diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000000000000000000000000000000000000..84023d9fbe5519c9b714306b80f8243a77a42c0c --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,131 @@ +# WHY GitHub Actions CI: catches broken imports, TypeScript errors, and test +# failures before they reach HuggingFace Spaces. Runs on every push to main. +name: CI + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Detect C sources + id: detect-c + run: | + if find . -name "*.c" -o -name "*.h" | grep -q .; then + echo "present=true" >> "$GITHUB_OUTPUT" + else + echo "present=false" >> "$GITHUB_OUTPUT" + fi + + - name: Install lcov + run: sudo apt-get update && sudo apt-get install -y lcov + + - name: Install clang-format (if needed) + if: steps.detect-c.outputs.present == 'true' + run: sudo apt-get install -y clang-format + + - name: Set up Python 3.12 + id: setup-python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Cache pip + uses: actions/cache@v4 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('requirements.txt', 'requirements-dev.txt') }} + restore-keys: | + ${{ runner.os }}-pip-${{ steps.setup-python.outputs.python-version }}- + ${{ runner.os }}-pip- + + - name: Install Python dependencies + run: pip install -r requirements.txt -r requirements-dev.txt + + - name: Python lint (ruff) + run: ruff check app/ tests/ + + - name: C style (clang-format) + if: steps.detect-c.outputs.present == 'true' + run: | + mapfile -d '' files < <(find . -name "*.c" -o -name "*.h" -print0) + clang-format --version + printf '%s\0' "${files[@]}" | xargs -0 clang-format --dry-run --Werror + + - name: Check all Python modules import cleanly + run: | + python -c "from app.model import ToxicityClassifier" + python -c "from app.database import get_recent_posts, save_post, seed_if_empty" + python -c "from app.graph import build_cooccurrence_graph" + python -c "from app.ingestion import ALGOSPEAK_QUERIES" + python -c "from app.main import app" + + - name: Run tests with coverage + env: + BLUESKY_HANDLE: "test@test.com" + BLUESKY_PASSWORD: "testpassword" + PYTHONPATH: . + run: | + python -m pytest tests/ -v --tb=short --cov=app --cov-report=xml + + # ── Frontend build verification ────────────────────────────────────────── + # WHY build React in CI: catches TypeScript errors and missing imports + # before they cause a silent failure during Docker build on HuggingFace. + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + + - name: Install pnpm + run: npm install -g pnpm + + - name: Cache pnpm store + uses: actions/cache@v4 + with: + path: ~/.local/share/pnpm/store + key: ${{ runner.os }}-pnpm-${{ hashFiles('frontend/pnpm-lock.yaml') }} + restore-keys: | + ${{ runner.os }}-pnpm- + + - name: Install frontend dependencies + working-directory: frontend + run: pnpm install --frozen-lockfile + + - name: Build frontend (verify no TypeScript errors) + working-directory: frontend + run: pnpm build + + - name: Collect lcov coverage + id: lcov + run: | + if find . -name "*.gcda" -o -name "*.gcno" | grep -q .; then + lcov --capture --directory . --output-file coverage.info --ignore-errors unused --no-external + echo "generated=true" >> "$GITHUB_OUTPUT" + else + echo "No gcda/gcno files found; skipping lcov capture." + echo "generated=false" >> "$GITHUB_OUTPUT" + fi + + - name: Upload Python coverage to Codecov + uses: codecov/codecov-action@v4 + with: + files: ./coverage.xml + token: ${{ secrets.CODECOV_TOKEN }} + flags: python + fail_ci_if_error: false + + - name: Upload C coverage to Codecov + if: steps.lcov.outputs.generated == 'true' + uses: codecov/codecov-action@v4 + with: + files: ./coverage.info + token: ${{ secrets.CODECOV_TOKEN }} + flags: c + fail_ci_if_error: false diff --git a/.github/workflows/hf-deploy.yml b/.github/workflows/hf-deploy.yml new file mode 100644 index 0000000000000000000000000000000000000000..761a962cfffcca217070faf683830d95f9edaa79 --- /dev/null +++ b/.github/workflows/hf-deploy.yml @@ -0,0 +1,53 @@ +name: Deploy to HuggingFace Spaces + +on: + push: + branches: [main] + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + + - name: Install pnpm + run: npm install -g pnpm + + - name: Build React frontend + working-directory: frontend + run: | + pnpm install --frozen-lockfile + pnpm build + + - name: Push to HuggingFace Space + env: + HF_TOKEN: ${{ secrets.HF_TOKEN }} + run: | + git config --global user.email "deploy@github-actions.com" + git config --global user.name "GitHub Actions" + + git checkout --orphan hf-deploy + + git rm -rf assets/ 2>/dev/null || true + git rm -r --cached frontend/src/ 2>/dev/null || true + git rm -r --cached frontend/node_modules/ 2>/dev/null || true + git rm -r --cached frontend/public/ 2>/dev/null || true + + cp README_HF.md README.md + git rm --cached README_HF.md 2>/dev/null || true + rm README_HF.md + + git add -f frontend/dist/ + git add . + + git commit -m "Deploy to HuggingFace Spaces" + git remote add space https://odeliyach:$HF_TOKEN@huggingface.co/spaces/odeliyach/Algoscope + git push space hf-deploy:main --force diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d2249e0ab2f7b868385d4b494a0f4dff13d21e65 --- /dev/null +++ b/.gitignore @@ -0,0 +1,38 @@ +# Credentials — NEVER commit +.env +.env.local +.env.*.local + +# Database — contains scraped user data +algoscope.db +*.db + +# Python +__pycache__/ +*.py[cod] +*.pyo +venv/ +.venv/ +*.egg-info/ +dist/ +build/ + +# HuggingFace model cache +.cache/ + +# Pyvis auto-generated output — not application code +*.html +test_graph.html + +# React build output — generated by `pnpm build`, not source code +frontend/dist/ +frontend/node_modules/ + +# OS +.DS_Store +Thumbs.db + +# IDE +.vscode/ +.idea/ +*.swp diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..7b1cbed4c8fdabb8b8147980fe75c43b6422f22f --- /dev/null +++ b/Dockerfile @@ -0,0 +1,13 @@ +FROM python:3.12-slim + +WORKDIR /app + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY app/ ./app/ +COPY frontend/dist/ ./frontend/dist/ + +EXPOSE 7860 + +CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860", "--log-level", "debug"] diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..00b9a13e594fbabe203ced36576087c89b7a6243 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 Odeliya Charitonova + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..305f0481e66a1d2d4e7e80dd127e7a9588746117 --- /dev/null +++ b/Makefile @@ -0,0 +1,20 @@ +.PHONY: install run-api run-dashboard test lint clean + +install: + pip install -r requirements.txt + +run-api: + uvicorn app.main:app --reload --port 8000 + +run-dashboard: + streamlit run dashboard.py + +test: + python -m pytest tests/ -v --tb=short + +lint: + ruff check app/ dashboard.py tests/ + +clean: + find . -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true + rm -f algoscope.db diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..10c7b6f65d1142ad39146d7f157d9889118a87b3 --- /dev/null +++ b/README.md @@ -0,0 +1,205 @@ +--- +title: AlgoScope +emoji: 🔍 +colorFrom: red +colorTo: yellow +sdk: docker +pinned: false +--- + +
+ +# 🔍 AlgoScope + +**Real-time algospeak & toxicity detection on Bluesky** + +[![Build](https://img.shields.io/github/actions/workflow/status/odeliyach/Algoscope/ci.yml?branch=main&label=Build)](https://github.com/odeliyach/Algoscope/actions/workflows/ci.yml) +[![Coverage](https://img.shields.io/codecov/c/github/odeliyach/Algoscope?branch=main&label=Coverage)](https://codecov.io/gh/odeliyach/Algoscope) +[![Linting/Style](https://img.shields.io/github/actions/workflow/status/odeliyach/Algoscope/ci.yml?branch=main&label=Linting%2FStyle)](https://github.com/odeliyach/Algoscope/actions/workflows/ci.yml) +[![Python 3.12](https://img.shields.io/badge/Python-3.12-blue?logo=python)](https://python.org) +[![HuggingFace Model](https://img.shields.io/badge/Model-AlgoShield-orange?logo=huggingface)](https://huggingface.co/odeliyach/AlgoShield-Algospeak-Detection) +[![HuggingFace Space](https://img.shields.io/badge/Live%20Demo-Spaces-yellow?logo=huggingface)](https://huggingface.co/spaces/odeliyach/algoscope) +[![Streamlit](https://img.shields.io/badge/Dashboard-Streamlit-red?logo=streamlit)](https://streamlit.io) +[![License: MIT](https://img.shields.io/badge/License-MIT-green)](LICENSE) + +*Odeliya Charitonova · Tel Aviv University, School of CS & AI · 2026* + +
