not in st.session_state:
    st.session_state.combo_answers = {}

# ==============================================
# DATA — DEA schedules (federal) + Non-scheduled
# ==============================================
DEA_SCHEDULES: Dict[str, str] = {
    # Schedule I
    "Heroin": "I",
    "LSD (acid)": "I",
    "MDMA (Ecstasy/Molly)": "I",
    "Psilocybin (mushrooms)": "I",
    "Cannabis/Marijuana (federal status)": "I",

    # Schedule II
    "Cocaine": "II",
    "Methamphetamine": "II",
    "Fentanyl": "II",
    "Oxycodone": "II",
    "Amphetamine (Adderall)": "II",

    # Schedule III
    "Ketamine": "III",
    "Anabolic steroids": "III",

    # Schedule IV
    "Alprazolam (Xanax)": "IV",
    "Diazepam (Valium)": "IV",

    # Non-scheduled (included for comparison in this exercise)
    "Alcohol (ethanol)": "Non-scheduled",
    "Nicotine/Tobacco": "Non-scheduled",
    "Kratom": "Non-scheduled",
    "Cannabidiol (CBD)": "Non-scheduled",
}

SCHEDULE_ORDER = ["I", "II", "III", "IV", "V", "Non-scheduled"]

# Randomize drug order once per session
if "drug_order" not in st.session_state:
    st.session_state.drug_order = random.sample(list(DEA_SCHEDULES.keys()), k=len(DEA_SCHEDULES))

# ==============================================
# Acute effects – categories (updated naming)
# ==============================================
NIDA_CATEGORIES: List[str] = [
    "Alcohol",
    "Cannabinoids",
    "Opioids",
    "Stimulants",
    "Club Drugs",            # was Depressants (benzos, GHB)
    "Dissociative Drugs",
    "Hallucinogens",
    "Tobacco/Nicotine",
]

# Rules per effect:
# - required: must be selected
# - neutral: selection does not matter (either included or not)
# Validation: valid iff (required ⊆ chosen) and (chosen ∩ forbidden == ∅)
ALL_CATS: Set[str] = set(NIDA_CATEGORIES)
EFFECT_RULES: Dict[str, Dict[str, Set[str]]] = {
    # 1) Euphoria — Alcohol, Cannabinoids, Opioids required; Dissociative Drugs neutral
    "Euphoria": {
        "required": {"Alcohol", "Cannabinoids", "Opioids"},
        "neutral": {"Dissociative Drugs"},
    },
    # 2) Loss of/Impaired Coordination — Alcohol, Cannabinoids, Opioids required; Club & Dissociative neutral
    "Loss of/Impaired Coordination": {
        "required": {"Alcohol", "Cannabinoids", "Opioids"},
        "neutral": {"Club Drugs", "Dissociative Drugs"},
    },
    # 3) Anxiety/panic — Stimulants & Cannabinoids required; Hallucinogens neutral
    "Anxiety/panic": {
        "required": {"Stimulants", "Cannabinoids"},
        "neutral": {"Hallucinogens"},
    },
}

# ==============================================
# Combination gallery – use only additive/synergistic/antagonistic examples
# (Removed the mixed example to allow clear grading/completion.)
# ==============================================
COMBOS = [
    {"name": "Alcohol + Benzodiazepine", "class": "Synergistic"},
    {"name": "Opioid (e.g., heroin) + Benzodiazepine", "class": "Synergistic"},
    {"name": "Alcohol + Cannabis", "class": "Additive"},
    {"name": "Naloxone + Opioid", "class": "Antagonistic"},
]

# ==============================================
# Helpers
# ==============================================

def sigmoid(x, mid=30, steep=0.12, top=1.0):
    return top / (1 + np.exp(-steep * (x - mid)))


def header() -> None:
    st.title("💊 Pharmacodynamics Review (Foundations)")
    st.markdown(
        """
        Goals today
        1) DEA scheduling basics (plus *non-scheduled* examples)
        2) Recognize acute effects and legitimate overlap across categories
        3) Identify drug-combo interactions by inspecting a combined effect graph

        Designed for asynchronous learning; illustrative only.
        """
    )
    name = st.text_input("Insert First and Last Name *", value=st.session_state.get("student_name", "")).strip()
    st.session_state.student_name = name
    if not name:
        st.info("Please enter your first and last name before proceeding to the next section.")


