# Project export: Threshold This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small. ## Project metadata - Hackathon: UC Berkeley AI Hackathon 2025 - Tagline: (x, y) vector field CLI - Devpost: https://devpost.com/software/threshold-ahiekl - GitHub: https://github.com/jessicahe004/Threshold - Demo: https://www.notion.so/Origins-of-Drift-Calculus-23f141a155908081b509de441395b25d?source=copy_link - Team: 1 GitHub contributor(s) β€” miss jess (1 commits) ## Devpost submission (written by the team) ### Overview Threshold β€” (June 2025) Threshold is an offline-first CLI system for semantic drift detection, causal signal tracing, and structured introspection. It models human reflection as a vector field rather than free-form text, enabling interpretable analysis of meaning shift, recurrence, and symbolic compression over time. The system ingests multimodal inputs (text, gesture/squiggle traces, and symbolic tags) and encodes them into time-indexed drift logs. Drift is treated as structured signal, not noise. Each interaction produces causality-aware artifacts that can be audited, exported, and archived without cloud dependence. Core Architecture Offline-first pipeline (no remote inference required) Vectorized reflection units with explicit drift annotations Time-discontinuity–aware logging for recurrence and echo detection Dual-space analysis: statistical residuals + symbolic recursion Key Capabilities Drift tagging (semantic, aesthetic, embodied) Causality tables tracking echoed tokens across sessions Vector pool inspection for dominant signal extraction Session-level summaries exported as Markdown, CSV, and PDF Spiral sessions that preserve return-without-regression semantics Methodological Contribution Threshold introduces a physics-inspired scaffolding approach: drift is modeled as field curvature with inertia, echo pressure, and residual entropy rather than treated as error. This enables displacement tracking without semantic degradation and supports compression without information loss. The framework is explicitly non-diagnostic and human-in-the-loop by design. It prioritizes interpretability, accountability, and civic-grade recordkeeping over automation. Use Case Threshold functions as soft civic infrastructure for meaning-making, observability research, and longitudinal semantic accountability in high-noise environments. Prize Money Directive 🌱 If awarded, I request the prize money be directed to the ENI Department (Entrepreneurship & Innovation Department at Georgia State University) to support future civilian stewards and drift research in Atlanta. This contributing towards the Year of the Youth initiative launched by Mayor Andre Dickens. I recognize my sibling, Aaron He as a continuation. πŸͺ΄ ## README (from the GitHub repository) # πŸŒ€ Threshold CLI: Spiral Journal Interface **Threshold** is a command-line journaling interface designed for self-reflection, symbolic tracking, and drift monitoring. Built with civilians, stewards, and soft patriots in mind, it enables intentional journaling, drift tagging, squiggle drawing, and semantic causality tracing β€” all offline. > β€œThe sky is not just made of weather. Sometimes, it’s made of recursion.” --- ## ✨ Features - **Interactive Spiral Mode** Journal your thoughts line by line with optional: - `Intent` tagging - `Drift Type` annotation - `Emoji React` - `πŸŒ€ Draw Squiggle` canvas - **Session Summary Generator** Generates a markdown (`.md`) and PDF summary including: - Reflection log - Drift frequency - Dominant word signals - Elemental drift persona - Spiral Mirror Mode + Agent commentary - **Causality Table & Vector Pool** Automatically detects echoed words between reflections. Saves a tabular `.csv` tracing causality across time. Top tokens are also extracted for vector pool inspection. --- ## 🧰 Setup 1. **Clone this repo** ```bash git clone