polish: remove emojis and em dashes from sources and docs

BLOG_POST.md

@@ -6,7 +6,7 @@
 
 ## The constraint that made this interesting
 
-The hackathon's only hard rule: **total parameters ≤ 32 billion**, and the whole thing must run on a laptop. No cloud APIs at runtime. Build something that solves a real problem for a real person, then prove a small model is genuinely the right tool for the job — not a cop-out for not having GPT-4o's API key.
+The hackathon's only hard rule: **total parameters ≤ 32 billion**, and the whole thing must run on a laptop. No cloud APIs at runtime. Build something that solves a real problem for a real person, then prove a small model is genuinely the right tool for the job, not a cop-out for not having GPT-4o's API key.
 
 I built **Apiarist**: a fully-offline AI hive frame inspector for backyard beekeepers. You point a phone camera at any honeycomb frame and the app counts bees, flags the queen, spots varroa mites, and writes a narrative report you can actually log. No internet required.
 
@@ -14,7 +14,7 @@ Live demo: [huggingface.co/spaces/build-small-hackathon/Apiarist](https://huggin
 
 ## Why beekeeping, why now
 
-Frank — a beekeeper a few streets over — keeps 14 hives. Every weekend he inspects each frame and writes notes by hand in a beat-up notebook. Last summer he lost four colonies because he didn't spot a queen failure in time.
+Frank, a beekeeper a few streets over, keeps 14 hives. Every weekend he inspects each frame and writes notes by hand in a beat-up notebook. Last summer he lost four colonies because he didn't spot a queen failure in time.
 
 The problem is real but tiny: he's not going to upload private hive data to a cloud API, and his apiary has no signal anyway. He doesn't need a foundation model. He needs **a fast, focused tool that runs on his laptop in a field.**
 
@@ -31,13 +31,13 @@ The trick: each model does what it's actually good at.
 
 Vision-language models are terrible at counting tiny things or picking subtle queens out of a sea of nearly-identical workers. Ask Qwen-3B "how many varroa mites?" and it will confidently hallucinate a number. Ask a specialist YOLO and you get a real count grounded in pixels.
 
-Inversely, YOLO can't write a useful report. "47 bees, 1 queen, 0 mites" is data, not language. Qwen takes those facts and produces "Healthy frame, queen present, brood pattern looks solid — no swarm prep visible."
+Inversely, YOLO can't write a useful report. "47 bees, 1 queen, 0 mites" is data, not language. Qwen takes those facts and produces "Healthy frame, queen present, brood pattern looks solid, no swarm prep visible."
 
 Together: structured + narrative. That's a real product.
 
 ## Training a YOLO on Modal in one afternoon
 
-The first dataset I tried (Matt Nudi's 909-image honey-bee set) had only ~50 queen examples. Queen mAP came out at 0.37 with P=0.19 — basically useless for queen detection.
+The first dataset I tried (Matt Nudi's 909-image honey-bee set) had only ~50 queen examples. Queen mAP came out at 0.37 with P=0.19, basically useless for queen detection.
 
 Pivoted to **hendricks_ricky/bee-project** on Roboflow Universe: 3,308 labeled images with 892 queens AND a Varroa class. Trained YOLOv8s for 60 epochs on a single Modal T4 in ~50 minutes for ~$0.40 of free hackathon credit.
 
@@ -59,7 +59,7 @@ The only network calls are *downloading* the models on first boot. After that, i
 
 - **More queen training data.** 892 examples is the most I could find, but the model still misses queens that aren't well-lit or facing the camera.
 - **A second specialist for swarm cells.** Right now those are detected by Qwen-3B's prose, which means false alarms.
-- **Voice notes**, with Whisper transcription. Beekeepers wear gloves — typing is bad UX in the field.
+- **Voice notes**, with Whisper transcription. Beekeepers wear gloves, typing is bad UX in the field.
 - **Bee count trends over time** in the PDF report. Pretty graphs sell.
 
 ## The thing I learned

DEMO_VIDEO_SCRIPT.md

@@ -1,4 +1,4 @@
-# Apiarist — 60s Demo Video Script
+# Apiarist, 60s Demo Video Script
 
 **Total runtime:** 60 seconds
 **Format:** Screen recording (your laptop) + voiceover. No face-on-camera needed.
@@ -9,47 +9,47 @@
 
 ## Shot list (timecoded)
 
-### 0–5s — Hook
+### 0-5s, Hook
 **Visual:** Close-up still of a honeycomb frame from the dataset, full screen. Cut to a laptop sitting on a wooden surface (your desk works).
 **Voiceover:**
 > "Beekeepers inspect hives every weekend. Most still write notes in a beat-up notebook."
 
-### 5–15s — Problem
+### 5-15s, Problem
 **Visual:** Browser tab opening to Apiarist Space. Pause on the welcome screen.
 **Voiceover:**
-> "Last summer, Frank — a backyard beekeeper down the road — lost four colonies because he didn't spot queen failures in time. Apiarist is the assistant he should have had."
+> "Last summer, Frank, a backyard beekeeper down the road, lost four colonies because he didn't spot queen failures in time. Apiarist is the assistant he should have had."
 