# ==============================================
# 1) DEA Schedule Sorting (randomized, strict gating)
# ==============================================

def schedule_sorter():
    st.subheader("1) DEA Schedule Sorting Challenge")
    st.caption("Assign each drug to a schedule. All items must be correct to unlock the next section.")

    assignments: Dict[str, str] = {}
    all_answered = True

    for d in st.session_state.drug_order:
        st.markdown(f"**{d}**")
        sel = st.selectbox(
            "Select schedule",
            options=["— Select —"] + SCHEDULE_ORDER,
            index=0,
            key=f"sched_{d}"
        )
        assignments[d] = sel
        if sel == "— Select —":
            all_answered = False
        st.divider()

    if st.button("Check my answers", type="primary"):
        if not st.session_state.student_name:
            st.warning("Please enter your **first and last name** above before checking.")
            return
        if not all_answered:
            st.warning("Please assign **every drug** before checking.")
            return
        correct = sum(1 for d in DEA_SCHEDULES if assignments.get(d) == DEA_SCHEDULES[d])
        total = len(DEA_SCHEDULES)
        if correct == total:
            st.success("All correct! You've unlocked Acute Effects.")
            st.session_state.unlock["effects"] = True
        else:
            st.error(f"{total - correct} incorrect. Review and try again.")


# ==============================================
# 2) Acute Effects (updated list & rules; strict gating)
# ==============================================

def is_effect_selection_correct(effect: str, chosen: Set[str]) -> bool:
    rules = EFFECT_RULES[effect]
    required = rules["required"]
    neutral = rules["neutral"]
    forbidden = ALL_CATS - required - neutral
    return required.issubset(chosen) and chosen.isdisjoint(forbidden)


def effects_overlap_quiz():
    st.subheader("2) Acute Effects – Overlap Recognition")
    st.caption("Select all categories that apply for each effect. You must get every effect fully correct to proceed.")

    user_choices: Dict[str, List[str]] = {}
    for effect in EFFECT_RULES.keys():
        st.markdown(f"**{effect}**")
        user_choices[effect] = st.multiselect(
            "Select categories:", options=NIDA_CATEGORIES, key=f"eff_{effect}")
        st.divider()

    if st.button("Check effects", type="primary"):
        wrong_effects = []
        for effect in EFFECT_RULES.keys():
            chosen = set(user_choices.get(effect, []))
            if not is_effect_selection_correct(effect, chosen):
                wrong_effects.append(effect)
        if not wrong_effects:
            st.success("All effects answered correctly! You've unlocked Drug Combination Effects.")
            st.session_state.unlock["combos"] = True
        else:
            st.error("Some effects need review. The following are not correct:")
            for eff in wrong_effects:
                st.write(f"- {eff}")


# ==============================================
# 3) Drug Combination Effects – Bar graphs (fixed dose, improved synergy)
# ==============================================

def combo_base_values(choice: str) -> Tuple[float, float]:
    """Return (A_val, B_val) at a fixed mid-range dose chosen to avoid saturation."""
    x = np.linspace(0, 100, 300)
    dose_fixed = 55  # remove slider per request; mid-range to avoid saturation

    # Use lower tops to keep sums below 1.0 so synergy is visible without clipping
    seed_map = {"Alcohol + Benzodiazepine": 1,
                "Opioid (e.g., heroin) + Benzodiazepine": 2,
                "Alcohol + Cannabis": 3,
                "Naloxone + Opioid": 4}
    rng = np.random.RandomState(seed_map.get(choice, 0))
    midA = 25 + rng.randint(-4, 5)
    midB = 30 + rng.randint(-4, 5)
    steepA = 0.14 + rng.rand() * 0.04
    steepB = 0.12 + rng.rand() * 0.04

    # Tops < 1 to avoid immediate ceiling
    topA = 0.72
    topB = 0.72

    A = sigmoid(x, midA, steepA, topA)
    B = sigmoid(x, midB, steepB, topB)

    # sample at dose_fixed
    idx = np.searchsorted(x, dose_fixed)
    def interp(series):
        if idx <= 0:
            return float(series[0])
        if idx >= len(series):
            return float(series[-1])
        x0, x1 = x[idx-1], x[idx]
        y0, y1 = series[idx-1], series[idx]
        t = (dose_fixed - x0) / (x1 - x0)
        return float(y0 + t * (y1 - y0))

    return interp(A), interp(B)


def combo_gallery():
    st.subheader("3) Drug Combination Effects – What interaction is this?")
    st.caption("We show Drug A, Drug B, and a combined effect as **bar graphs** at a fixed dose. Classify: additive, synergistic, or antagonistic.")