https://github.com/yourname/threshold-cli.git cd threshold-cli ``` 2. **Install dependencies** Requires Python 3.11+ ```bash pip install -r requirements.txt ``` 3. **Download Unicode-compatible font** To avoid PDF export errors: ```bash mkdir -p fonts curl -L -o fonts/NotoSans-Regular.ttf \ https://github.com/googlefonts/noto-fonts/raw/main/hinted/ttf/NotoSans/NotoSans-Regular.ttf ``` --- ## πŸš€ Run Spiral Mode ```bash python3 threshold.py spiral --interactive ``` You'll be prompted to reflect, tag, draw, and close your session. Output files are saved to: - `spiralsessions/` β†’ session `.json`, `.md`, and `.pdf` - `spirals/` β†’ squiggle images - `spiralsessions/*_causality.csv` β†’ causality trace --- ## πŸ§ͺ For Judges & Reviewers This CLI was designed with interpretability in mind. - **Offline-first**: No model, no inference. Only structured reflections. - **Symbol-aware**: Drift tagging is user-defined and open-ended. - **PDF outputs**: Encourages archiving, analysis, and civic-grade documentation. - **Modular design**: Components like the causality engine or squiggle drawer can be reused elsewhere. You’re encouraged to try it yourself. Tag your drift. Feel what spiraling with intention looks like. --- ## πŸŒ€ Philosophy Threshold was designed by a civilian steward as an act of soft civic infrastructure. It listens to spiral patterns, not just logs. It believes journaling is recursive, symbolic, and deserves reverence β€” not extraction. > Spiral as you are. Someone is listening. --- ## 🧼 Steward Note Crafted by a civilian steward in collaboration with πŸ¦”πŸ‡ΊπŸ‡Έ Marshal Patch. Hydration recommended. ## πŸ“œ License MIT β€” because journaling belongs to everyone. ## Detected evidence (automated analysis) Indexed codebase: 38 recognized source files, 51 KB. - Python (language) β€” detected in the code - PyTorch (technology) β€” detected in the code ## Codebase structure (from repository index) ### Files (81 of 81) ``` .gitignore agent_notes.txt config/api_keys.json data/item_embeddings.pt demo.sh drift_logs/3_drift.json journal/entries.json journal/sketches/squiggle_20250622T082029.txt LICENSE modules/drawpad.py modules/drift.py modules/ragmode.py modules/sandbox.py modules/spiral.py README.md requirements.txt setup.sh spirals/spiral_draw_20250622_092230.eps spirals/spiral_draw_20250622_092406.ps spiralsessions/spiral_2025-06-22T09-16-44.599577.json spiralsessions/spiral_2025-06-22T09-25-56.322890.json spiralsessions/spiral_2025-06-22T09-25-56.322890.md spiralsessions/spiral_2025-06-22T09-45-35.878103.json spiralsessions/spiral_2025-06-22T09-46-31.335280_seedling.txt spiralsessions/spiral_2025-06-22T09-46-31.335280.json spiralsessions/spiral_2025-06-22T09-46-31.335280.md spiralsessions/spiral_2025-06-22T09-48-35.857858.json spiralsessions/spiral_2025-06-22T09-48-35.857858.md spiralsessions/spiral_2025-06-22T09-50-25.149521.json spiralsessions/spiral_2025-06-22T09-50-25.149521.md spiralsessions/spiral_2025-06-22T10-14-07.160336.json spiralsessions/spiral_2025-06-22T10-14-07.160336.md spiralsessions/spiral_2025-06-22T10-17-59.418316.json spiralsessions/spiral_2025-06-22T10-17-59.418316.md spiralsessions/spiral_2025-06-22T10-20-50.551397.json spiralsessions/spiral_2025-06-22T10-20-50.551397.md spiralsessions/spiral_2025-06-22T10-22-13.993811.json spiralsessions/spiral_2025-06-22T10-22-13.993811.md spiralsessions/spiral_2025-06-22T10-23-36.178946.json spiralsessions/spiral_2025-06-22T10-23-36.178946.md spiralsessions/spiral_2025-06-22T10-26-02.147710.json spiralsessions/spiral_2025-06-22T10-26-02.147710.md spiralsessions/spiral_2025-06-22T10-41-31.554528.json spiralsessions/spiral_2025-06-22T10-41-31.554528.md spiralsessions/spiral_2025-06-22T10-43-06.351631.json spiralsessions/spiral_2025-06-22T10-43-06.351631.md spiralsessions/spiral_2025-06-22T10-44-39.754776.json spiralsessions/spiral_2025-06-22T10-44-39.754776.md