-### 15–30s — The App In Action (THE PROOF)
+### 15-30s, The App In Action (THE PROOF)
 **Visual:** Screen recording.
 1. Click **Inspect** tab
 2. Pick **"Hive #7"** from dropdown
-3. Upload a clean bee frame photo (use one with a visible queen — pre-test which ones the model nails)
+3. Upload a clean bee frame photo (use one with a visible queen, pre-test which ones the model nails)
 4. Click **Analyze Frame**
-5. ~5 seconds of "thinking" — cut this short in edit if needed
+5. ~5 seconds of "thinking", cut this short in edit if needed
 6. **Annotated frame appears** with bounding boxes
-7. **Pan over the result panel** — show queen badge (bright green), bee counts, brood pattern, notes
+7. **Pan over the result panel**, show queen badge (bright green), bee counts, brood pattern, notes
 
 **Voiceover (over the action):**
-> "Snap a photo of any comb frame. Apiarist runs a custom-trained YOLO detector to find queens, drones, workers, and varroa mites — then a 3-billion-parameter vision-language model writes a narrative report. All local. No cloud APIs. No subscription."
+> "Snap a photo of any comb frame. Apiarist runs a custom-trained YOLO detector to find queens, drones, workers, and varroa mites, then a 3-billion-parameter vision-language model writes a narrative report. All local. No cloud APIs. No subscription."
 
-### 30–42s — The Kill Shot (Compare tab)
+### 30-42s, The Kill Shot (Compare tab)
 **Visual:** Switch to **Compare** tab. Upload same photo. Click "Run Comparison." Both sides analyze. Camera lingers on the side-by-side: clean structured output on the left (Apiarist), vague prose on the right (raw VLM).
 **Voiceover:**
-> "Compare a generic vision model on its own — it describes the photo, but can't count bees or pick out the queen. Apiarist's specialist YOLO + small VLM combo gives you something a beekeeper can actually log."
+> "Compare a generic vision model on its own, it describes the photo, but can't count bees or pick out the queen. Apiarist's specialist YOLO + small VLM combo gives you something a beekeeper can actually log."
 
-### 42–52s — Workflow
+### 42-52s, Workflow
 **Visual:**
 - Switch to **Hives** tab
 - Show the apiary table with multiple hives
-- Click into inspection history for one hive — show 4–5 past inspections
-- Click **"Generate PDF Report"** — show the downloaded PDF preview
+- Click into inspection history for one hive, show 4-5 past inspections
+- Click **"Generate PDF Report"**, show the downloaded PDF preview
 
 **Voiceover:**
 > "Every inspection auto-saves to that hive's log. Generate weekly PDF reports. Track which hives need attention. SQLite-backed, runs anywhere."
 
-### 52–60s — Close
+### 52-60s, Close
 **Visual:** Apiarist logo full screen with text overlay:
-> 🐝 Apiarist
+> Apiarist
 > Custom YOLOv8s + Qwen2.5-VL-3B
 > Fully offline. Built in 10 days.
 > huggingface.co/spaces/build-small-hackathon/Apiarist
@@ -63,12 +63,12 @@
 
 - [ ] Practice run the click-through 3 times before hitting record
 - [ ] Pre-load 3-4 bee photos in a folder, picked because the model handles them well
-- [ ] Pre-create at least 3 hives in the Hives tab with realistic names ("Hive #1 — Northeast", "Hive #2 — Apricot tree", "Hive #3 — Backyard")
-- [ ] Log 4–5 inspections per hive before recording (so the history view has content)
+- [ ] Pre-create at least 3 hives in the Hives tab with realistic names ("Hive #1, Northeast", "Hive #2, Apricot tree", "Hive #3, Backyard")
+- [ ] Log 4-5 inspections per hive before recording (so the history view has content)
 - [ ] Switch the Space to T4/ZeroGPU mode and prewarm by clicking Analyze once before recording (avoids 30s cold-start in the video)
-- [ ] Record voiceover separately on phone in a quiet room — read each section twice, pick best take
+- [ ] Record voiceover separately on phone in a quiet room, read each section twice, pick best take
 - [ ] Edit in CapCut / Premiere / DaVinci Resolve (all free for this)
-- [ ] Add subtle background music (uncopyrighted — try YouTube Audio Library "instrumental folk")
+- [ ] Add subtle background music (uncopyrighted, try YouTube Audio Library "instrumental folk")
 - [ ] Export as MP4 1920x1080 30fps, under 50MB
 
 ## What NOT to do

LINKEDIN_POST.md

@@ -6,23 +6,23 @@ Post this on submission day. Upload the demo video natively (LinkedIn throttles
 
 I spent 10 days teaching a small AI to do something OpenAI's flagship can't: count bees on a honeycomb frame.
 
-For the Build Small Hackathon by Hugging Face × Gradio, I built **Apiarist** — a fully offline AI hive inspector for backyard beekeepers. Point a phone at any comb frame and it tells you:
+For the Build Small Hackathon by Hugging Face × Gradio, I built **Apiarist**, a fully offline AI hive inspector for backyard beekeepers. Point a phone at any comb frame and it tells you:
 
-✅ how many bees, drones, and queen candidates it sees
-✅ where the queen is (with a green box around her)
-✅ whether varroa mites are visible
-✅ what the brood pattern looks like
-✅ generates a weekly PDF inspection report
+ how many bees, drones, and queen candidates it sees
+ where the queen is (with a green box around her)
+ whether varroa mites are visible
+ what the brood pattern looks like
+ generates a weekly PDF inspection report
 
-Why small models? Because apiaries don't have signal. Hive-health data isn't for the cloud. And a custom YOLOv8s trained on 3,308 labeled bee images, paired with Qwen2.5-VL-3B running locally on ZeroGPU, beats GPT-4o at counting bees — because that's literally what it was designed for.
+Why small models? Because apiaries don't have signal. Hive-health data isn't for the cloud. And a custom YOLOv8s trained on 3,308 labeled bee images, paired with Qwen2.5-VL-3B running locally on ZeroGPU, beats GPT-4o at counting bees, because that's literally what it was designed for.
 