+ +--- + +## What is AlgoScope? + +Algospeak is the evolving coded language people use to evade content moderation — "unalive" instead of suicide, "seggs" instead of sex, "le dollar bean" instead of lesbian. Standard toxicity APIs score these near zero because they look benign to classifiers trained on explicit language. + +AlgoScope is a live dashboard that catches them anyway. It ingests posts from the Bluesky social network in real time, classifies each one with a fine-tuned DistilBERT model trained specifically on algospeak, and visualizes toxicity patterns, co-occurrence networks, and trend spikes in an interactive dashboard. + +> **Why this matters:** Algospeak evasion is an active research problem in content moderation. This project turns published NLP research into a live, clickable product. + +--- + +## Live Demo + +| Resource | Link | +|----------|------| +| 🖥️ Live dashboard | [huggingface.co/spaces/odeliyach/algoscope](https://huggingface.co/spaces/odeliyach/algoscope) | +| 🤗 Fine-tuned model | [odeliyach/AlgoShield-Algospeak-Detection](https://huggingface.co/odeliyach/AlgoShield-Algospeak-Detection) | +| 💻 GitHub | [github.com/odeliyach/Algoscope](https://github.com/odeliyach/Algoscope) | + +--- + +--- + +## Features + +- **🚨 Spike alerts** — red banner when a tracked term exceeds 80% toxic in the last hour +- **📊 Toxicity over time** — hourly line chart with color-coded data points (green/orange/red by toxicity level) +- **🕸️ Co-occurrence graph** — interactive word graph built with NetworkX + Pyvis; nodes colored by toxicity rate +- **⚖️ Term comparison** — side-by-side toxicity profiles for any two tracked terms +- **📥 Export** — download all analyzed posts as CSV or JSON +- **🎛️ Threshold slider** — tune precision/recall tradeoff at inference time without retraining + +--- + +## Architecture + +``` +┌─────────────────┐ AT Protocol ┌───────────────────┐ +│ Bluesky API │ ───────────────▶ │ ingestion.py │ +└─────────────────┘ │ dedup + preproc │ + └─────────┬─────────┘ + │ + ┌─────────▼─────────┐ + │ model.py │ + │ DistilBERT │ + │ singleton + batch│ + └─────────┬─────────┘ + │ + ┌────────────────────▼──────────────────────┐ + │ database.py │ + │ SQLite · URI-keyed deduplication │ + └────────────────────┬──────────────────────┘ + │ + ┌────────────────────────────────▼────────────────────────────┐ + │ dashboard.py │ + │ Streamlit · Plotly · NetworkX · Pyvis (4 tabs) │ + └─────────────────────────────────────────────────────────────┘ +``` + +**Stack:** Python 3.12 · FastAPI · Streamlit · SQLite · NetworkX · Pyvis · Plotly · HuggingFace Transformers · AT Protocol (Bluesky) + +--- + +## Model — AlgoShield + +The classifier powering AlgoScope is **AlgoShield**, a DistilBERT model fine-tuned on the [MADOC dataset](https://arxiv.org/abs/2306.01976) (Multimodal Algospeak Detection and Offensive Content). It was trained and evaluated separately — full training code, dataset preprocessing, and evaluation notebooks are in the [AlgoShield repository](https://huggingface.co/odeliyach/AlgoShield-Algospeak-Detection). + +| Metric | Baseline DistilBERT | AlgoShield (fine-tuned) | +|--------|---------------------|------------------------| +| Precision | 70.3% | 61.2% | +| Recall | 33.2% | **73.2% (+40 pts)** | +| F1 | 49.0% | **66.7% (+17.7 pts)** | + +The +40-point recall improvement comes at the cost of ~9 points of precision — a deliberate tradeoff. In content moderation, a false negative (missing a toxic post) causes real harm; a false positive just means a human reviews something innocent. The threshold slider in AlgoScope lets operators tune this tradeoff at deployment time without retraining. + +> Want to understand how AlgoShield was built? See the [model card and training details →](https://huggingface.co/odeliyach/AlgoShield-Algospeak-Detection) + +--- + +## Key Engineering Decisions + +**Train/serve parity** — The same `preprocess_text()` function used during AlgoShield's training is applied at inference time in AlgoScope. Without this, the model sees out-of-distribution input on every prediction — a production ML bug called train/serve skew. + +**Threshold