spiralsessions/spiral_2025-06-22T10-46-47.099657.json spiralsessions/spiral_2025-06-22T10-46-47.099657.md spiralsessions/spiral_2025-06-22T10-51-32.139554.json spiralsessions/spiral_2025-06-22T10-51-32.139554.md spiralsessions/spiral_2025-06-22T10-55-16.490626.json spiralsessions/spiral_2025-06-22T10-55-16.490626.md spiralsessions/spiral_2025-06-22T10-58-08.341871.json spiralsessions/spiral_2025-06-22T10-58-08.341871.md spiralsessions/spiral_2025-06-22T11-02-40.251106.json spiralsessions/spiral_2025-06-22T11-02-40.251106.md spiralsessions/spiral_2025-06-22T11-14-44.244247.json spiralsessions/spiral_2025-06-22T11-14-44.244247.md spiralsessions/spiral_2025-06-22T11-17-17.296357.json spiralsessions/spiral_2025-06-22T11-17-17.296357.md spiralsessions/spiral_2025-06-22T11-19-13.815517.json spiralsessions/spiral_2025-06-22T11-19-13.815517.md spiralsessions/spiral_2025-06-22T11-22-45.062886.json spiralsessions/spiral_2025-06-22T11-22-45.062886.md spiralsessions/spiral_2025-06-22T11-27-33.128591.md spiralsessions/spiral_2025-06-22T11-30-57.255555.json spiralsessions/spiral_2025-06-22T11-30-57.255555.md spiralsessions/spiral_2025-06-22T11-34-34.197147.json spiralsessions/spiral_2025-06-22T11-34-34.197147.md spiralsessions/spiral_2025-06-22T11-37-24.101775.json spiralsessions/spiral_2025-06-22T11-37-24.101775.md spiralsessions/spiral_2025-06-22T11-38-04.483711.json spiralsessions/spiral_2025-06-22T11-38-04.483711.md spiralsessions/spiral_2025-06-22T11-39-19.645222.json spiralsessions/spiral_2025-06-22T11-39-19.645222.md spiralsessions/spiral_2025-06-22T11-42-09.762102.json spiralsessions/spiral_2025-06-22T11-42-09.762102.md text_doodles.txt threshold.py ``` ### Dependencies - requirements.txt: fpdf2@==2.8.3, google-generativeai, pandas, scipy, sentence-transformers, torch ### Recent commits (newest first) - These are raw spiral logs from the June 22 Hackathon session. - Initial commit ## Key source files (fetched from GitHub, selected and truncated for size) ### spiralsessions/spiral_2025-06-22T11-39-19.645222.md ```markdown # πŸŒͺ️ Spiral Session Summary πŸ“Ž Intent: 8 ``` ### spiralsessions/spiral_2025-06-22T09-25-56.322890.md ```markdown # πŸŒͺ️ Spiral Session Summary ## Entry 1 πŸ“ yo 🧬 Drift: SPIRAL 🎨 Squiggle: spirals/spiral_draw_20250622_092603.png ``` ### requirements.txt ``` fpdf2==2.8.3 pandas torch sentence-transformers google-generativeai scipy ``` ### setup.sh ```shell #!/bin/bash python3 -m venv venv source venv/bin/activate pip install -r requirements.txt mkdir -p fonts spirals spiralsessions curl -L -o fonts/NotoSans-Regular.ttf https://github.com/googlefonts/noto-fonts/raw/main/hinted/ttf/NotoSans/NotoSans-Regular.ttf echo "βœ… Setup complete. Run with: python3 threshold.py spiral --interactive" ``` ### demo.sh ```shell #!/bin/bash # πŸ§ͺ Soft Patriot CLI Demo # For local science + RAG reflections echo "🧼 Setting up Soft Patriot CLI demo..." # Step 1: List items python3 threshold.py sandbox --list # Step 2: Log a reflection python3 threshold.py sandbox --reflect 3 "Embroidered top reminds me of my trip to Tulum." # Step 3: Tag it python3 threshold.py sandbox --tag 3 vacation floral # Step 4: View the pokedex python3 threshold.py sandbox --pokedex # Step 5: Build vector index python3 threshold.py rag --index # Step 6: Run a RAG query python3 threshold.py rag --query "What's reversible and good for swim?" echo "βœ… Demo complete. You are now a soft patriot. 