 Specialist (YOLO) finds and counts the things. Generalist (VLM) writes the narrative report. Together: structured + narrative inspection in 5 seconds, no internet required.
 
 Total params: ~3 billion. Total cost per inspection: zero.
 
-🎥 Demo video below
-👉 Try it: huggingface.co/spaces/build-small-hackathon/Apiarist
-📓 Field notes: [link to your blog post once published]
+ Demo video below
+ Try it: huggingface.co/spaces/build-small-hackathon/Apiarist
+ Field notes: [link to your blog post once published]
 
 Thanks to Hugging Face, Gradio, Modal, OpenAI, NVIDIA, Cohere, Black Forest Labs, and OpenBMB for the hackathon and the compute.
 
@@ -47,15 +47,15 @@ The names turn **bold blue** when they're real tags.
 ## Upload order
 
 1. Open new LinkedIn post
-2. **Upload demo video file directly** (not a YouTube link) — LinkedIn favors native video
+2. **Upload demo video file directly** (not a YouTube link), LinkedIn favors native video
 3. Type the post text above (with tags applied)
-4. **Put the Space URL in the first comment**, not the post body — LinkedIn throttles posts with external links in the body
+4. **Put the Space URL in the first comment**, not the post body, LinkedIn throttles posts with external links in the body
 5. Post
 
 ## Backup channels
 
 If you don't get traction on LinkedIn, cross-post to:
-- Twitter / X (split into 3 tweets — hook + features + link)
+- Twitter / X (split into 3 tweets, hook + features + link)
 - Bluesky (same as Twitter)
-- Reddit r/beekeeping (separate post, lead with "I built an AI hive inspector — would love your feedback")
-- Reddit r/LocalLLaMA (lead with the tech story — specialist+generalist, 3B params, ZeroGPU)
+- Reddit r/beekeeping (separate post, lead with "I built an AI hive inspector, would love your feedback")
+- Reddit r/LocalLLaMA (lead with the tech story, specialist+generalist, 3B params, ZeroGPU)

README.md

@@ -23,7 +23,7 @@ datasets:
   - maryammeda/apiarist-inaturalist-bees
 ---
 
-# 🐝 Apiarist
+# Apiarist
 
 A fully-offline AI hive frame inspector for backyard beekeepers. Built
 in 10 days for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon).
@@ -50,15 +50,15 @@ All local. No cloud APIs at inference.
 
 ## Stack
 
-- 🤖 [Hugging Face Spaces](https://huggingface.co/spaces) on ZeroGPU
-- 🎨 [Gradio](https://gradio.app) with a custom field-tool theme
-- ⚡ [Modal](https://modal.com) for fine-tuning the YOLO (1× T4, 60 epochs)
-- 🐝 Training data: [Hendricks Ricky bee-project](https://universe.roboflow.com/hendricks_ricky-hotmail-de/bee-project) (3,308 imgs, 892 queens)
-- 🌿 Context imagery: [Apiarist iNaturalist bees dataset](https://huggingface.co/datasets/maryammeda/apiarist-inaturalist-bees)
+- [Hugging Face Spaces](https://huggingface.co/spaces) on ZeroGPU
+- [Gradio](https://gradio.app) with a custom field-tool theme
+- [Modal](https://modal.com) for fine-tuning the YOLO (1× T4, 60 epochs)
+- Training data: [Hendricks Ricky bee-project](https://universe.roboflow.com/hendricks_ricky-hotmail-de/bee-project) (3,308 imgs, 892 queens)
+- Context imagery: [Apiarist iNaturalist bees dataset](https://huggingface.co/datasets/maryammeda/apiarist-inaturalist-bees)
 
 ## Badges chased
 
-🔌 Off the Grid · 🎯 Well-Tuned · 🎨 Off-Brand · 📡 Sharing is Caring · 📓 Field Notes
+ Off the Grid · Well-Tuned · Off-Brand · Sharing is Caring · Field Notes
 
 ## License
 

app.py

@@ -23,7 +23,7 @@ import db
 import detector
 import report
 
-# ZeroGPU integration — no-op outside HF Spaces
+# ZeroGPU integration, no-op outside HF Spaces
 try:
     import spaces
 
