Deploy Forager's Field Station

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
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-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/chanterelle.jpg filter=lfs diff=lfs merge=lfs -text
+examples/lions_mane.jpg filter=lfs diff=lfs merge=lfs -text
+examples/poison_hemlock.jpg filter=lfs diff=lfs merge=lfs -text
+examples/wild_blueberry.jpg filter=lfs diff=lfs merge=lfs -text
+examples/yarrow.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/
+*.pyc
+.venv/
+.gradio/
+.DS_Store
+flagged/
+# ONNX weights are large and live in forager_ml (source of truth); the HF Space
+# gets them via deploy.py uploading from disk. Re-sync locally with
+# scripts/sync_from_forager_ml.sh. Not tracked in this dev git repo.
+models/*.onnx

README.md

@@ -1,15 +1,68 @@
 ---
-title: Forager Field Notes
-emoji: 🏆
-colorFrom: gray
-colorTo: yellow
+title: Forager's Field Station
+emoji: 🍄
+colorFrom: yellow
+colorTo: green
 sdk: gradio
 sdk_version: 6.16.0
-python_version: '3.13'
 app_file: app.py
 pinned: false
 license: apache-2.0
 short_description: Pocket-sized intelligence for identifying edible wild foods
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Forager's Field Station
+
+Photograph a wild plant or mushroom and the model identifies it — **or refuses
+when it isn't sure.** A domain router plus three `tf_efficientnet_lite2`
+classifiers (~9M params each), ~0.04B parameters total. The same stack runs
+offline on a Hailo 8L NPU in a handheld field device; this Space is its CPU twin.
+
+Built for the **Build Small Hackathon** — Backyard AI track. The honest fit:
+a forager in the woods has no signal, so a small on-device model isn't a
+compromise, it's the only thing that works.
+
+## How it works
+
+```
+photo ─► domain router (berry / mushroom / plant / other)
+            │  conf < 0.74  or  "other"  ─► ABSTAIN
+            ▼
+        ONE expert owns each domain (no cross-expert voting):
+            berry ─► berry_expert   mushroom ─► highvalue_expert
+            plant ─► medicinals_expert
+            │  below confidence gate  ─► ABSTAIN
+            ▼
+        SAFE / CAUTION / DEADLY  + scientific name, lookalike, key difference
+```
+
+Single-expert routing is a safety choice: an off-domain expert never gets to
+misclassify an input it doesn't own (e.g. the mushroom expert never sees a
+plant, so it can't call a poison hemlock "ramps"). The deadly plants live in the
+medicinals expert, which scored 0% toxic-as-edible on held-out validation.
+
+The system is built to **refuse by default.** Across real-world test photos it
+abstained rather than guess on the cases it couldn't handle, and never labelled
+a deadly specimen as edible.
+
+## Models
+
+| Model | Domain | Classes |
+|---|---|---|
+| `domain_router_v2` | berry / mushroom / plant / other | 4 |
+| `berry_expert` | wild berries + toxic lookalikes | 11 |
+| `highvalue_expert` | chanterelles, morels, lion's mane, ginseng… | 11 |
+| `medicinals_expert` | wild medicinal plants + toxic lookalikes | 21 |
+
+Trained on iNaturalist research-grade observations. Apache-2.0.
+
+## Safety notice
+
+**Identification aid only — never an authority.** Wild plant and mushroom
+identification carries fatal risk. No output should be acted on — including any
+consumption decision — without independent verification by a qualified expert.
+Amatoxin poisoning (Amanita, Galerina, Conocybe) is lethal with no reliable
+field antidote. The maintainers accept no liability for decisions made from
+model output.
+
+— [HomesteaderLabs](https://homesteaderlabs.com)