    # Fixed values provided by instructor (normalized 0–1)
    # Note: Interpreted 9.3 as 0.93 to keep within normalized display.
    FIXED_VALUES = {
        "Alcohol + Benzodiazepine": {"A": 0.20, "B": 0.20, "Combined": 0.90, "label": "Synergistic"},
        "Opioid (e.g., heroin) + Benzodiazepine": {"A": 0.10, "B": 0.15, "Combined": 0.93, "label": "Synergistic"},
        "Alcohol + Cannabis": {"A": 0.30, "B": 0.30, "Combined": 0.60, "label": "Additive"},
        "Naloxone + Opioid": {"A": 0.20, "B": 0.60, "Combined": 0.15, "label": "Antagonistic"},
    }

    choice = st.selectbox("Pick a combination:", list(FIXED_VALUES.keys()), key="combo_choice")

    # Labels for bars
    if " + " in choice:
        a_label, b_label = choice.split(" + ", 1)
    else:
        a_label, b_label = "Drug A", "Drug B"

    vals = FIXED_VALUES[choice]
    a_val, b_val, combo_val = vals["A"], vals["B"], vals["Combined"]

    # Plot bars
    fig, ax = plt.subplots(figsize=(6, 4))
    bars = [a_label, b_label, "Combined"]
    ax.bar(bars, [a_val, b_val, combo_val])
    ax.set_ylim(0, 1.0)
    ax.set_ylabel("Effect (normalized)")
    ax.set_title(f"{choice} @ fixed dose")
    st.pyplot(fig)

    guess = st.radio("This combined effect is…", ["Additive", "Synergistic", "Antagonistic"], index=None, key=f"combo_guess_{choice}")

    if st.button("Check combo", type="primary"):
        if guess is None:
            st.warning("Choose one option above.")
        else:
            # record correctness without revealing the answer
            is_correct = (guess == vals["label"]) 
            st.session_state.combo_answers[choice] = is_correct
            if is_correct:
                st.success("Correct")
            else:
                st.error("Incorrect selection")

    # If all combos have been answered correctly, offer PDF submission
    all_correct = all(st.session_state.combo_answers.get(c["name"], False) for c in COMBOS)
    if all_correct and st.session_state.student_name:
        st.divider()
        st.markdown("### Submit")
        st.caption("Download and upload this completion PDF to Canvas.")

        def build_pdf_bytes(name: str) -> bytes:
            """Create a completion PDF. Falls back to a text file if reportlab is unavailable."""
            try:
                from reportlab.lib.pagesizes import letter
                from reportlab.pdfgen import canvas
            except Exception:
                # Fallback: plain text bytes (safe single-line joins)
                timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                lines = [
                    "Pharmacodynamics Review - Completion Certificate",
                    f"Student: {name}",
                    f"Date: {timestamp}",
                    "Sections completed: DEA Schedules, Acute Effects, Drug Combos",
                ]
                txt = "\n".join(lines) + "\n"
                return txt.encode("utf-8")

            buffer = io.BytesIO()
            c = canvas.Canvas(buffer, pagesize=letter)
            w, h = letter
            c.setFont("Helvetica-Bold", 18)
            c.drawString(72, h - 72, "Pharmacodynamics Review - Completion Certificate")
            c.setFont("Helvetica", 12)
            c.drawString(72, h - 110, f"Student: {name}")
            c.drawString(72, h - 130, f"Date: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
            c.drawString(72, h - 150, "Sections completed: DEA Schedules, Acute Effects, Drug Combos")
            c.showPage()
            c.save()
            buffer.seek(0)
            return buffer.getvalue()

        pdf_bytes = build_pdf_bytes(st.session_state.student_name)
        st.download_button(
            label="Submit (Download PDF)",
            data=pdf_bytes,
            file_name="pharmacodynamics_completion.pdf",
            mime="application/pdf",
            type="primary",
        )

# ==============================================
# Sidebar progress
# ==============================================

def sidebar_progress():
    with st.sidebar:
        st.header("Module Progress")
        st.write(f"Name provided: {'✅' if st.session_state.student_name else '❌'}")
        st.write(f"Acute Effects unlocked: {'✅' if st.session_state.unlock['effects'] else '❌'}")
        # Count correctly answered combos
        correct_count = sum(1 for c in COMBOS if st.session_state.combo_answers.get(c['name'], False))
        st.write(f"Combos correct: {correct_count}/{len(COMBOS)}")
        st.caption("Educational demo. Federal scheduling shown; state laws vary.")


# ==============================================
# Layout (gated tabs)
# ==============================================
header()
sidebar_progress()

TAB1, TAB2, TAB3 = st.tabs(["DEA Schedules", "Acute Effects", "Drug Combos"]) 

with TAB1:
    schedule_sorter()
with TAB2:
    if st.session_state.unlock["effects"]:
        effects_overlap_quiz()
    else:
        if st.session_state.student_name:
            st.info("🔒 Complete the DEA Schedule task correctly to unlock this section.")
        else:
            st.info("🔒 Enter your name and complete the DEA Schedule task to unlock this section.")
with TAB3:
    if st.session_state.unlock["combos"]:
        combo_gallery()
    else:
        st.info("🔒 Complete the Acute Effects section correctly to unlock this section.")

st.divider()
st.caption("© 2025 – Teaching tool for foundational pharmacodynamics. For education only.")