@@ -99,12 +99,12 @@ def parse_response(text: str, hive_name: str) -> dict:
 
 def build_narrative(r: dict, raw: str) -> str:
     queen_line = (
-        "✅ Queen detected" if r["queen_detected"] else "⚠️ No queen visible"
+        " Queen detected" if r["queen_detected"] else " No queen visible"
     )
     swarm_line = (
-        "⚠️ Swarm cells (VLM estimate)"
+        " Swarm cells (VLM estimate)"
         if r["swarm_cells_detected"]
-        else "✅ No swarm cells (VLM estimate)"
+        else " No swarm cells (VLM estimate)"
     )
 
     yolo_block = ""
@@ -113,7 +113,7 @@ def build_narrative(r: dict, raw: str) -> str:
         # Show meaningful counts only
         ordered = ["queen", "drone", "bee", "pollenbee"]
         lines = [
-            f"  • **{counts[c]}** {c}" for c in ordered if counts.get(c)
+            f" • **{counts[c]}** {c}" for c in ordered if counts.get(c)
         ]
         yolo_block = "\n**Specialist detector counts:**\n" + "\n".join(lines)
         footer = "*Powered by custom YOLOv8s + Qwen2.5-VL-3B on ZeroGPU.*"
@@ -122,7 +122,7 @@ def build_narrative(r: dict, raw: str) -> str:
 
     # Mite count is Qwen-only (no specialist), flag that clearly
     mite_line = (
-        f"🔎 ~{r['varroa_mites_visible']} mite(s) (VLM estimate, not specialist)"
+        f" ~{r['varroa_mites_visible']} mite(s) (VLM estimate, not specialist)"
     )
 
     return f"""**Hive: {r['hive']}**
@@ -132,7 +132,7 @@ def build_narrative(r: dict, raw: str) -> str:
 {mite_line}
 {yolo_block}
 
-**Brood pattern:** {r['brood_pattern']}  |  **Overall:** {r['frame_health']}
+**Brood pattern:** {r['brood_pattern']} | **Overall:** {r['frame_health']}
 **Notes:** {r['notes']}
 
 ---
@@ -285,12 +285,12 @@ def run_comparison(image: Image.Image):
         left_results["queen_detected"] = counts.get("queen", 0) > 0
         left_results["yolo_counts"] = counts
         left_results["detector_used"] = True
-        left_md = _short_summary("🐝 Apiarist", left_results, extra_counts=counts)
+        left_md = _short_summary(" Apiarist", left_results, extra_counts=counts)
         left_image = annotated if annotated is not None else image
     else:
         left_md = (
-            "### 🐝 Apiarist\n\n"
-            "*Custom YOLO weights haven't landed yet — "
+            "### Apiarist\n\n"
+            "*Custom YOLO weights haven't landed yet, "
             "left column will activate as soon as `weights/honey_bee_detector.pt` is committed.*"
         )
         left_image = image
@@ -299,16 +299,16 @@ def run_comparison(image: Image.Image):
     right_response = _run_qwen(image, prefix="")
     right_results = parse_response(right_response, "")
     right_results["detector_used"] = False
-    right_md = _short_summary("🤖 Qwen alone", right_results)
+    right_md = _short_summary(" Qwen alone", right_results)
 
     return left_image, left_md, image, right_md
 
 
 def _short_summary(title: str, r: dict, extra_counts: dict | None = None) -> str:
-    queen = "✅ Queen detected" if r["queen_detected"] else "⚠️ No queen visible"
-    mite_icon = "⚠️" if r["varroa_mites_visible"] >= 3 else "✅"
+    queen = " Queen detected" if r["queen_detected"] else " No queen visible"
+    mite_icon = "" if r["varroa_mites_visible"] >= 3 else ""
     swarm = (
-        "⚠️ Swarm cells" if r["swarm_cells_detected"] else "✅ No swarm cells"
+        " Swarm cells" if r["swarm_cells_detected"] else " No swarm cells"
     )
     counts_block = ""
     if extra_counts:
@@ -323,7 +323,7 @@ def _short_summary(title: str, r: dict, extra_counts: dict | None = None) -> str
 {swarm}
 {counts_block}
 
-**Brood:** {r['brood_pattern']}  **Health:** {r['frame_health']}
+**Brood:** {r['brood_pattern']} **Health:** {r['frame_health']}
 **Notes:** {r['notes']}
 """
 
@@ -338,7 +338,7 @@ def _hives_table_state() -> list[list]:
         last = (
             time.strftime("%Y-%m-%d %H:%M", time.localtime(h["last_inspected"]))
             if h["last_inspected"]
-            else "—"
+            else "-"
         )
         out.append(
             [
@@ -359,13 +359,13 @@ def add_hive_action(name, location, marker, notes):
             _hives_table_state(),
             gr.update(),
             gr.update(),
-            "⚠️ Name required.",
+            " Name required.",
         )
     try:
         db.add_hive(name, location or "", marker or "", notes or "")
-        msg = f"✅ Added hive '{name}'."
+        msg = f" Added hive '{name}'."
     except sqlite3.IntegrityError:
-        msg = f"⚠️ Hive '{name}' already exists."
+        msg = f" Hive '{name}' already exists."
     hive_names = [h["name"] for h in db.list_hives()]
     return (
         _hives_table_state(),
@@ -457,13 +457,13 @@ def _seed_sample_data():
     """Drop a handful of plausible hives into the DB on first boot
     so the Hives tab isn't empty for first-visit judges."""
     if db.list_hives():
-        return  # someone already has data
+        return # someone already has data
     samples = [
-        ("Hive #1 — Apricot tree", "South corner of yard", "yellow",
+        ("Hive #1, Apricot tree", "South corner of yard", "yellow",
          "Italian queen, marked yellow"),
-        ("Hive #2 — Cedar fence", "East side, near cedar", "red",
+        ("Hive #2, Cedar fence", "East side, near cedar", "red",
          "2024 queen, watch for supersedure cells"),
-        ("Hive #3 — Vegetable garden", "By the tomato beds", "white",
+        ("Hive #3, Vegetable garden", "By the tomato beds", "white",
          "Newly split from Hive #1 in May"),
     ]
     for name, loc, marker, notes in samples:
@@ -478,7 +478,7 @@ def build_ui() -> gr.Blocks:
     _seed_sample_data()
 
