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Dockerfile

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+# Use a small Python base image
+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1     PYTHONUNBUFFERED=1     PIP_NO_CACHE_DIR=1     PORT=7860
+
+WORKDIR /app
+
+# System deps (optional): build-base for matplotlib backends if needed
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libglib2.0-0 libsm6 libxext6 libxrender1 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --upgrade pip && pip install -r requirements.txt
+
+COPY app.py /app/app.py
+
+EXPOSE 7860
+
+CMD streamlit run app.py --server.port $PORT --server.address 0.0.0.0

README.md

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+---
+title: Signal to Secretion
+emoji: 🧠
+colorFrom: indigo
+colorTo: indigo
+sdk: docker
+pinned: false
+license: mit
+---
+
+# Signal to Secretion (Streamlit • Docker Space)
+
+Interactive physiology mini-app:
+
+1. Choose a scenario.
+2. Build the **dominant autonomic pathway** (transmitter → receptor → PSP).
+3. Select **hormone type / circulation / receptor** for common hormones.
+4. Explore a **flow & pressure gradient simulator** for delivery.
+
+## How this Space runs
+This is a **Docker** Space with Streamlit. The container starts:
+
+```bash
+streamlit run app.py --server.port $PORT --server.address 0.0.0.0
+```
+
+No additional configuration is required.