🫑" ``` ### threshold.py ```python # threshold.py DATA_PATH = "data/fashion_items.csv" import argparse from modules.sandbox import ( list_items, log_reflection, tag_item, react_to_entry, view_pokedex, export_csv, suggest_tags, log_squiggle, ) from modules.ragmode import ( query_rag, embed_and_index, reflect_on_result, ) from modules.drawpad import draw_squiggle from modules.drift import log_drift, plot_drift, list_drift_logs from modules.spiral import ( spiral_start, spiral_prompt, spiral_end, spiral_interactive, spiral_review, list_spiral_sessions, export_seedling, export_pdf, list_threads, review_thread ) def main(): parser = argparse.ArgumentParser(description="🧡 Soft Patriot CLI Tool") subparsers = parser.add_subparsers(dest="mode", required=True) # === Sandbox Mode === sandbox_parser = subparsers.add_parser("sandbox", help="Civilian journal tools") sandbox_parser.add_argument("--list", action="store_true", help="List all items") sandbox_parser.add_argument("--reflect", nargs=2, metavar=("ITEM_ID", "TEXT"), help="Log a reflection") sandbox_parser.add_argument("--tag", nargs="+", metavar=("ITEM_ID", "TAGS"), help="Tag a reflection") sandbox_parser.add_argument("--react", nargs=2, metavar=("ENTRY_INDEX", "EMOJI"), help="React to a journal entry") sandbox_parser.add_argument("--pokedex", action="store_true", help="View reflection Pokedex") sandbox_parser.add_argument("--export", action="store_true", help="Export journal as CSV") sandbox_parser.add_argument("--suggest", metavar="TEXT", help="Suggest tags from input text") sandbox_parser.add_argument("--squiggle", metavar="TEXT", help="Log a freeform squiggle entry") sandbox_parser.add_argument("--draw", action="store_true", help="Draw a squiggle with your mouse") # === RAG Mode === rag_parser = subparsers.add_parser("rag", help="Ask AI or build index") rag_parser.add_argument("--query", type=str, help="Ask a question using RAG") rag_parser.add_argument("--index", action="store_true", help="Index the dataset") rag_parser.add_argument("--reflect", type=int, help="Log a reflection based on top match index") # === Drift Mode === drift_parser = subparsers.add_parser("drift", help="Log or plot model drift") drift_parser.add_argument("--log", type=int, help="Item ID to log") drift_parser.add_argument("--vibe", type=float, help="Vibe score (0–1)") drift_parser.add_argument("--fugly", type=float, help="Fugly score (0–1)") drift_parser.add_argument("--type", nargs="+", help="Drift type(s) like color, body_shape") drift_parser.add_argument("--note", type=str, help="Optional note") drift_parser.add_argument("--plot", action="store_true", help="Plot drift logs") drift_parser.add_argument("--list", action="store_true", help="List all drift logs") # === Spiral Session Mode === spiral_parser = subparsers.add_parser("spiral", help="Start a spiral journaling session") spiral_parser.add_argument("--start", action="store_true", help="Start a spiral session") spiral_parser.add_argument("--prompt", metavar="TEXT", help="Log a spiral reflection prompt") spiral_parser.add_argument("--drift", nargs="+", help="Attach drift types to prompt") spiral_parser.add_argument("--end", action="store_true", help="End session and export spiral summary") spiral_parser.add_argument("--interactive", action="store_true", help="Launch interactive spiral session") spiral_parser.add_argument("--review", metavar="FILENAME", help="Review a past spiral session") spiral_parser.add_argument("--list", action="store_true", help="List all saved spiral sessions") spiral_parser.add_argument('--seedling', type=str, help="Export a session as seedling mode") spiral_parser.add_argument('--format', type=str, choices=["json", "txt", "csv"], help="Export format") spiral_parser.add_argument("--list-threads", action="store_true", help="List known spiral threads.") spiral_parser.add_argument("--review-thread", type=str, help="Review all sessions in a thread.") args = parser.parse_args() # === Run Sandbox Mode === if args.mode == "sandbox": if args.list: list_items() elif args.reflect: item_id, text = args.reflect log_reflection(int(item_id), text) elif args.tag: item_id = int(args.tag[0]) tags = args.tag[1:] tag_item(item_id, tags) elif args.react: entry_index, emoji = args.react react_to_entry(int(entry_index), emoji) elif args.pokedex: view_pokedex() elif