     with gr.Blocks(title="Apiarist - Hive Frame Inspector") as app:
-        gr.Markdown("# 🐝 APIARIST")
+        gr.Markdown("# APIARIST")
         gr.Markdown(
             "*Offline AI inspector for honeybee hive frames. "
             "Built for the Build Small Hackathon.*"
@@ -486,7 +486,7 @@ def build_ui() -> gr.Blocks:
 
         with gr.Tabs():
             # ------- INSPECT TAB -------
-            with gr.Tab("🔍 Inspect"):
+            with gr.Tab(" Inspect"):
                 with gr.Row():
                     with gr.Column():
                         hive_input = gr.Dropdown(
@@ -501,7 +501,7 @@ def build_ui() -> gr.Blocks:
                             sources=["upload", "webcam"],
                         )
                         analyze_btn = gr.Button(
-                            "🔬 Analyze Frame", variant="primary"
+                            " Analyze Frame", variant="primary"
                         )
                     with gr.Column():
                         annotated_output = gr.Image(label="Annotated Frame")
@@ -510,7 +510,7 @@ def build_ui() -> gr.Blocks:
                             json_output = gr.Code(language="json")
 
             # ------- HIVES TAB -------
-            with gr.Tab("📋 Hives") as hives_tab:
+            with gr.Tab(" Hives") as hives_tab:
                 with gr.Row():
                     with gr.Column(scale=1):
                         gr.Markdown("### Add a Hive")
@@ -529,7 +529,7 @@ def build_ui() -> gr.Blocks:
                         new_notes = gr.Textbox(
                             label="Notes (optional)", lines=2
                         )
-                        add_btn = gr.Button("➕ Add Hive", variant="primary")
+                        add_btn = gr.Button(" Add Hive", variant="primary")
                         add_msg = gr.Markdown()
 
                     with gr.Column(scale=2):
@@ -544,7 +544,7 @@ def build_ui() -> gr.Blocks:
                             value=_hives_table_state(),
                             wrap=True,
                         )
-                        refresh_btn = gr.Button("🔄 Refresh")
+                        refresh_btn = gr.Button(" Refresh")
 
                 gr.Markdown("---\n### Inspection history")
                 history_select = gr.Dropdown(
@@ -565,20 +565,20 @@ def build_ui() -> gr.Blocks:
                 gr.Markdown("---\n### Weekly report")
                 with gr.Row():
                     report_btn = gr.Button(
-                        "📄 Generate PDF report", variant="primary"
+                        " Generate PDF report", variant="primary"
                     )
                     report_file = gr.File(
                         label="Latest report", interactive=False
                     )
 
             # ------- COMPARE TAB -------
-            with gr.Tab("⚖️ Compare"):
+            with gr.Tab(" Compare"):
                 gr.Markdown(
                     "### Apiarist vs raw generalist VLM\n"
                     "Same image, two pipelines. Apiarist combines a "
                     "**custom-trained YOLO specialist** with a generalist VLM. "
                     "The right column shows what happens when you ask the "
-                    "generalist alone — same model, no specialist."
+                    "generalist alone, same model, no specialist."
                 )
                 with gr.Row():
                     cmp_image = gr.Image(
@@ -587,33 +587,33 @@ def build_ui() -> gr.Blocks:
                         sources=["upload", "webcam"],
                     )
                     cmp_btn = gr.Button(
-                        "⚖️ Run Comparison", variant="primary", scale=0
+                        " Run Comparison", variant="primary", scale=0
                     )
 
                 with gr.Row():
                     with gr.Column():
-                        gr.Markdown("### 🐝 Apiarist (specialist + generalist)")
+                        gr.Markdown("### Apiarist (specialist + generalist)")
                         cmp_left_image = gr.Image(
                             label="Annotated by YOLO + Qwen"
                         )
                         cmp_left_text = gr.Markdown()
                     with gr.Column():
-                        gr.Markdown("### 🤖 Raw generalist (Qwen alone)")
+                        gr.Markdown("### Raw generalist (Qwen alone)")
                         cmp_right_image = gr.Image(label="No annotations")
                         cmp_right_text = gr.Markdown()
 
             # ------- ABOUT TAB -------
-            with gr.Tab("ℹ️ About"):
+            with gr.Tab("About"):
                 gr.Markdown(
                     """
-## 🐝 Apiarist
+## Apiarist
 
 An offline AI hive frame inspector for backyard beekeepers. Built in
 10 days for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon).
 
 ### The problem
 
-Frank — a beekeeper down the road — keeps 14 hives and inspects each
+Frank, a beekeeper down the road, keeps 14 hives and inspects each
 weekend by hand. Last summer he lost four colonies to queen failures
 he didn't spot in time. Apiarist is the assistant he should have had:
 fast, focused, runs on a laptop in a field, never sends his hive data
@@ -623,9 +623,9 @@ anywhere.
 