separation** — The model outputs a raw confidence score; a threshold slider converts it to a binary label. This separates the ML model from business policy — the same pattern used in Gmail spam and YouTube moderation. One model, multiple thresholds tuned per context. + +**Graph construction order** — The co-occurrence graph filters to the 1-hop neighborhood of algospeak seed words *before* frequency ranking. The naive approach (top-30 globally, then filter) always returns generic English function words ("get", "like", "know") — useless for the project's purpose. + +**Physics disabled** — Pyvis force-directed layout is O(n²) per animation frame. With 30+ nodes it froze the browser for 2+ minutes. A fixed `randomSeed` layout loads instantly with reproducible positions. + +**SQLite with clean abstraction** — All persistence is isolated in `database.py`. No other file imports `sqlite3` directly. Replacing SQLite with PostgreSQL or Cassandra requires changing only that one file. + +--- + +## Running Locally + +**Requirements:** Python 3.12, a Bluesky account + +```bash +git clone https://github.com/odeliyach/Algoscope +cd Algoscope +python -m venv venv +venv\Scripts\activate # Windows +# source venv/bin/activate # Mac/Linux +pip install -r requirements.txt +``` + +Or with Make: +```bash +make install +make run-dashboard # in one terminal +make run-api # in another +``` + +Create `.env` in the project root: +```env +BLUESKY_HANDLE=yourhandle.bsky.social +BLUESKY_PASSWORD=yourpassword +``` + +--- + +## Project Structure + +``` +Algoscope/ +├── app/ +│ ├── main.py # FastAPI endpoints (/health, /predict) +│ ├── model.py # ToxicityClassifier — singleton load, batch inference +│ ├── ingestion.py # Bluesky AT Protocol client + preprocessing +│ ├── database.py # SQLite persistence — isolated for easy swap +│ └── graph.py # NetworkX co-occurrence graph + Pyvis HTML export +├── assets/ +│ ├── overview.png # Dashboard overview screenshot +│ ├── graph.png # Co-occurrence graph screenshot +│ └── term_comparison.png # Term comparison screenshot +├── tests/ +│ └── test_core.py # Preprocessing parity, DB round-trip, stopwords +├── dashboard.py # Streamlit dashboard — 4 tabs +├── Makefile # install / run / test / lint shortcuts +├── requirements.txt # Runtime dependencies +├── pyproject.toml # Project metadata + tooling config +├── Dockerfile # python:3.12-slim, non-root user +├── .github/workflows/ +│ └── ci.yml # Import checks + syntax + pytest on every push +└── .env # Credentials — not committed +``` + +--- + +## Deployment (HuggingFace Spaces) + +1. Push this repo to GitHub (verify `.env` and `algoscope.db` are in `.gitignore`) +2. Go to [huggingface.co](https://huggingface.co) → New Space → Streamlit → connect this GitHub repo +3. In Space Settings → Secrets, add `BLUESKY_HANDLE` and `BLUESKY_PASSWORD` +4. The Space auto-deploys on every push to `main` + +--- + +## Limitations & Future Work + +- **Bluesky-only** — the ingestion layer is modular; adding Reddit or Mastodon requires only a new adapter in `ingestion.py` +- **Fetch-on-click** — a background ingestion loop would keep data flowing continuously without user interaction +- **Static model** — algospeak evolves; periodic retraining or drift detection would maintain coverage over time +- **SQLite single-writer** — replacing with PostgreSQL or Cassandra enables concurrent multi-worker ingestion + +--- + +## License + +MIT — see [LICENSE](LICENSE) + +--- + +
+AlgoScope · Tel Aviv University, School of CS & AI · Odeliya Charitonova · 2026 +
diff --git a/app/__init__.py b/app/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/app/__init__.py @@ -0,0 +1 @@ + diff --git a/app/database.py b/app/database.py new file mode 100644 index 0000000000000000000000000000000000000000..25f1153b8ac6ffdb916b8afe3b98cd0831402787 --- /dev/null +++ b/app/database.py @@ -0,0 +1,164 @@ +""" +SQLite database for storing post classification results. + +ARCHITECTURE NOTE (interview talking point): +All persistence is isolated in this file. No other module imports sqlite3 +directly. This means swapping SQLite for PostgreSQL or any other store +requires changing only this one