app.py

@@ -1,7 +1,315 @@
+"""
+app.py — Forager's Field Station (Gradio Space).
+
+Photograph a wild plant or mushroom; the model identifies it — or refuses when
+it isn't sure. A domain router + three tf_efficientnet_lite2 experts (~9M params
+each), the same stack that runs offline on a Hailo NPU in a handheld field
+device. This Space is styled as that device's copper-and-bronze e-ink readout.
+
+Built for the Build Small Hackathon. Safety-first: this is an identification
+aid, never an authority — SAFE is never presented as permission to eat.
+"""
+
+import os
+import time
+
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+from pipeline.convergence import build_result
+from pipeline.infer import Pipeline
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+EXAMPLES_DIR = os.path.join(HERE, "examples")
+PIPE = Pipeline()
+
+# Safety tier -> (badge label, accent, glyph). These greens/ambers/reds are the
+# SAFETY channel and are kept distinct from the copper/bronze brand chrome so the
+# tier reads instantly. SAFE is never a green light: the label says "verify" and
+# _card always appends a confirm-with-an-expert line.
+INK_SAFE, INK_CAUTION, INK_DEADLY, INK_UNK = "#2f6b2b", "#87671c", "#8c1d14", "#57544c"
+TIER = {
+    "SAFE":    ("EDIBLE · VERIFY",     INK_SAFE,    "✓"),
+    "CAUTION": ("CAUTION",             INK_CAUTION, "▲"),
+    "DEADLY":  ("DEADLY · DO NOT EAT", INK_DEADLY,  "✕"),
+    "UNKNOWN": ("UNKNOWN",             INK_UNK,     "?"),
+}
+DOMAIN_LABEL = {"berry": "Berry", "mushroom": "Mushroom", "plant": "Plant", "other": "Other"}
+
+DARWIN = ("It is not the strongest of the species that survive, nor the most "
+          "intelligent, but the one most responsive to change.")
+
+
+def _pretty(species: str) -> str:
+    return species.replace("_toxic", "").replace("_deadly", "").replace("_", " ").title()
+
+
+def _idle(msg: str = "FIELD STATION READY") -> str:
+    return (
+        f"<div class='rdt-idle'><div class='rdt-idle-glyph'>⌖</div>"
+        f"<div class='rdt-idle-msg'>{msg}</div>"
+        f"<div class='rdt-idle-sub'>upload, capture, or pick a sample below to scan</div></div>"
+    )
+
+
+def _loading() -> str:
+    """Vine-growing animation shown while inference runs (no quote — too brief to read)."""
+    return """
+    <div class='loading'>
+      <svg class='vine' viewBox='0 0 120 220' xmlns='http://www.w3.org/2000/svg'>
+        <path class='stem' d='M60 212 C 40 190, 80 174, 60 152 C 42 132, 82 114, 60 94
+                              C 42 76, 80 58, 60 36 C 50 24, 64 16, 60 8'/>
+        <g transform='translate(44,150) rotate(-38)'><ellipse class='leaf' style='animation-delay:.35s' rx='10' ry='4.3'/></g>
+        <g transform='translate(78,114) rotate(34)'><ellipse class='leaf' style='animation-delay:.55s' rx='10' ry='4.3'/></g>
+        <g transform='translate(42,76) rotate(-34)'><ellipse class='leaf' style='animation-delay:.75s' rx='9' ry='4'/></g>
+        <g transform='translate(78,46) rotate(36)'><ellipse class='leaf' style='animation-delay:.95s' rx='8' ry='3.6'/></g>
+        <circle class='bud' cx='60' cy='8' r='4'/>
+      </svg>
+      <div class='scan-label'>ANALYZING SPECIMEN</div>
+    </div>
+    """
+
+
+def _card(r) -> str:
+    label, color, glyph = TIER.get(r.safety, TIER["UNKNOWN"])
+
+    if r.abstained:
+        color, label, glyph = INK_UNK, "UNKNOWN", "?"
+        title, sub = "UNKNOWN", "not confident enough — refusing by default"
+        rows = [
+            f"<div class='rdt-row'>{r.key_diff}</div>",
+            f"<div class='rdt-meta'>ROUTER · {DOMAIN_LABEL.get(r.domain, r.domain)} "
+            f"@ {r.confidence*100:.0f}%</div>",
+        ]
+    else:
+        title, sub = _pretty(r.species), "most likely — not confirmed"
+        rows = [
+            f"<div class='rdt-row'><i>{r.scientific_name}</i></div>",
+            f"<div class='rdt-row'>confidence <b>{r.confidence*100:.1f}%</b></div>",
+        ]
+        if r.lookalike and r.lookalike != "N/A":
+            rows.append(
+                f"<div class='rdt-look'>⚠ deadly look-alike: <b>{r.lookalike}</b><br>"
+                f"<span class='rdt-diff'>{r.key_diff}</span></div>"
+            )
+        elif r.key_diff:
+            rows.append(f"<div class='rdt-row rdt-diff'>{r.key_diff}</div>")
+        prefix = "DO NOT EAT. " if r.safety == "DEADLY" else ""
+        rows.append(
+            f"<div class='rdt-confirm'>⚠ {prefix}Confirm with an expert before eating — "
+            f"identification aid, not an authority.</div>"
+        )
+
+    body = "".join(rows)
+    return (
+        f"<div class='rdt' style='--accent:{color}'>"
+        f"  <div class='rdt-top'><span class='rdt-tag'>▌ FIELD READOUT</span>"
+        f"      <span class='rdt-badge'>{glyph} {label}</span></div>"
+        f"  <div class='rdt-title'>{title}</div>"
+        f"  <div class='rdt-sub'>{sub}</div>"
+        f"  <div class='rdt-body'>{body}</div>"
+        f"</div>"
+    )
+
+
+def identify(image):
+    """Generator: show the vine animation, then the readout."""
+    if image is None:
+        yield _idle()
+        return
+    t0 = time.time()
+    yield _loading()
+    result = build_result(PIPE.identify(image))
+    elapsed = time.time() - t0
+    if elapsed < 1.6:                       # let the vine finish drawing
+        time.sleep(1.6 - elapsed)
+    yield _card(result)
+
+
+EXPERTS_PANEL = """
+<div id='experts'>
+  <div class='ex-head'>● EXPERTS ONLINE — 4 MODELS</div>
+  <div class='ex-grid'>
+    <details class='ex'><summary>DOMAIN ROUTER</summary>
+      <div class='ex-body'>routes to berry · mushroom · plant · other — or abstains when unsure</div></details>
+    <details class='ex'><summary>BERRY EXPERT</summary>
+      <div class='ex-body'>11 species · wild berries + toxic look-alikes</div></details>
+    <details class='ex'><summary>HIGH-VALUE EXPERT</summary>
+      <div class='ex-body'>11 species · chanterelle · morel · lion's mane · ginseng</div></details>
+    <details class='ex'><summary>MEDICINALS EXPERT</summary>
+      <div class='ex-body'>21 species · wild medicinals + deadly look-alikes</div></details>
+  </div>
+</div>
+"""
+
+QUOTE_BAR = (
+    f"<div id='quotebar'><span class='q'>&ldquo;{DARWIN}&rdquo;</span>"
+    f"<span class='q-by'>— Charles Darwin</span></div>"
+)
+
+SAFETY_NOTICE = (
+    "**Identification aid only — never an authority.** Wild plant and mushroom "
+    "ID carries fatal risk. Do not consume anything based on this output without "
+    "independent verification by a qualified expert. The system refuses by default "
+    "when unsure. Amatoxin poisoning is lethal with no reliable field antidote."
+)
+
+CSS = """
+@import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600;700&family=Caveat:wght@600;700&display=swap');
+
+:root {
+  --paper:#e6e4dd; --panel:#E3DBCA; --ink:#1b1a17; --ink2:#57544c;  /* box panels */
+  --bronze:#7a3f1a; --copper:#b87333;                               /* HomesteaderLabs chrome */
+}
+.gradio-container, .gradio-container * {
+  font-family:'IBM Plex Mono','Courier New',monospace !important;
+}
+.gradio-container {
+  background:var(--paper) !important; color:var(--ink) !important;
+  background-image:repeating-linear-gradient(0deg,
+    rgba(27,26,23,.035) 0 1px, transparent 1px 3px) !important;   /* e-ink scanlines */
+  max-width:940px !important; margin:0 auto !important;
+}
+footer { display:none !important; }
+
+/* strip Gradio's default block chrome + row gutters so every panel shares one
+   width and the columns line up flush with the masthead/experts boxes */
+.gradio-container .block { background:transparent !important; border:none !important;
+  box-shadow:none !important; padding:0 !important; box-sizing:border-box !important; }
+.gradio-container .row { margin:0 !important; }
+.gradio-container .html-container { padding:0 !important; }
+.eink-input .image-container, .eink-input .image-frame, .eink-input .wrap,
+.eink-input .upload-container, .eink-input .empty, .eink-input [data-testid='image'] {
+  background:var(--panel) !important; }
+/* Gradio's themed labels/upload text were light (built for dark blocks) and turn
+   invisible on the eggshell panels — force them to ink. Leaves the readout card's