 Two models, two jobs:
 
-- **YOLOv8s, custom-trained** (22 MB) — finds bees, drones, queens, and
+- **YOLOv8s, custom-trained** (22 MB), finds bees, drones, queens, and
   varroa mites with bounding boxes. CPU-fast.
-- **Qwen2.5-VL-3B-Instruct** — takes the image + YOLO detection counts
+- **Qwen2.5-VL-3B-Instruct**, takes the image + YOLO detection counts
   and writes a narrative inspection report. Runs on ZeroGPU.
 
 Together: structured detection + narrative report in ~5 seconds, no
@@ -633,10 +633,10 @@ internet required.
 
 ### What's in the box
 
-- 🔍 **Inspect** — upload a frame, get an annotated image + report
-- 📋 **Hives** — registry, per-hive inspection history, weekly PDF reports
-- ⚖️ **Compare** — Apiarist vs raw generalist VLM, side-by-side
-- 📓 SQLite persistence for the whole apiary
+- **Inspect**, upload a frame, get an annotated image + report
+- **Hives**, registry, per-hive inspection history, weekly PDF reports
+- **Compare**, Apiarist vs raw generalist VLM, side-by-side
+- SQLite persistence for the whole apiary
 
 ### Stack
 
@@ -649,7 +649,7 @@ internet required.
 
 ### Badges chased
 
-🔌 Off the Grid · 🎯 Well-Tuned · 🎨 Off-Brand · 📡 Sharing is Caring · 📓 Field Notes
+ Off the Grid · Well-Tuned · Off-Brand · Sharing is Caring · Field Notes
 """
                 )
 

data/SOURCES.md

@@ -2,7 +2,7 @@
 
 The Apiarist model needs photos of **honeycomb frames** (the wooden rectangles
 bees build comb on), not just bees flying around. iNaturalist gives us a base
-of bee imagery but is light on hive-frame shots — we supplement from other
+of bee imagery but is light on hive-frame shots, we supplement from other
 sources below.
 
 ## 1. iNaturalist (auto-scraped)
@@ -17,7 +17,7 @@ Output: `data/raw/inaturalist/` + `metadata.jsonl`. Resumable.
 
 ## 2. Roboflow Universe (hand-search, often pre-labeled)
 
-Search these queries — many datasets ship with YOLO labels already.
+Search these queries, many datasets ship with YOLO labels already.
 
 - https://universe.roboflow.com/search?q=honeycomb
 - https://universe.roboflow.com/search?q=varroa+mite
@@ -66,8 +66,8 @@ Suggested channels (manual licensing review required):
 ## Labeling
 
 Use Roboflow's free tier (up to 1k images). Define 3 classes:
-- `queen` — bounding box around the queen
-- `mite` — small box per visible mite
-- `swarm_cell` — box around each peanut-shaped cell
+- `queen`, bounding box around the queen
+- `mite`, small box per visible mite
+- `swarm_cell`, box around each peanut-shaped cell
 
 Export as YOLOv8 format → drop into `data/processed/yolo/`.

db.py

@@ -2,7 +2,7 @@
 SQLite layer for hive registry + inspection history.
 
 Storage is ephemeral on HF Spaces' free tier (container restart wipes
-the filesystem) — that's fine for a hackathon demo. For real-world
+the filesystem), that's fine for a hackathon demo. For real-world
 deployment we'd back this with HF Hub persistence or a real DB.
 """
 

detector.py

@@ -3,7 +3,7 @@ YOLOv8 specialist detector for bees / drones / queens / pollen bees.
 
 Loads the custom-trained weights from weights/honey_bee_detector.pt if
 present. Degrades gracefully (no-op detections, original image) if the
-weights file is missing — useful while training is still in progress.
+weights file is missing, useful while training is still in progress.
 """
 
 from __future__ import annotations
@@ -88,7 +88,7 @@ def _try_load_yolo():
     try:
         from ultralytics import YOLO
         _yolo = YOLO(str(WEIGHTS_PATH))
-        # Force CPU — we run on the main container, not the ZeroGPU worker.
+        # Force CPU, we run on the main container, not the ZeroGPU worker.
         # ZeroGPU's CUDA emulation rejects torch.cuda calls outside @gpu.
         _yolo.to("cpu")
         print(f"[detector] loaded YOLO weights from {WEIGHTS_PATH} (cpu)",
@@ -122,11 +122,11 @@ _CLASS_ALIASES = {
 
 
 _PER_CLASS_CONF = {
-    "bee":       0.25,
-    "drone":     0.55,  # higher — drones false-positive on dark shapes (fingers, shadows)
-    "varroa":    0.30,  # mites are tiny; moderate threshold
+    "bee": 0.25,
+    "drone": 0.55, # higher, drones false-positive on dark shapes (fingers, shadows)
+    "varroa": 0.30, # mites are tiny; moderate threshold
     "pollenbee": 0.30,
-    "queen":     0.15,  # lower — try harder to find her even at lower confidence
+    "queen": 0.15, # lower, try harder to find her even at lower confidence
 }
 
 
@@ -177,11 +177,11 @@ def detect(
 # Per-class drawing config. RGB(A). Tuned for clarity over a busy
 # honeycomb background.
 _CLASS_STYLES = {
-    "bee":       {"color": (244, 163, 0),  "width": 1, "label": None},
-    "drone":     {"color": (255, 80, 80),  "width": 1, "label": None},
+    "bee": {"color": (244, 163, 0), "width": 1, "label": None},
+    "drone": {"color": (255, 80, 80), "width": 1, "label": None},
     "pollenbee": {"color": (255, 220, 80), "width": 1, "label": None},
-    "varroa":    {"color": (220, 50, 220), "width": 2, "label": "mite"},
-    "queen":     {"color": (50, 255, 100), "width": 4, "label": "QUEEN"},
+    "varroa": {"color": (220, 50, 220), "width": 2, "label": "mite"},
+    "queen": {"color": (50, 255, 100), "width": 4, "label": "QUEEN"},
 }
 
 

report.py

@@ -2,7 +2,7 @@
 PDF report generator for an apiary's weekly inspections.
 