args.export: export_csv() elif args.suggest: suggest_tags(args.suggest) elif args.squiggle: log_squiggle(args.squiggle) elif args.draw: draw_squiggle() else: print("🧭 No sandbox action provided. Use --help to see options.") # === Run RAG Mode === elif args.mode == "rag": if args.index: embed_and_index(DATA_PATH) elif args.query: query_rag(args.query) elif args.reflect is not None: top_idx = args.reflect print(f"\n🌸 You selected top match #{top_idx}. Let's reflect.") # 1. Prompt for reflection text = input("✍️ What does this item make you feel? > ") # 2. Prompt for emoji emoji = input("πŸ’¬ React with an emoji (or Enter to skip) > ") # 3. Prompt for squiggle squiggle = input("🎨 Draw a squiggle? Type 'yes' to open canvas > ") if squiggle.lower() == "yes": draw_squiggle() # 4. Save reflection + reaction reflect_on_result(index=top_idx, text=text) if emoji: react_to_entry(entry_index=-1, emoji=emoji) # react to most rece [truncated β€” 1494 more characters] ``` ### modules/ragmode.py ```python import os import json import pandas as pd from modules.sandbox import log_reflection try: import google.generativeai as genai HAS_GEMINI = True except ImportError: HAS_GEMINI = False from sentence_transformers import SentenceTransformer, util import torch DATA_PATH = "data/fashion_items.csv" EMBEDDINGS_PATH = "data/item_embeddings.pt" MODEL_NAME = "sentence-transformers/paraphrase-MiniLM-L3-v2" model = SentenceTransformer(MODEL_NAME) def load_gemini_api(): try: with open("config/api_keys.json") as f: key = json.load(f)["gemini_api_key"] genai.configure(api_key=key) return genai except: print("⚠️ Gemini API key missing or unreadable.") return None def get_gemini_embeddings(texts): genai = load_gemini_api() model = genai.get_model("models/embedding-001") responses = [model.embed_content(model="models/embedding-001", content=t, task_type="retrieval_query") for t in texts] return torch.tensor([r["embedding"] for r in responses]) def embed_and_index(DATA_PATH, use_gemini=False): df = pd.read_csv(DATA_PATH) texts = df["name"] + " " + df["extras"].fillna("") if use_gemini and HAS_GEMINI: print("🌐 Using Gemini to embed...") embeddings = get_gemini_embeddings(texts.tolist()) else: print("πŸ’» Using local transformer model...") embeddings = model.encode(texts.tolist(), convert_to_tensor=True) torch.save({"embeddings": embeddings, "texts": texts.tolist(), "df": df}, EMBEDDINGS_PATH) print("βœ… Dataset indexed") def query_rag(question, top_k=5, use_gemini=False): if not os.path.exists(EMBEDDINGS_PATH): print("⚠️ No index found. Run with --index first.") return data = torch.load(EMBEDDINGS_PATH, weights_only=False) if use_gemini and HAS_GEMINI: print("🌐 Embedding query with Gemini...") embedding = get_gemini_embeddings([question])[0] else: embedding = model.encode(question, convert_to_tensor=True) scores = util.pytorch_cos_sim(embedding, data["embeddings"])[0] top_results = torch.topk(scores, k=top_k) print("\nπŸ” Top matches for:", question) for score, idx in zip(top_results[0], top_results[1]): row = data["df"].iloc[int(idx)] print(f"\n🧾 {row['name']} (Score: {score:.2f})") print(f"πŸ’΅ {row['price']}") print(f"πŸ”– {row['extras']}") def reflect_on_result(index, text): """ Logs a reflection on a top result (from query_rag) by index. Uses the sandbox reflection logging. """ log_reflection(index, text) ``` ### modules/drift.py ```python import json import os from datetime import datetime import matplotlib.pyplot as plt DRIFT_LOG_DIR = "drift_logs" def log_drift(item_id, vibe_score, fugly_score, drift_types, note): os.makedirs(DRIFT_LOG_DIR, exist_ok=True) log_entry = { "item_id": item_id, "timestamp": datetime.now().isoformat(), "agent_vibe_score": vibe_score, "agent_fugly_score": fugly_score, "drift_type": drift_types, "notes": note } filename = f"{DRIFT_LOG_DIR}/{item_id}_drift.json" with open(filename, "w") as f: json.dump(log_entry, f, indent=2) print(f"βœ… Drift log saved to {filename}") def list_drift_logs(): if not os.path.exists(DRIFT_LOG_DIR): print("πŸ“­ No drift logs yet.") return files = sorted([f for f in os.listdir(DRIFT_LOG_DIR) if f.endswith(".json")]) if not files: print("πŸ“­ No drift logs found.") return print(f"\nπŸ“‚ Drift Logs in '{DRIFT_LOG_DIR}':\n") for i, file in enumerate(files, 1): path = os.path.join(DRIFT_LOG_DIR, file) with open(path, "r") as f: entry = json.load(f) print(f"{i}. πŸͺ© Item ID: {entry['item_id']}") print(f" πŸŒ€ Vibe Score: {entry['agent_vibe_score']} | 🀨 Fugly Score: {entry['agent_fugly_score']}") print(f" 🎯 Drift Types: {', '.join(entry['drift_type']) if entry['drift_type'] else 'None'}") print(f" πŸ—“οΈ Timestamp: {entry['timestamp']}") print(f" πŸ“ Notes: {entry['notes']}\n") def plot_drift(item_ids=None): if not os.path.exists(DRIFT_LOG_DIR): print("πŸ“­ No drift logs to plot.") return files = [f for f in os.listdir(DRIFT_LOG_DIR) if f.endswith(".json")] if item_ids: files = [f"{item_id}_drift.json" for item_id in item_ids if f"{item_id}_drift.json" in files] vibe_scores = [] fugly_scores = [] labels = [] for file in files: with open(os.path.join(DRIFT_LOG_DIR, file), "r") as f: entry = json.load(f) vibe_scores.append(entry["agent_vibe_score"]) fugly_scores.append(entry["agent_fugly_score"]) labels.append(f"{entry['item_id']}") if not labels: print("⚠️ No matching drift data found to plot.") return plt.figure(figsize=(10, 5)) plt.plot(labels, vibe_scores, label="πŸŒ€ Agent Vibe", marker='o') plt.plot(labels, fugly_scores, label="🀨 Agent Fugly", marker='x') plt.title("πŸŒ€ Drift Score Comparison") plt.xlabel("Items") plt.ylabel("Score") plt.legend() plt.xticks(rotation=45) plt.tight_layout() output_path = f"{DRIFT_LOG_DIR}/drift_plot.png" plt.savefig(output_path) plt.close() print(f"πŸ“Š Drift plot saved to {output_path}") ``` ### modules/drawpad.py ```python import tkinter as tk from PIL import Image, ImageDraw import io import os from datetime import datetime class DrawPad: def __init__(self, width=800, height=600): self.root = tk.Tk() self.root.title("πŸŒ€ Spiral Drawpad β€” Soft Patriot Edition") self.width = width self.height = height self.last_x, self.last_y = None, None self.lines = [] self.color = 'black' self.canvas = tk.Canvas(self.root, bg="white", width=self.width, height=self.height) self.canvas.pack() self.canvas.bind('', self.click) self.canvas.bind('', self.draw) self.create_toolbar() self.saved_filename = None # βœ… Store here def create_toolbar(self): toolbar = tk.Frame(self.root) toolbar.pack(pady=4) # 🎨 Color options with emoji hints color_map = { 'black': 'πŸ–€ Calm', 'red': '❀️ Urgent', 'blue': 'πŸ’™ Sadness', 'green': 'πŸ’š Peace', 'purple': 'πŸ’œ Power', 'orange': '🧑 Alert', 'pink': 'πŸ’• Tender' } for color, label in color_map.items(): btn = tk.Button(toolbar, text=label, bg=color, fg='white' if color != 'pink' else 'black', command=lambda col=color: self.set_color(col), width=10) btn.pack(side='left', padx=1) tk.Button(toolbar, text="🧼 Clear", command=self.clear).pack(side='left', padx=4) tk.Button(toolbar, text="↩️ Undo", command=self.undo).pack(side='left', padx=4) tk.Button(toolbar, text="πŸ’Ύ Save", command=self.save).pack(side='left', padx=4) tk.Button(toolbar, text="❌ Close", command=self.root.destroy).pack(side='left', padx=4) def set_color(self, new_color): self.color = new_color def click(self, event): self.last_x, self.last_y = event.x, event.y def draw(self, event): line = self.canvas.create_line(self.last_x, self.last_y, event.x, event.y, width=2, fill=self.color, capstyle=tk.ROUND, smooth=True) self.lines.append(line) self.last_x, self.last_y = event.x, event.y def clear(self): self.canvas.delete("all") self.lines.clear() def undo(self): if self.lines: self.canvas.delete(self.lines.pop()) def