+   tier colors, the bronze SCAN button, and the DISPLAY tab untouched. */
+.eink-input, .eink-input label, .eink-input .label-wrap, .eink-input span,
+.eink-input p, .eink-input button:not(.eink-scan) { color:var(--ink) !important; }
+.eink-screen { color:var(--ink); }
+
+/* ── masthead ───────────────────────────────────────────────── */
+#masthead { border:3px solid var(--bronze); background:var(--panel);
+  padding:14px 18px; margin-bottom:12px; box-shadow:7px 7px 0 rgba(122,63,26,.55); }
+#masthead .brand { font-size:.7rem; letter-spacing:.34em; color:var(--bronze); font-weight:700; }
+#masthead .title { font-size:1.7rem; font-weight:700; letter-spacing:.04em; line-height:1.05;
+  margin-top:2px; color:var(--copper); }
+#masthead .tag { font-family:'Caveat',cursive !important; font-size:1.4rem; color:var(--bronze);
+  margin-top:0; transform:rotate(-1.2deg); display:inline-block; }
+#masthead .strip { margin-top:10px; padding-top:8px; border-top:2px dashed var(--bronze);
+  font-size:.66rem; letter-spacing:.16em; color:var(--ink2); display:flex; justify-content:space-between; }
+
+/* ── experts panel (collapsible) ────────────────────────────── */
+#experts { border:2px solid var(--bronze); background:var(--panel); padding:10px 14px; margin-bottom:12px;
+  box-shadow:5px 5px 0 rgba(122,63,26,.4); }
+#experts .ex-head { font-size:.68rem; letter-spacing:.22em; color:var(--copper); font-weight:700; margin-bottom:8px; }
+#experts .ex-grid { display:grid; grid-template-columns:repeat(auto-fit,minmax(230px,1fr)); gap:6px 18px; }
+#experts details.ex { border-left:4px solid var(--copper); padding:2px 0 2px 9px; }
+#experts summary { cursor:pointer; list-style:none; font-size:.76rem; letter-spacing:.08em;
+  color:var(--ink); font-weight:700; display:flex; align-items:center; gap:7px; }
+#experts summary::-webkit-details-marker { display:none; }
+#experts summary::before { content:'▸'; color:var(--copper); font-size:.7rem; }
+#experts details[open] summary::before { content:'▾'; }
+#experts .ex-body { font-size:.7rem; color:var(--ink2); padding:4px 0 2px 14px; line-height:1.4; }
+
+/* ── controls ───────────────────────────────────────────────── */
+.eink-input, .eink-screen { border:3px solid var(--bronze) !important; background:var(--panel) !important;
+  box-shadow:7px 7px 0 rgba(122,63,26,.55) !important; border-radius:0 !important; }
+.eink-input { padding:6px !important; }
+button.eink-scan { background:var(--bronze) !important; color:var(--paper) !important;
+  border:3px solid var(--bronze) !important; border-radius:0 !important; font-weight:700 !important;
+  letter-spacing:.18em !important; box-shadow:5px 5px 0 rgba(122,63,26,.4) !important; }
+button.eink-scan:hover { background:var(--copper) !important; border-color:var(--copper) !important; }
+
+.eink-screen { padding:0 !important; position:relative; min-height:330px; }
+.eink-screen::before { content:'▌ DISPLAY'; position:absolute; top:-3px; left:-3px;
+  background:var(--bronze); color:var(--paper); font-size:.6rem; letter-spacing:.2em;
+  padding:3px 8px; z-index:2; }
+
+/* ── readout card ───────────────────────────────────────────── */
+.rdt { background:var(--panel); border-left:10px solid var(--accent);
+  padding:34px 18px 18px; min-height:330px; }
+.rdt-top { display:flex; justify-content:space-between; align-items:center;
+  border-bottom:2px solid var(--ink); padding-bottom:7px; margin-bottom:12px;
+  font-size:.7rem; letter-spacing:.14em; }
+.rdt-tag { color:var(--ink2); font-weight:600; }
+.rdt-badge { color:var(--accent); font-weight:700; }
+.rdt-title { font-size:1.55rem; font-weight:700; line-height:1.15; color:var(--ink); }
+.rdt-sub { font-size:.7rem; letter-spacing:.1em; color:var(--ink2); margin-top:3px; text-transform:uppercase; }
+.rdt-body { margin-top:14px; }
+.rdt-row { line-height:1.6; font-size:.95rem; }
+.rdt-row i { color:var(--ink2); }
+.rdt-meta { margin-top:8px; font-size:.72rem; letter-spacing:.1em; color:var(--ink2); }
+.rdt-diff { color:var(--ink2); font-size:.86rem; }
+.rdt-look { margin-top:12px; padding:10px 12px; border:2px solid #8c1d14;
+  background:rgba(140,29,20,.06); color:#8c1d14; font-size:.9rem; line-height:1.5; }
+.rdt-confirm { display:block; margin-top:14px; padding-top:10px; border-top:2px dashed var(--ink);
+  color:var(--accent); font-weight:700; font-size:.9rem; line-height:1.5; }
+
+/* idle / standby */
+.rdt-idle { min-height:330px; display:flex; flex-direction:column; align-items:center;
+  justify-content:center; text-align:center; color:var(--ink2); padding:24px; }
+.rdt-idle-glyph { font-size:3rem; opacity:.5; color:var(--bronze); }
+.rdt-idle-msg { margin-top:10px; font-size:1rem; font-weight:700; letter-spacing:.2em; }
+.rdt-idle-sub { margin-top:6px; font-size:.74rem; letter-spacing:.08em; opacity:.8; }
+
+/* ── loading: vine only ─────────────────────────────────────── */
+.loading { min-height:330px; display:flex; flex-direction:column; align-items:center; justify-content:center; }
+.vine { width:92px; height:170px; }
+.vine .stem { fill:none; stroke:var(--bronze); stroke-width:3.6; stroke-linecap:round;
+  stroke-dasharray:320; stroke-dashoffset:320; animation:grow 1.35s ease-out forwards; }
+.vine .leaf { fill:var(--copper); opacity:0; transform-box:fill-box; transform-origin:center;
+  animation:leafin .5s ease-out forwards; }
+.vine .bud { fill:var(--copper); opacity:0; transform-box:fill-box; transform-origin:center;
+  animation:leafin .5s ease-out 1.1s forwards; }
+@keyframes grow { to { stroke-dashoffset:0; } }
+@keyframes leafin { from { opacity:0; transform:scale(0); } to { opacity:1; transform:scale(1); } }
+@keyframes fadein { from { opacity:0; } to { opacity:1; } }
+.scan-label { margin-top:12px; font-size:.72rem; letter-spacing:.24em; color:var(--copper);
+  font-weight:700; opacity:0; animation:fadein .6s ease-out .2s forwards; }
+
+/* ── Darwin quote bar (static, readable) ────────────────────── */
+#quotebar { text-align:center; margin-top:16px; padding:14px 12px 4px; border-top:2px dashed var(--bronze); }
+#quotebar .q { font-family:'Caveat',cursive !important; font-size:1.5rem; color:var(--bronze);
+  line-height:1.3; display:block; max-width:620px; margin:0 auto; }
+#quotebar .q-by { font-size:.64rem; letter-spacing:.2em; color:var(--ink2); }
+
+/* safety footer */
+#notice { border:2px dashed var(--bronze) !important; background:transparent !important;
+  padding:10px 14px; margin-top:10px; font-size:.74rem !important; line-height:1.55 !important;
+  color:var(--ink2) !important; }
+#notice * { font-size:.74rem !important; color:var(--ink2) !important; }
+"""
+
+with gr.Blocks(title="Forager's Field Station") as demo:
+    gr.HTML(
+        "<div id='masthead'>"
+        "  <div class='brand'>HOMESTEADER LABS</div>"
+        "  <div class='title'>FORAGER'S FIELD STATION</div>"
+        "  <div class='tag'>Backyard AI · real-world stakes</div>"
+        "  <div class='strip'><span>ROUTER + 3 EXPERTS · ~0.04B PARAMS</span>"
+        "      <span>REFUSES BY DEFAULT</span></div>"
+        "</div>"
+    )
+    gr.HTML(EXPERTS_PANEL)
+    with gr.Row():
+        with gr.Column(scale=1):
+            img = gr.Image(type="pil", label="SPECIMEN", sources=["upload", "webcam"],
+                           elem_classes="eink-input", height=300)
+            btn = gr.Button("▸ SCAN SPECIMEN", variant="primary", elem_classes="eink-scan")
+            if os.path.isdir(EXAMPLES_DIR):
+                samples = [[os.path.join(EXAMPLES_DIR, f)] for f in (
+                    "chanterelle.jpg", "lions_mane.jpg", "wild_blueberry.jpg",
+                    "yarrow.jpg", "poison_hemlock.jpg") if os.path.exists(os.path.join(EXAMPLES_DIR, f))]
+                if samples:
+                    gr.Examples(examples=samples, inputs=img,
+                                label="No specimen handy? Try a sample:")
+        with gr.Column(scale=1, elem_classes="eink-screen"):
+            out = gr.HTML(_idle())
+    gr.HTML(QUOTE_BAR)
+    gr.Markdown(SAFETY_NOTICE, elem_id="notice")
+
+    btn.click(identify, inputs=img, outputs=out)
+    img.change(identify, inputs=img, outputs=out)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+if __name__ == "__main__":
+    demo.launch(theme=gr.themes.Monochrome(), css=CSS)