 Pulls inspection rows from SQLite (db.py) and renders a clean one-page
-report per hive plus a summary page. Uses reportlab — pure Python, no
+report per hive plus a summary page. Uses reportlab, pure Python, no
 system deps, ships fine on HF Spaces.
 """
 
@@ -62,7 +62,7 @@ def _styles():
 
 def _date(epoch: float | None) -> str:
     if not epoch:
-        return "—"
+        return "-"
     return time.strftime("%Y-%m-%d %H:%M", time.localtime(epoch))
 
 
@@ -75,7 +75,7 @@ def _hive_table(inspections: list[dict]) -> Table:
                 "Y" if i["queen_detected"] else "N",
                 str(i["varroa_mites_visible"] or 0),
                 "Y" if i["swarm_cells_detected"] else "N",
-                i["frame_health"] or "—",
+                i["frame_health"] or "-",
                 (i["notes"] or "")[:80],
             ]
         )
@@ -115,7 +115,7 @@ def generate_report() -> bytes:
     )
 
     story = []
-    story.append(Paragraph("🐝 Apiarist Inspection Report", styles["ApiaristTitle"]))
+    story.append(Paragraph(" Apiarist Inspection Report", styles["ApiaristTitle"]))
     story.append(
         Paragraph(
             f"Generated {time.strftime('%Y-%m-%d %H:%M')}",

scripts/download_yolo_weights.py

@@ -1,7 +1,7 @@
 """
 Pull Matt Nudi's honey-bee/drone/queen YOLO weights from Roboflow once,
 then copy the .pt file into weights/ so the Space can load it locally
-with ultralytics — no Roboflow auth needed at runtime.
+with ultralytics, no Roboflow auth needed at runtime.
 
 Usage:
     py scripts/download_yolo_weights.py
@@ -70,9 +70,9 @@ def main() -> None:
             # Pick the most recently modified
             latest = max(pt_files, key=lambda p: p.stat().st_mtime)
             shutil.copy(latest, TARGET_PT)
-            print(f"\n✓ Copied weights from {latest}")
-            print(f"  to {TARGET_PT.resolve()}")
-            print(f"  size: {TARGET_PT.stat().st_size / 1024 / 1024:.1f} MB")
+            print(f"\n Copied weights from {latest}")
+            print(f" to {TARGET_PT.resolve()}")
+            print(f" size: {TARGET_PT.stat().st_size / 1024 / 1024:.1f} MB")
             return
         print("inference cache had no .pt files yet; falling back to dataset export.")
     except Exception as e:
@@ -86,8 +86,8 @@ def main() -> None:
     pt_files = list(Path(dataset.location).rglob("*.pt"))
     if pt_files:
         shutil.copy(pt_files[0], TARGET_PT)
-        print(f"\n✓ Copied weights to {TARGET_PT.resolve()}")
-        print(f"  size: {TARGET_PT.stat().st_size / 1024 / 1024:.1f} MB")
+        print(f"\n Copied weights to {TARGET_PT.resolve()}")
+        print(f" size: {TARGET_PT.stat().st_size / 1024 / 1024:.1f} MB")
         return
 
     raise SystemExit(

scripts/extract_dataset.py

@@ -41,7 +41,7 @@ def main() -> None:
         fail = 0
         for i, member in enumerate(members):
             if i % 500 == 0:
-                print(f"  progress: {i}/{total}  (ok={ok}, fail={fail})")
+                print(f" progress: {i}/{total} (ok={ok}, fail={fail})")
             target = DEST / member
             try:
                 target.parent.mkdir(parents=True, exist_ok=True)
@@ -58,7 +58,7 @@ def main() -> None:
             except Exception as e:
                 fail += 1
                 if fail <= 5:
-                    print(f"  [!] {member[:80]}... -> {type(e).__name__}: {e}")
+                    print(f" [!] {member[:80]}... -> {type(e).__name__}: {e}")
 
     print(f"\nDone. ok={ok}, fail={fail}")
 

scripts/push_dataset_to_hub.py

@@ -35,7 +35,7 @@ size_categories:
 # Apiarist iNaturalist bee photos
 
 Honey-bee (*Apis mellifera*) photos scraped from the iNaturalist API as
-training context for **Apiarist** — a fully-offline AI hive frame
+training context for **Apiarist**, a fully-offline AI hive frame
 inspector built for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon).
 