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import streamlit as st
+
+st.set_page_config(page_title="Signal to Secretion", page_icon="🧠", layout="wide")
+
+# -------------------
+# Data: scenarios (dominant only; effectors have no parentheses)
+# -------------------
+SCENARIOS = {
+    "Predator threat (fight-or-flight)": {
+        "dominant": "Sympathetic",
+        "steps_labels": ("Pre-ganglionic", "Post-ganglionic"),
+        "effector": "Heart",
+        "function": "↑ Heart rate and contractility → ↑ Cardiac output",
+    },
+    "Post-meal digestion (rest-and-digest)": {
+        "dominant": "Parasympathetic",
+        "steps_labels": ("Pre-ganglionic", "Post-ganglionic"),
+        "effector": "GI tract smooth muscle & glands",
+        "function": "↑ Motility and secretions → enhanced digestion/absorption",
+    },
+    "Cold exposure (thermoregulatory response)": {
+        "dominant": "Sympathetic",
+        "steps_labels": ("Pre-ganglionic", "Post-ganglionic"),
+        "effector": "Cutaneous arterioles",
+        "function": "Vasoconstriction → conserve core heat",
+    },
+    "Hypotension (baroreflex — pressure drop)": {
+        "dominant": "Sympathetic",
+        "steps_labels": ("Pre-ganglionic", "Post-ganglionic"),
+        "effector": "Arterioles and Heart",
+        "function": "↑ Total peripheral resistance (± ↑ HR) → restore arterial pressure",
+    },
+    "Guided breathing / meditation (vagal tone)": {
+        "dominant": "Parasympathetic",
+        "steps_labels": ("Pre-ganglionic", "Post-ganglionic"),
+        "effector": "Heart",
+        "function": "↓ SA node rate and AV conduction → ↓HR, ↑HRV",
+    },
+}
+
+# Allowed options (per your constraints)
+TX_OPTIONS = ["— select —", "Acetylcholine (ACh)", "Norepinephrine/Epinephrine"]
+RC_OPTIONS = ["— select —", "Cholinergic (nicotinic)", "Cholinergic (muscarinic)", "Adrenergic"]
+PSP_OPTIONS = ["— select —", "EPSP", "IPSP"]
+
+HORMONES = ["Insulin", "Epinephrine", "Cortisol", "Aldosterone", "T3/T4"]
+HORMONE_CLASS = ["— select —", "amino acid/peptide", "steroid"]
+HORMONE_CIRC = ["— select —", "Free", "Bound to Transport Protein"]
+HORMONE_RECEPTOR = ["— select —", "membrane bound receptor", "intracellular receptor"]
+
+# Sidebar
+with st.sidebar:
+    st.header("1) Choose a Scenario")
+    scenario = st.selectbox("Scenario", list(SCENARIOS.keys()), index=0)
+    st.caption("Use the main panel to build the pathway and explore gradients.")
+
+st.title("Signal to Secretion: From Hypothalamus to Effector")
+
+# Section 2
+st.subheader("2) Neural Pathway (Dominant Branch Only)")
+st.write("Complete the transmitter → receptor → PSP for each step. Then review the effector and function card.")
+
+st.markdown("**Step** | **Transmitter** | **Receptor** | **PSP**")
+st.markdown("---")
+
+chosen = {}
+for idx, step_label in enumerate(SCENARIOS[scenario]["steps_labels"]):
+    c1, c2, c3, c4 = st.columns([1.3, 2.2, 2.4, 1.2])
+    with c1:
+        st.write(step_label)
+    with c2:
+        tx = st.selectbox(f"Transmitter_{idx}", TX_OPTIONS, key=f"tx_{scenario}_{idx}")
+    with c3:
+        rc = st.selectbox(f"Receptor_{idx}", RC_OPTIONS, key=f"rc_{scenario}_{idx}")
+    with c4:
+        psp = st.selectbox(f"PSP_{idx}", PSP_OPTIONS, key=f"psp_{scenario}_{idx}")
+    chosen[step_label] = {"tx": tx, "rc": rc, "psp": psp}
+
+st.info(f"**Effector:** {SCENARIOS[scenario]['effector']}\n\n**Function:** {SCENARIOS[scenario]['function']}")
+
+st.markdown("---")
+
+# Section 3
+st.subheader("3) Hormone Structure • Transport • Receptors")
+st.write("For each hormone, choose its type, circulation mode, and receptor location/type.")
+
+header_cols = st.columns([1.2, 1.6, 1.8, 1.8])
+header_cols[0].markdown("**Hormone**")
+header_cols[1].markdown("**Type**")
+header_cols[2].markdown("**Circulation**")
+header_cols[3].markdown("**Receptor**")
+
+for i, h in enumerate(HORMONES):
+    c1, c2, c3, c4 = st.columns([1.2, 1.6, 1.8, 1.8])
+    with c1:
+        st.write(h)
+    with c2:
+        st.selectbox(f"Type_{i}", HORMONE_CLASS, key=f"type_{h}")
+    with c3:
+        st.selectbox(f"Circulation_{i}", HORMONE_CIRC, key=f"circ_{h}")
+    with c4:
+        st.selectbox(f"Receptor_{i}", HORMONE_RECEPTOR, key=f"rec_{h}")
+
+st.markdown("---")
+
+# Section 4
+st.subheader("4) Flow Down Gradients (Delivery Simulator)")
+st.write("Adjust concentration and pressure to visualize delivery dynamics. Higher concentration and higher pressure speed delivery to tissues.")
+
+colA, colB = st.columns(2)
+with colA:
+    dC = st.slider("Δ[Hormone] (target - blood)", min_value=0.0, max_value=1.0, value=0.4, step=0.02)
+with colB:
+    P = st.slider("Circulatory pressure (relative)", min_value=0.1, max_value=2.0, value=1.0, step=0.05)
+
+G = max(dC, 1e-3)
+rate = P * G
+t = np.linspace(0, 10, 250)
+k = 0.35 * rate + 0.05
+delivered = 1 - np.exp(-k * t)
+
+fig, ax = plt.subplots()
+ax.plot(t, delivered, label="Fraction delivered to tissue")
+ax.set_xlabel("Time")
+ax.set_ylabel("Delivered (0–1)")
+ax.set_title("Hormone Delivery vs. Gradients")
+ax.legend(loc="best")
+st.pyplot(fig, clear_figure=True)
+
+st.caption("Delivery rises faster with larger Δ[Hormone] and higher circulatory pressure.")

requirements.txt

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+streamlit==1.38.0
+numpy==1.26.4
+matplotlib==3.8.4