models/berry_expert_classes.json

@@ -0,0 +1,13 @@
+[
+  "bittersweet_nightshade_toxic",
+  "blackberry_common",
+  "blueberry_highbush",
+  "blueberry_wild",
+  "canada_moonseed_deadly",
+  "elderberry_american",
+  "poison_ivy",
+  "pokeweed_toxic",
+  "staghorn_sumac",
+  "virginia_creeper_toxic",
+  "wild_grape_riverbank"
+]

models/domain_router_v2_classes.json

@@ -0,0 +1,6 @@
+[
+  "berry",
+  "mushroom",
+  "other",
+  "plant"
+]

models/energy_thresholds.json

@@ -0,0 +1,26 @@
+{
+  "berry_expert": {
+    "p99": -2.6033,
+    "p95": -2.7896,
+    "p90": -2.8722,
+    "p85": -2.9346
+  },
+  "highvalue_expert": {
+    "p99": -2.8313,
+    "p95": -3.4466,
+    "p90": -3.5391,
+    "p85": -3.5952
+  },
+  "psychedelics_expert": {
+    "p99": -2.7753,
+    "p95": -2.9717,
+    "p90": -3.0823,
+    "p85": -3.1227
+  },
+  "medicinals_expert": {
+    "p99": -2.7018,
+    "p95": -3.1205,
+    "p90": -3.3293,
+    "p85": -3.414
+  }
+}

models/highvalue_expert_classes.json

@@ -0,0 +1,13 @@
+[
+  "chaga_medicinal",
+  "chanterelles_edible",
+  "chicken_of_the_woods",
+  "ginseng_american",
+  "high_value_toxics",
+  "lions_mane",
+  "morels_edible",
+  "ostrich_fern_fiddlehead",
+  "ramps_wild_leek",
+  "reishi_northeast",
+  "saffron_crocus"
+]

models/medicinals_expert_classes.json

@@ -0,0 +1,23 @@
+[
+  "boneset",
+  "burdock",
+  "catnip",
+  "coltsfoot",
+  "echinacea",
+  "foxglove_toxic",
+  "goldenrod",
+  "motherwort",
+  "mullein",
+  "plantain_broadleaf",
+  "poison_hemlock_deadly",
+  "red_clover",
+  "st_johns_wort",
+  "stinging_nettle",
+  "valerian",
+  "water_hemlock_deadly",
+  "white_snakeroot_toxic",
+  "wild_bergamot",
+  "wild_carrot",
+  "wood_nettle",
+  "yarrow"
+]

pipeline/__init__.py

@@ -0,0 +1 @@
+"""Field Station inference pipeline (ONNX / CPU)."""