 ## Composition
@@ -44,8 +44,8 @@ inspector built for the [Build Small Hackathon](https://huggingface.co/build-sma
 - Most are forager bees on flowers; useful as bee context for VLM prompting
 
 ## Files
-- `images/` — JPEG photos, filenames are iNaturalist photo IDs
-- `metadata.jsonl` — one line per photo: license, observation ID, observed date, place, source URL
+- `images/`, JPEG photos, filenames are iNaturalist photo IDs
+- `metadata.jsonl`, one line per photo: license, observation ID, observed date, place, source URL
 
 ## License
 Each photo retains its original Creative Commons license (see `metadata.jsonl`).
@@ -116,7 +116,7 @@ def main() -> None:
     )
 
     print(
-        f"\n✓ Dataset live at: https://huggingface.co/datasets/{args.repo_id}"
+        f"\n Dataset live at: https://huggingface.co/datasets/{args.repo_id}"
     )
 
 

scripts/push_yolo_to_hub.py

@@ -37,7 +37,7 @@ pipeline_tag: object-detection
 # Apiarist Honey-Bee Detector (YOLOv8s)
 
 A custom-trained **YOLOv8s** specialist detector for honeycomb frame
-inspection. Built as part of [Apiarist](https://huggingface.co/spaces/build-small-hackathon/Apiarist) — a fully-offline AI hive inspector for
+inspection. Built as part of [Apiarist](https://huggingface.co/spaces/build-small-hackathon/Apiarist), a fully-offline AI hive inspector for
 backyard beekeepers, made for the
 [Build Small Hackathon](https://huggingface.co/build-small-hackathon).
 
@@ -46,10 +46,10 @@ backyard beekeepers, made for the
 Trained on the [hendricks_ricky bee-project](https://universe.roboflow.com/hendricks_ricky-hotmail-de/bee-project) dataset
 (3,308 labeled images, including 892 queen-bee examples). Four classes:
 
-- `Queen` — queen bee (larger, elongated abdomen)
-- `Worker` — worker / forager bees (the majority class)
-- `Drone` — male / drone bees
-- `Varroa` — varroa destructor mites visible on bees or comb
+- `Queen`, queen bee (larger, elongated abdomen)
+- `Worker`, worker / forager bees (the majority class)
+- `Drone`, male / drone bees
+- `Varroa`, varroa destructor mites visible on bees or comb
 
 ## Usage
 
@@ -71,9 +71,9 @@ frame photos):
 ```python
 PER_CLASS_CONF = {
     "Worker": 0.25,
-    "Drone":  0.55,  # higher — drones false-positive on fingers, shadows
-    "Varroa": 0.30,  # mites are small
-    "Queen":  0.15,  # lower — try harder to find her
+    "Drone": 0.55, # higher, drones false-positive on fingers, shadows
+    "Varroa": 0.30, # mites are small
+    "Queen": 0.15, # lower, try harder to find her
 }
 ```
 
@@ -91,7 +91,7 @@ PER_CLASS_CONF = {
 The training set includes ~892 queen images, which is far more than the
 typical bee-detection dataset but still limited compared to the
 millions of bees in worker class. Queen detection precision is
-moderate — when the model labels a queen, verify visually. Use as a
+moderate, when the model labels a queen, verify visually. Use as a
 **candidate flag**, not as ground truth.
 
 Varroa mite detection works on close-up frames where mites are visible

scripts/scrape_inaturalist.py

@@ -1,7 +1,7 @@
 """
 Scrape Apis mellifera (Western honeybee) photos from the iNaturalist API.
 
-Most observations are forager bees on flowers, not hive frames — but they
+Most observations are forager bees on flowers, not hive frames, but they
 still give Moondream2 plenty of bee imagery to ground its responses, and
 the high-quality bee close-ups are useful negatives / context for YOLO.
 
@@ -102,12 +102,12 @@ def main() -> None:
             try:
                 data = search_page(args.taxon, page, args.per_page)
             except Exception as e:
-                print(f"  [!] page {page} failed: {e}")
+                print(f" [!] page {page} failed: {e}")
                 time.sleep(2)
                 continue
 
             obs_list = data.get("results", [])
-            print(f"  {len(obs_list)} observations returned")
+            print(f" {len(obs_list)} observations returned")
 
             for obs in tqdm(obs_list, desc=f"page {page}", leave=False):
                 for photo in obs.get("photos", []):
@@ -125,7 +125,7 @@ def main() -> None:
                     try:
                         download_photo(url, dest)
                     except Exception as e:
-                        tqdm.write(f"  [!] photo {photo_id} failed: {e}")
+                        tqdm.write(f" [!] photo {photo_id} failed: {e}")
                         continue
 
                     rec = {
@@ -142,11 +142,11 @@ def main() -> None:
                     seen.add(photo_id)
                     total_new += 1
 
-            # iNaturalist asks for max 1 req/sec — be polite
+            # iNaturalist asks for max 1 req/sec, be polite
             time.sleep(1.5)
 
-    print(f"\n✓ Done. Downloaded {total_new} new photos this run.")
-    print(f"  Collection total: {len(seen)} photos in {output_dir}")
+    print(f"\n Done. Downloaded {total_new} new photos this run.")
+    print(f" Collection total: {len(seen)} photos in {output_dir}")
 
 
 if __name__ == "__main__":

scripts/train_yolo_on_modal.py

@@ -119,6 +119,6 @@ def main() -> None:
     print("=" * 60)
     print(
         "\nDownload locally with:\n"
-        f"  modal volume get {VOLUME_NAME} /apiarist/weights/best.pt "
+        f" modal volume get {VOLUME_NAME} /apiarist/weights/best.pt "
         f"weights/honey_bee_detector.pt"
     )