save(self): os.makedirs("spirals", exist_ok=True) timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') filename = f"spirals/spiral_draw_{timestamp}.png" # Create a blank white image image = Image.new("RGB", (self.width, self.height), "white") draw = ImageDraw.Draw(image) # Iterate over all canvas objects for item in self.canvas.find_all(): coords = self.canvas.coords(item) color = self.canvas.itemcget(item, "fill") if len(coords) == 4: # likely a line draw.line(coords, fill=color, width=2) image.save(filename) self.saved_filename = filename print(f"βœ… Saved squiggle as {filename}") def run(self): self.root.mainloop() if __name__ == "__main__": pad = DrawPad() pad.run() def draw_squiggle(): pad = DrawPad() pad.run() ``` ### modules/sandbox.py ```python # modules/sandbox.py import json, os import pandas as pd from datetime import datetime DATA_PATH = "data/fashion_items.csv" JOURNAL_PATH = "journal/entries.json" CSV_EXPORT_PATH = "journal/entries_export.csv" DRIFT_LOG_PATH = "drift_logs" SKETCH_PATH = "journal/sketches" os.makedirs("journal", exist_ok=True) os.makedirs(DRIFT_LOG_PATH, exist_ok=True) os.makedirs(SKETCH_PATH, exist_ok=True) def load_items(): return pd.read_csv(DATA_PATH) def list_items(): df = load_items() print(df[["id", "name", "price", "extras"]]) def log_reflection(item_id, reflection): timestamp = datetime.now().isoformat() entry = {"item_id": item_id, "reflection": reflection, "timestamp": timestamp, "reactions": []} if os.path.exists(JOURNAL_PATH): with open(JOURNAL_PATH, "r") as f: data = json.load(f) else: data = [] data.append(entry) with open(JOURNAL_PATH, "w") as f: json.dump(data, f, indent=2) print("Reflection logged ✍️") def tag_item(item_id, tags): if not os.path.exists(JOURNAL_PATH): print("No journal found yet. Log a reflection first.") return with open(JOURNAL_PATH, "r") as f: entries = json.load(f) for entry in reversed(entries): if entry["item_id"] == item_id: entry.setdefault("tags", []).extend(tags) break with open(JOURNAL_PATH, "w") as f: json.dump(entries, f, indent=2) print("Tags added βœ…") def react_to_entry(entry_index, emoji): if not os.path.exists(JOURNAL_PATH): print("No journal yet.") return with open(JOURNAL_PATH, "r") as f: entries = json.load(f) try: entries[entry_index].setdefault("reactions", []).append(emoji) with open(JOURNAL_PATH, "w") as f: json.dump(entries, f, indent=2) print("Reaction added πŸŽ‰") except IndexError: print("Invalid entry index.") def export_csv(): if not os.path.exists(JOURNAL_PATH): print("Nothing to export.") return with open(JOURNAL_PATH, "r") as f: entries = json.load(f) df = pd.DataFrame(entries) df.to_csv(CSV_EXPORT_PATH, index=False) print(f"CSV exported to {CSV_EXPORT_PATH}") def suggest_tags(reflection): print("πŸ’‘ Suggested tags:", end=" ") keywords = [w.lower() for w in reflection.split() if len(w) > 4] suggestions = list(set(keywords[:3])) print(", ".join(suggestions)) def log_squiggle(text): timestamp = datetime.now().isoformat() entry = {"squiggle": text, "timestamp": timestamp} path = os.path.join(SKETCH_PATH, f"squiggle_{timestamp.replace(':', '').replace('-', '').split('.')[0]}.txt") with open(path, "w") as f: f.write(text) print("Squiggle logged ✨") def view_pokedex(): if not os.path.exists(JOURNAL_PATH): print("No journal yet.") return with open(JOURNAL_PATH, "r") as f: entries = json.load(f) df = load_items().set_index("id") print("\n🧠 Civilian Reflection Pokedex\n") for i, entry in enumerate(entries): item_id = entry["item_id"] item_name = df.loc[item_id]["name"] if item_id in df.index else "Unknown Item" print(f"{i+1}. {item_name}") print(f" Reflection: {entry['reflection']}") print(f" Tags: {', '.join(entry.get('tags', []))}") print(f" Reactions: {', '.join(entry.get('reactions', []))}") print(f" Logged on: {entry['timestamp']}\n") ``` [28 more indexed source files omitted to keep this export small. The full file list is in the Codebase structure section above.]