pipeline/convergence.py

@@ -0,0 +1,110 @@
+"""
+convergence.py — Turn an infer.Pipeline result into a single ForagerResult.
+
+Ported from forager_ml with the Hailo coupling removed: instead of a
+RawPrediction it takes the plain dict returned by infer.Pipeline.identify().
+The safety-first philosophy is unchanged — abstain by default, flag DEADLY
+prominently, never present a below-threshold guess as an identification.
+"""
+
+from dataclasses import dataclass
+
+from .metadata import SPECIES_METADATA, UNKNOWN_META
+
+LOW_CONFIDENCE_THRESHOLD    = 0.50  # below this -> flagged low_confidence
+EXPERT_CONFIDENCE_THRESHOLD = 0.60  # a committed (non-deadly) ID must clear this.
+                                    # These experts are accurate but underconfident
+                                    # on SAFE classes (avg ~0.5–0.6); 0.60 balances
+                                    # decisiveness (75% safe-correct) against residual
+                                    # deadly-as-safe. The UX never treats SAFE as a
+                                    # green light — see app.py — so the gate is a
+                                    # usability dial, not the safety mechanism.
+DEADLY_VETO_FLOOR           = 0.40  # a DEADLY call at/above this overrides any
+                                    # higher-confidence SAFE/CAUTION call from
+                                    # another expert (safety-biased arbitration)
+
+
+@dataclass
+class ForagerResult:
+    domain:          str
+    species:         str    # class key, or "unknown"
+    scientific_name: str
+    confidence:      float
+    safety:          str    # SAFE | CAUTION | DEADLY | UNKNOWN
+    lookalike:       str
+    key_diff:        str
+    low_confidence:  bool
+    expert_model:    str
+    abstained:       bool
+    reason:          str     # why we abstained (or "" when committed)
+
+    @property
+    def is_deadly(self) -> bool:
+        return self.safety == "DEADLY" and not self.low_confidence
+
+    @property
+    def is_unknown(self) -> bool:
+        return self.species == "unknown"
+
+
+_ABSTAIN_REASON = {
+    "uncertain_domain": "Couldn't confidently place this in a known domain.",
+    "off_domain":       "This doesn't look like anything in the trained domains.",
+    "low_confidence":   "Leaning toward an answer, but not confident enough to commit.",
+}
+
+
+def _abstain(domain: str, reason: str, conf: float = 0.0, expert: str = "none") -> ForagerResult:
+    return ForagerResult(
+        domain=domain, species="unknown", scientific_name=UNKNOWN_META["scientific"],
+        confidence=conf, safety="UNKNOWN", lookalike=UNKNOWN_META["lookalike"],
+        key_diff=_ABSTAIN_REASON.get(reason, UNKNOWN_META["key_diff"]),
+        low_confidence=True, expert_model=expert, abstained=True, reason=reason,
+    )
+
+
+def _commit(domain: str, c: dict) -> ForagerResult:
+    species = c["species"]
+    meta = SPECIES_METADATA.get(species, UNKNOWN_META)
+    conf = float(c["confidence"])
+    return ForagerResult(
+        domain=domain, species=species, scientific_name=meta["scientific"], confidence=conf,
+        safety=meta["safety"], lookalike=meta["lookalike"], key_diff=meta["key_diff"],
+        low_confidence=conf < LOW_CONFIDENCE_THRESHOLD, expert_model=c["expert"],
+        abstained=False, reason="",
+    )
+
+
+def build_result(call: dict) -> ForagerResult:
+    """
+    `call` is the dict from infer.Pipeline.identify().
+
+    Safety-biased arbitration across the domain's experts:
+      1. If ANY expert flags a DEADLY species at >= DEADLY_VETO_FLOOR, surface
+         that — a deadly vote vetoes a more-confident SAFE/CAUTION call from an
+         off-domain expert (prevents e.g. hemlock -> "ramps" because highvalue
+         is more confident than medicinals).
+      2. Otherwise take the highest-confidence call, abstaining if it can't
+         clear EXPERT_CONFIDENCE_THRESHOLD.
+    """
+    domain = call.get("domain", "unknown")
+
+    if call.get("abstain") and "calls" not in call:
+        return _abstain(domain, call.get("reason", "low_confidence"),
+                        float(call.get("confidence", 0.0)), call.get("expert", "none"))
+
+    calls = call.get("calls", [])
+    if not calls:
+        return _abstain(domain, "low_confidence")
+
+    for c in calls:
+        c["safety"] = SPECIES_METADATA.get(c["species"], UNKNOWN_META)["safety"]
+
+    deadly = [c for c in calls if c["safety"] == "DEADLY" and c["confidence"] >= DEADLY_VETO_FLOOR]
+    if deadly:
+        return _commit(domain, max(deadly, key=lambda c: c["confidence"]))
+
+    best = max(calls, key=lambda c: c["confidence"])
+    if best["confidence"] < EXPERT_CONFIDENCE_THRESHOLD:
+        return _abstain(domain, "low_confidence", best["confidence"], best["expert"])
+    return _commit(domain, best)

pipeline/infer.py

@@ -0,0 +1,150 @@
+"""
+infer.py — Two-stage ONNX inference for the Field Station Space.
+
+Mirrors the on-device forager_ml pipeline (domain router -> expert routing ->
+abstention) but runs on CPU via onnxruntime instead of the Hailo NPU.
+
+Stage 1: domain router classifies the frame into berry / mushroom / plant / other.
+Stage 2: route to the matching expert(s); for multi-expert domains run both and
+         keep the higher-confidence call. Abstain (UNKNOWN) when the router is
+         unsure, the domain is "other", or the winning expert is below threshold.
+
+Preprocessing note: these ONNX files are the bare timm tf_efficientnet_lite2
+models (no normalization baked in), so inputs are ImageNet-normalized
+[1, 3, 224, 224] — NOT the [0,255] NHWC the HEF expects.
+"""
+
+import json
+import os
+
+import numpy as np
+import onnxruntime as ort
+from PIL import Image
+
+MODELS_DIR = os.path.join(os.path.dirname(__file__), "..", "models")
+
+ROUTER = "domain_router_v2"
+# The psychedelics/mycologist expert is intentionally NOT shipped in this public
+# Space: it is never routed to (mushroom -> highvalue only) and a psilocybin
+# identifier invites policy scrutiny for zero functional gain. It still lives in
+# forager_ml and can ship as its own model repo.
+EXPERTS = ["berry_expert", "highvalue_expert", "medicinals_expert"]
+
+# Router domain -> the ONE expert that owns it. Single-expert routing (no
+# cross-expert voting): an off-domain expert never gets to misclassify an input
+# it doesn't own — e.g. highvalue never sees a plant, so it can't call a hemlock
+# "ramps". The deadly plants live in medicinals (0% toxic-as-edible FAR).
+# "other" is intentionally absent => abstain. The mycologist/psychedelics expert
+# is held out of the live path (weak on real photos; benched).
+DOMAIN_EXPERTS: dict[str, str] = {
+    "berry":    "berry_expert",
+    "mushroom": "highvalue_expert",
+    "plant":    "medicinals_expert",
+}
+
+# Gates (match the on-device convergence thresholds).
+ROUTER_CONFIDENCE_THRESHOLD = 0.74
+EXPERT_CONFIDENCE_THRESHOLD = 0.75
+
+# Energy-OOD vote suppression: an expert's vote is dropped when its input energy
+# exceeds the in-domain threshold (i.e. the frame is out-of-domain for that
+# expert). This stops an off-domain expert from out-voting the correct one —
+# e.g. highvalue calling a hemlock "ramps". Thresholds are fp32 in-domain
+# percentiles in models/energy_thresholds.json (correct -logsumexp energy).
+# With single-expert routing there are no competing votes to suppress, and
+# val-calibrated thresholds over-fire on real photos. Off by default; the
+# router's "other" class + the confidence gate carry OOD.
+ENABLE_ENERGY_SUPPRESSION = False
+ENERGY_SUPPRESS_PERCENTILE = "p90"
+
+_IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
+_IMAGENET_STD  = np.array([0.229, 0.224, 0.225], dtype=np.float32)
+_RESIZE_SHORT  = int(224 * 1.14)  # 255, matches the training/val transform
+_CROP          = 224
+
+
+def preprocess(img: Image.Image) -> np.ndarray:
+    """PIL image -> ImageNet-normalized float32 NCHW [1, 3, 224, 224]."""
+    img = img.convert("RGB")
+    w, h = img.size
+    if w <= h:
+        nw, nh = _RESIZE_SHORT, round(_RESIZE_SHORT * h / w)
+    else:
+        nw, nh = round(_RESIZE_SHORT * w / h), _RESIZE_SHORT
+    img = img.resize((nw, nh), Image.BILINEAR)
+    left = (nw - _CROP) // 2
+    top  = (nh - _CROP) // 2
+    img = img.crop((left, top, left + _CROP, top + _CROP))
+
+    x = np.asarray(img, dtype=np.float32) / 255.0          # HWC, [0,1]
+    x = (x - _IMAGENET_MEAN) / _IMAGENET_STD               # ImageNet normalize
+    x = np.transpose(x, (2, 0, 1))[None]                   # 1, C, H, W
+    return np.ascontiguousarray(x, dtype=np.float32)
+
+
+def _softmax(logits: np.ndarray) -> np.ndarray:
+    z = logits - logits.max()
+    e = np.exp(z)
+    return e / e.sum()
+
+
+def _energy(logits: np.ndarray) -> float:
+    """Correct energy E(x) = -logsumexp(logits). Higher = more out-of-domain."""
+    m = logits.max()
+    return -float(m + np.log(np.exp(logits - m).sum()))
+
+
+class Pipeline:
+    """Loads all ONNX sessions once and runs the two-stage identification."""
+
+    def __init__(self, models_dir: str = MODELS_DIR):
+        self._sessions: dict[str, ort.InferenceSession] = {}
+        self._classes: dict[str, list[str]] = {}
+        for name in [ROUTER, *EXPERTS]:
+            self._sessions[name] = ort.InferenceSession(
+                os.path.join(models_dir, f"{name}_logits.onnx"),
+                providers=["CPUExecutionProvider"],
+            )
+            with open(os.path.join(models_dir, f"{name}_classes.json")) as f:
+                self._classes[name] = json.load(f)
+
+        self._energy_thr: dict[str, float] = {}
+        thr_path = os.path.join(models_dir, "energy_thresholds.json")
+        if ENABLE_ENERGY_SUPPRESSION and os.path.exists(thr_path):
+            with open(thr_path) as f:
+                table = json.load(f)
+            self._energy_thr = {n: v[ENERGY_SUPPRESS_PERCENTILE] for n, v in table.items()}
+
+    def _run(self, name: str, x: np.ndarray) -> tuple[str, float, float]:
+        """Returns (top_class, top_confidence, energy)."""
+        logits = self._sessions[name].run(None, {"input": x})[0][0]
+        probs = _softmax(logits)
+        idx = int(probs.argmax())
+        return self._classes[name][idx], float(probs[idx]), _energy(logits)
+
+    def identify(self, img: Image.Image) -> dict:
+        """
+        Returns a dict describing the call:
+          { domain, domain_confidence, abstain, reason?,
+            expert?, species?, confidence?, runner_up? }
+        """
+        x = preprocess(img)
+
+        # ── Stage 1: domain router ───────────────────────────────────────────
+        domain, dconf, _ = self._run(ROUTER, x)
+        out = {"domain": domain, "domain_confidence": dconf}
+
+        if dconf < ROUTER_CONFIDENCE_THRESHOLD or domain not in DOMAIN_EXPERTS:
+            reason = "uncertain_domain" if dconf < ROUTER_CONFIDENCE_THRESHOLD else "off_domain"
+            return {**out, "abstain": True, "reason": reason}
+
+        # ── Stage 2: run the single expert that owns this domain. Optional
+        #    energy gate abstains if the frame is out-of-domain for that expert.
+        ename = DOMAIN_EXPERTS[domain]
+        species, conf, energy = self._run(ename, x)
+        thr = self._energy_thr.get(ename)
+        if thr is not None and energy > thr:
+            return {**out, "abstain": True, "reason": "off_domain"}
+
+        call = {"expert": ename, "species": species, "confidence": conf, "energy": round(energy, 4)}
+        return {**out, "abstain": False, "calls": [call]}

pipeline/metadata.py

@@ -0,0 +1,80 @@
+"""
+metadata.py — Per-species safety metadata.
+
+Lifted verbatim from the forager_ml inference pipeline (convergence.py) so the
+Space stays decoupled from the Hailo runtime. Keep in sync via
+scripts/sync_from_forager_ml.sh if the source dict changes.
+
+safety tiers: SAFE | CAUTION | DEADLY | UNKNOWN
+"""
+
+SPECIES_METADATA: dict[str, dict] = {
+    # ── Berry expert ──────────────────────────────────────────────────────────
+    "blackberry_common":            {"safety": "SAFE",    "scientific": "Rubus allegheniensis",       "lookalike": "Pokeweed (young)",       "key_diff": "Pokeweed has smooth stems, white flowers"},
+    "blueberry_highbush":           {"safety": "SAFE",    "scientific": "Vaccinium corymbosum",       "lookalike": "Canada moonseed",        "key_diff": "Moonseed has one crescent seed, no true drupelets"},
+    "blueberry_wild":               {"safety": "SAFE",    "scientific": "Vaccinium angustifolium",    "lookalike": "Canada moonseed",        "key_diff": "Moonseed has one crescent seed, no true drupelets"},
+    "elderberry_american":          {"safety": "CAUTION", "scientific": "Sambucus canadensis",        "lookalike": "Pokeweed",               "key_diff": "Elderberry has compound leaves; must be cooked"},
+    "staghorn_sumac":               {"safety": "SAFE",    "scientific": "Rhus typhina",               "lookalike": "Poison sumac",           "key_diff": "Poison sumac has white berries, swampy habitat"},
+    "wild_grape_riverbank":         {"safety": "SAFE",    "scientific": "Vitis riparia",              "lookalike": "Canada moonseed",        "key_diff": "Grape has tendrils and true seeds"},
+    "bittersweet_nightshade_toxic": {"safety": "DEADLY",  "scientific": "Solanum dulcamara",          "lookalike": "N/A",                    "key_diff": "Purple flowers, red-to-black berries — avoid"},
+    "canada_moonseed_deadly":       {"safety": "DEADLY",  "scientific": "Menispermum canadense",      "lookalike": "Wild grape",             "key_diff": "Crescent-shaped seed, no tendrils"},
+    "poison_ivy":                   {"safety": "DEADLY",  "scientific": "Toxicodendron radicans",     "lookalike": "N/A",                    "key_diff": "Leaves of three, let it be"},
+    "pokeweed_toxic":               {"safety": "DEADLY",  "scientific": "Phytolacca americana",       "lookalike": "Elderberry",             "key_diff": "Pink-red stems, hollow; all parts toxic"},
+    "virginia_creeper_toxic":       {"safety": "CAUTION", "scientific": "Parthenocissus quinquefolia","lookalike": "N/A",                    "key_diff": "5-leaflet vine; berries toxic"},
+
+    # ── High-value expert ─────────────────────────────────────────────────────
+    "chanterelles_edible":          {"safety": "SAFE",    "scientific": "Cantharellus cibarius",      "lookalike": "Jack-o'-lantern",        "key_diff": "Jack-o'-lantern has true gills, grows in clusters"},
+    "morels_edible":                {"safety": "SAFE",    "scientific": "Morchella esculenta",        "lookalike": "False morel",            "key_diff": "True morel is fully hollow; false morel has cottony interior"},
+    "chicken_of_the_woods":         {"safety": "SAFE",    "scientific": "Laetiporus sulphureus",      "lookalike": "N/A",                    "key_diff": "Unmistakable orange shelf; avoid on conifers"},
+    "lions_mane":                   {"safety": "SAFE",    "scientific": "Hericium erinaceus",         "lookalike": "N/A",                    "key_diff": "White cascade of teeth — no true lookalike"},
+    "chaga_medicinal":              {"safety": "SAFE",    "scientific": "Inonotus obliquus",          "lookalike": "Burnt wood knot",        "key_diff": "Orange-yellow interior when cut"},
+    "reishi_mushroom":              {"safety": "SAFE",    "scientific": "Ganoderma lucidum",          "lookalike": "N/A",                    "key_diff": "Shiny lacquered cap, white pore surface — verify species for region"},
+    "reishi_northeast":             {"safety": "SAFE",    "scientific": "Ganoderma tsugae",           "lookalike": "N/A",                    "key_diff": "Shiny lacquered cap, white pore surface"},
+    "ramps_wild_leek":              {"safety": "SAFE",    "scientific": "Allium tricoccum",           "lookalike": "Lily of the valley",     "key_diff": "Lily of the valley has no garlic smell — critical check"},
+    "ostrich_fern_fiddlehead":      {"safety": "CAUTION", "scientific": "Matteuccia struthiopteris",  "lookalike": "Bracken fern",           "key_diff": "Ostrich fern has deep U-shaped groove on stem"},
+    "ginseng_american":             {"safety": "SAFE",    "scientific": "Panax quinquefolius",        "lookalike": "N/A",                    "key_diff": "Protected species — observe, don't harvest"},
+    "saffron_crocus":               {"safety": "SAFE",    "scientific": "Crocus sativus",             "lookalike": "Autumn crocus",          "key_diff": "Autumn crocus is highly toxic — 3 stigmas only in saffron"},
+    "high_value_toxics":            {"safety": "DEADLY",  "scientific": "Various",                    "lookalike": "N/A",                    "key_diff": "High-value toxic lookalike class — do not consume"},
+
+    # ── Psychedelics / mycologist expert ──────────────────────────────────────
+    "amanita_phalloides_deadly":    {"safety": "DEADLY",  "scientific": "Amanita phalloides",         "lookalike": "Puffball (young)",       "key_diff": "Death cap has volva at base, white gills, ring"},
+    "amanita_muscaria_toxic":       {"safety": "DEADLY",  "scientific": "Amanita muscaria",           "lookalike": "N/A",                    "key_diff": "Red cap with white warts — highly toxic"},
+    "galerina_marginata_toxic":     {"safety": "DEADLY",  "scientific": "Galerina marginata",         "lookalike": "Psilocybe species",      "key_diff": "Rusty brown spore print; ring present — deadly lookalike"},
+    "conocybe_filaris_deadly":      {"safety": "DEADLY",  "scientific": "Conocybe filaris",           "lookalike": "Psilocybe species",      "key_diff": "Rusty-brown spores; tiny ring on stem"},
+    "gymnopilus_junonius":          {"safety": "CAUTION", "scientific": "Gymnopilus junonius",        "lookalike": "Chanterelle",            "key_diff": "Very bitter taste; yellow-orange gills"},
+    "panaeolus_cinctulus":          {"safety": "CAUTION", "scientific": "Panaeolus cinctulus",        "lookalike": "Edible field mushrooms", "key_diff": "Brown rim band on cap; dung/rich soil habitat"},
+    "psilocybe_ovoideocystidiata":  {"safety": "CAUTION", "scientific": "Psilocybe ovoideocystidiata","lookalike": "Galerina marginata",     "key_diff": "Blue bruising; wood chip habitat; rusty spores in Galerina"},
+    "psilocybe_cubensis":           {"safety": "CAUTION", "scientific": "Psilocybe cubensis",         "lookalike": "Galerina marginata",     "key_diff": "Bruises blue; purple-black spore print — Galerina does not bruise"},
+    "psilocybe_cyanescens":         {"safety": "CAUTION", "scientific": "Psilocybe cyanescens",       "lookalike": "Galerina marginata",     "key_diff": "Wavy cap edge; strong blue bruising"},
+    "psilocybe_semilanceata":       {"safety": "CAUTION", "scientific": "Psilocybe semilanceata",     "lookalike": "Conocybe filaris",       "key_diff": "Pointed nipple-cap; deep blue bruising"},
+    "psilocybe_azurescens":         {"safety": "CAUTION", "scientific": "Psilocybe azurescens",       "lookalike": "Galerina marginata",     "key_diff": "Caramel cap, very potent blue bruising"},
+    "psilocybe_caerulipes":         {"safety": "CAUTION", "scientific": "Psilocybe caerulipes",       "lookalike": "Galerina marginata",     "key_diff": "Blue stem base; deciduous wood debris habitat"},
+    "other_mushroom":               {"safety": "UNKNOWN", "scientific": "Unknown",                    "lookalike": "N/A",                    "key_diff": "Cannot identify — do not consume"},
+    "panax_quinquefolius_ginseng_conservation": {
+                                     "safety": "SAFE",    "scientific": "Panax quinquefolius",        "lookalike": "N/A",                    "key_diff": "Protected — observe only, do not harvest"},
+
+    # ── Medicinals expert ─────────────────────────────────────────────────────
+    "boneset":                      {"safety": "CAUTION", "scientific": "Eupatorium perfoliatum",     "lookalike": "White snakeroot",        "key_diff": "White snakeroot has heart-shaped leaves, highly toxic — confirm perfoliate leaf pairs"},
+    "burdock":                      {"safety": "SAFE",    "scientific": "Arctium lappa",              "lookalike": "Rhubarb (leaves)",       "key_diff": "Rhubarb leaves are highly toxic; burdock has burr seedheads"},
+    "catnip":                       {"safety": "SAFE",    "scientific": "Nepeta cataria",             "lookalike": "N/A",                    "key_diff": "Square stem, grey-green downy leaves, minty-musty scent"},
+    "coltsfoot":                    {"safety": "CAUTION", "scientific": "Tussilago farfara",          "lookalike": "Dandelion (flower)",     "key_diff": "Coltsfoot flowers appear before leaves; pyrrolizidine alkaloids — avoid internal use"},
+    "echinacea":                    {"safety": "SAFE",    "scientific": "Echinacea purpurea",         "lookalike": "N/A",                    "key_diff": "Spiny orange-brown cone with drooping purple rays"},
+    "foxglove_toxic":               {"safety": "DEADLY",  "scientific": "Digitalis purpurea",         "lookalike": "Comfrey (rosette)",      "key_diff": "Foxglove has tubular spotted flowers; all parts highly toxic — cardiac glycosides"},
+    "goldenrod":                    {"safety": "SAFE",    "scientific": "Solidago canadensis",        "lookalike": "N/A",                    "key_diff": "Arching plumes of small yellow flowers in late summer"},
+    "motherwort":                   {"safety": "CAUTION", "scientific": "Leonurus cardiaca",          "lookalike": "N/A",                    "key_diff": "Square stem, deeply lobed leaves, pink-purple flowers; avoid in pregnancy"},
+    "mullein":                      {"safety": "SAFE",    "scientific": "Verbascum thapsus",          "lookalike": "N/A",                    "key_diff": "Distinctive tall spike, large velvety basal rosette leaves"},
+    "plantain_broadleaf":           {"safety": "SAFE",    "scientific": "Plantago major",             "lookalike": "N/A",                    "key_diff": "Oval ribbed leaves, parallel veins, narrow seedhead spike"},
+    "poison_hemlock_deadly":        {"safety": "DEADLY",  "scientific": "Conium maculatum",           "lookalike": "Wild carrot",            "key_diff": "Purple-blotched hollow stem, musty smell, no hairy stem — ALL parts deadly"},
+    "red_clover":                   {"safety": "SAFE",    "scientific": "Trifolium pratense",         "lookalike": "N/A",                    "key_diff": "Pink-purple globe flowers, trifoliate leaves with pale V-chevron"},
+    "st_johns_wort":                {"safety": "CAUTION", "scientific": "Hypericum perforatum",       "lookalike": "N/A",                    "key_diff": "Yellow 5-petalled flowers with black dots; translucent leaf dots; photosensitizing"},
+    "stinging_nettle":              {"safety": "SAFE",    "scientific": "Urtica dioica",              "lookalike": "Wood nettle",            "key_diff": "Cook or dry to neutralize sting; serrated leaves, opposite pairs"},
+    "valerian":                     {"safety": "CAUTION", "scientific": "Valeriana officinalis",      "lookalike": "Water hemlock",          "key_diff": "Water hemlock has purple-streaked hollow stem, chambered root — deadly; valerian has pinnate leaves"},
+    "water_hemlock_deadly":         {"safety": "DEADLY",  "scientific": "Cicuta maculata",            "lookalike": "Wild carrot / Valerian", "key_diff": "Chambered root, purple-streaked hollow stem — most violently toxic plant in NA"},
+    "white_snakeroot_toxic":        {"safety": "DEADLY",  "scientific": "Ageratina altissima",        "lookalike": "Boneset",                "key_diff": "Heart-shaped leaves, flat-topped white flowers; causes milk sickness — avoid"},
+    "wild_bergamot":                {"safety": "SAFE",    "scientific": "Monarda fistulosa",          "lookalike": "N/A",                    "key_diff": "Lavender ragged flowers, square stem, oregano-like scent"},
+    "wild_carrot":                  {"safety": "CAUTION", "scientific": "Daucus carota",              "lookalike": "Poison hemlock / Water hemlock", "key_diff": "Hairy stem, central purple floret, carroty smell — confirm all three before use"},
+    "wood_nettle":                  {"safety": "SAFE",    "scientific": "Laportea canadensis",        "lookalike": "Stinging nettle",        "key_diff": "Alternate leaves (vs opposite in stinging nettle); forested habitat; cook to neutralize"},
+    "yarrow":                       {"safety": "CAUTION", "scientific": "Achillea millefolium",        "lookalike": "Poison hemlock (leaf)",  "key_diff": "Flat-topped white flower clusters, ferny aromatic leaves; hemlock has blotched hollow stem"},
+}
+
+UNKNOWN_META = {"safety": "UNKNOWN", "scientific": "Unknown", "lookalike": "N/A", "key_diff": "No confident identification"}

requirements.txt

@@ -0,0 +1,4 @@
+gradio==6.16.0
+onnxruntime==1.18.0
+numpy<2
+pillow