file — the rest of the codebase is +completely unaware of how data is stored. +""" + +import logging +import os +import sqlite3 +from typing import Any + +logger = logging.getLogger(__name__) + +_temp_dir = os.environ.get("TMPDIR") +if not _temp_dir: + _temp_dir = os.environ.get("TEMP") or os.environ.get("TMP") or "/tmp" +DB_PATH = os.environ.get( + "ALGOSCOPE_DB_PATH", + os.path.join(_temp_dir, "algoscope.db"), +) + +_db_initialized = False + + +def _get_connection() -> sqlite3.Connection: + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + return conn + + +def init_db() -> None: + """Create tables if they don't exist. Safe to call multiple times.""" + with _get_connection() as conn: + conn.execute( + """ + CREATE TABLE IF NOT EXISTS posts ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + text TEXT NOT NULL, + label TEXT NOT NULL, + score REAL NOT NULL, + platform TEXT NOT NULL, + query_term TEXT NOT NULL DEFAULT '', + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """ + ) + try: + conn.execute("ALTER TABLE posts ADD COLUMN query_term TEXT NOT NULL DEFAULT ''") + except sqlite3.OperationalError: + pass + conn.commit() + + +def _ensure_init() -> None: + """Initialize DB once per process, not on every call.""" + global _db_initialized + if not _db_initialized: + init_db() + _db_initialized = True + + +def save_post( + text: str, + label: str, + score: float, + platform: str, + query_term: str = "", +) -> None: + """Insert a classified post into the posts table.""" + _ensure_init() + with _get_connection() as conn: + conn.execute( + "INSERT INTO posts (text, label, score, platform, query_term) VALUES (?, ?, ?, ?, ?)", + (text, label, score, platform, query_term), + ) + conn.commit() + + +def get_recent_posts(limit: int = 100) -> list[dict[str, Any]]: + """Return the most recent posts as a list of dicts, newest first.""" + _ensure_init() + with _get_connection() as conn: + cursor = conn.execute( + """ + SELECT id, text, label, score, platform, query_term, created_at + FROM posts + ORDER BY created_at DESC + LIMIT ? + """, + (limit,), + ) + rows = cursor.fetchall() + return [dict(row) for row in rows] + + +def get_post_count() -> int: + """Return total number of posts in the DB.""" + _ensure_init() + with _get_connection() as conn: + return conn.execute("SELECT COUNT(*) FROM posts").fetchone()[0] + + +def seed_if_empty() -> None: + """ + If the DB is empty (cold start or HF ephemeral filesystem wipe), fetch + a small batch of real posts from Bluesky and classify them so the + dashboard has data immediately without requiring the user to click FETCH. + + WHY this is safe now (it was disabled before): + Previously this ran at module import time, triggering a model download + before uvicorn bound to port 7860, killing the container with no logs. + Now it is called from lifespan() AFTER the server is up and AFTER the + classifier has loaded. A failure here is non-fatal. + + WHY 4 queries at limit=32 (not all queries at full limit): + Seeding is best-effort background work. ~30 posts is enough to populate + all dashboard widgets. Seeding all queries would add 10-30s to cold + start time, unacceptable for a free-tier Space that restarts often. + """ + _ensure_init() + count = get_post_count() + if count > 0: + logger.info("seed_if_empty: DB has %d posts, skipping seed", count) + return + + logger.info("seed_if_empty: DB is empty, seeding from Bluesky...") + try: + from app.ingestion import ALGOSPEAK_QUERIES, fetch_posts + from app.model import ToxicityClassifier + + classifier = ToxicityClassifier() + if classifier._pipeline is None: + logger.warning("seed_if_empty: classifier not ready, skipping seed") + return + + seed_queries = ALGOSPEAK_QUERIES[:4] + posts = fetch_posts(query=seed_queries[0], limit=32, queries=seed_queries) + if not posts: + logger.warning("seed_if_empty: no posts returned from Bluesky") + return + + texts = [t for t, _ in posts] + timestamps = [ts for _, ts in posts] + predictions = classifier.predict_batch(texts) + + for text, ts, pred in zip(texts, timestamps, predictions): + score = float(pred.get("score", 0.0) or 0.0) + label = "toxic" if score >= 0.70 else "non-toxic" + matched = next( + (q for q in seed_queries if q and q.lower() in text.lower()), + seed_queries[0], + ) + save_post(text=text, label=label, score=score, platform="bluesky", query_term=matched) + + logger.info("seed_if_empty: seeded %d posts", len(texts)) + except Exception as exc: + # WHY catch-all: Bluesky credentials may not be set, the network may + # be unavailable, or the model may not have loaded. The app must start + # regardless - the user can always click FETCH manually. + logger.warning("seed_if_empty: failed (non-fatal): %s", exc) diff --git a/app/graph.py b/app/graph.py new file mode 100644 index 0000000000000000000000000000000000000000..a778828ab689bc211328fe85bd7308bec3b5d9ec --- /dev/null +++ b/app/graph.py @@ -0,0 +1,211 @@ +""" +Graph utilities for exploring algospeak co-occurrence patterns. + +Co-occurrence graphs are a classic NLP exploratory tool: by connecting words +that frequently appear together, we can surface clusters of related slang +or emergent euphemisms that would be hard to spot from raw text alone. +""" + +from __future__ import annotations + +import re +from itertools import combinations +from typing import Dict, List + +import networkx as nx +from pyvis.network import Network + +from app.database import get_recent_posts + +# All words to exclude from the graph. Centralizing here makes it easy to tune. +STOPWORDS = { + # English function words + "the", "and", "or", "but", "if", "then", "else", "when", "where", + "what", "which", "who", "whom", "this", "that", "these", "those", + "a", "an", "of", "in", "on", "for", "to", "from", "by", "with", + "at", "as", "is", "am", "are", "was", "were", "be", "been", "being", + "it", "its", "he", "she", "they", "them", "we", "us", "you", "your", + "yours", "i", "me", "my", "mine", "our", "ours", "their", "theirs", + "do", "does", "did", "doing", "done", "have", "has", "had", + "will", "would", "can", "could", "should", "must", "may", "might", + "just", "like", "so", "very", "too", "not", "no", "yes", + "there", "here", "than", "then", "also", "even", "more", "most", + "get", "got", "go", "going", "say", "said", "out", "now", "day", + "because", "some", "people", "love", "social", "really", "while", + "think", "know", "want", "see", "make", "take", "come", "look", + "good", "new", "first", "last", "long", "great", "little", "own", + "right", "big", "high", "small", "large", "next", "early", "old", + "well", "still", "way", "every", "never", "always", "much", "need", + "feel", "put", "keep", "let", "ask", "seem", "show", "try", "call", + "back", "other", "free", "real", "best", "true", "about", "after", + "again", "dont", "isnt", "cant", "wont", "didnt", "doesnt", "youre", + "theyre", "whats", "thats", "dont", "thing", "things", "time", + # Spanish function words (common on Bluesky) + "de", "que", "con", "como", "para", "una", "uno", "los", "las", + "por", "del", "sus", "pero", "todo", "esta", "este", "son", "hay", + "nos", "han", "fue", "ser", "ver", "vez", "sin", "sobre", "entre", + "cuando", "bien", "solo", "puede", "tiene", "desde", "hasta", + # Web / file tokens + "jpg", "jpeg", "png", "gif", "webp", "www", "http", "https", + "com", "org", "net", "html", "php", "amp", "via", "bit", +} + + +def _tokenize(text: str) -> List[str]: + """ + Lightweight tokenizer for short social posts. + Drops stopwords, short tokens, and tokens with digits. + """ + text = text.lower() + raw_tokens = re.split(r"\W+", text) + tokens = [] + for tok in raw_tokens: + if not tok or len(tok) <= 2: + continue + if any(ch.isdigit() for ch in tok): + continue + if tok in STOPWORDS: + continue + tokens.append(tok) + return tokens + + +def build_cooccurrence_graph(min_cooccurrence: int = 2) -> nx.Graph: + """ + Build a word co-occurrence graph from all posts in the database. + + WHY co-occurrence graphs for algospeak: slang evolves in clusters. + 'unalive' tends to appear with 'suicide', 'depression', 'mental'. + Mapping these clusters reveals semantic neighborhoods of evasive language + that a simple keyword list would miss. + """ + posts = get_recent_posts(limit=10_000_000) + + G = nx.Graph() + word_counts: Dict[str, int] = {} + toxic_word_counts: Dict[str, int] = {} + + for row in posts: + text = (row.get("text") or "").strip() + label = (row.get("label") or "non-toxic").lower() + if not text: + continue + tokens = _tokenize(text) + if not tokens: + continue + + # Use a set so repeated words in one post don't inflate edge weights + unique_words = set(tokens) + + for w in unique_words: + word_counts[w] = word_counts.get(w, 0) + 1 + if label == "toxic": + toxic_word_counts[w] = toxic_word_counts.get(w, 0) + 1 + + for w1, w2 in combinations(sorted(unique_words), 2): + if G.has_edge(w1, w2): + G[w1][w2]["weight"] += 1 + else: + G.add_edge(w1, w2, weight=1) + + # Remove weak edges and isolated nodes + G.remove_edges_from([ + (u, v) for u, v, d in G.edges(data=True) + if d.get("weight", 0) < min_cooccurrence + ]) + G.remove_nodes_from(list(nx.isolates(G))) + + # Attach node metadata for visualization + for word, count in word_counts.items(): + if word not in G: + continue + G.nodes[word]["count"] = count + G.nodes[word]["toxic_count"] = toxic_word_counts.get(word, 0) + + # STEP 1: Filter to algospeak neighborhood FIRST. + # WHY order matters: filtering before top-30 ensures we get the most + # frequent *algospeak-related* words, not the most frequent generic words. + from app.ingestion import ALGOSPEAK_QUERIES + seed_words = {w for q in ALGOSPEAK_QUERIES for w in q.lower().split()} + + relevant = set() + for seed in seed_words: + if seed in G: + relevant.add(seed) + relevant.update(G.neighbors(seed)) + + if relevant: + G = G.subgraph(relevant).copy() + + # STEP 2: Take top 30 by frequency from the algospeak neighborhood + if G.number_of_nodes() > 30: + top_nodes = sorted( + G.nodes(data=True), + key=lambda x: x[1].get("count", 0), + reverse=True, + )[:30] + G = G.subgraph({n[0] for n in top_nodes}).copy() + + return G + + +def graph_to_pyvis(graph: nx.Graph, toxic_only: bool = False) -> str: + """ + Convert a NetworkX graph into interactive Pyvis HTML. + Physics disabled for instant rendering (animation caused 2min load times). + """ + net = Network(height="600px", width="100%", directed=False, notebook=False) + net.set_options('''{ + "physics": {"enabled": false}, + "configure": {"enabled": false}, + "layout": {"randomSeed": 42} + }''') + + included_nodes = set() + for node, data in graph.nodes(data=True): + count = int(data.get("count", 1) or 1) + toxic_count = int(data.get("toxic_count", 0) or 0) + toxic_ratio = toxic_count / count if count else 0.0 + + if toxic_only and toxic_count == 0: + continue + + # 3-color system: red=mostly toxic, orange=mixed, green=mostly benign + # More informative than binary because it shows usage context gradient + if toxic_ratio > 0.7: + color = "#ff4b4b" + elif toxic_ratio >= 0.4: + color = "#ff9f43" + else: + color = "#2ecc71" + + net.add_node(node, label=node, color=color, value=count) + included_nodes.add(node) + + for u, v, data in graph.edges(data=True): + if u not in included_nodes or v not in included_nodes: + continue + net.add_edge(u, v, value=int(data.get("weight", 1) or 1)) + + # Replace local file reference that breaks in Streamlit's sandboxed iframe + html = net.generate_html() + + import json # noqa: F401 + center_script = """ + + """ + html = html.replace("", center_script + "") + + html = html.replace( + '', + '' + ) + return html \ No newline at end of file diff --git a/app/ingestion.py b/app/ingestion.py new file mode 100644 index 0000000000000000000000000000000000000000..51daa3752f7527a513ed454f303b2d8ea9f1fe4c --- /dev/null +++ b/app/ingestion.py @@ -0,0 +1,221 @@ +""" +Bluesky post ingestion via the atproto library. +""" + +import os +import re +from concurrent.futures import ThreadPoolExecutor, TimeoutError +from typing import Any, Iterable, Optional, Set +import logging + +logger = logging.getLogger(__name__) + +# Using multiple queries lets us cover different algospeak variants that +# users employ to evade simple keyword filters. Centralizing them here makes +# it easy to tune the "vocabulary" without changing the rest of the pipeline. +ALGOSPEAK_QUERIES: list[str] = [ + "unalive", + "le dollar bean", + "seggs", + "cornhole", + "spicy eggplant", + "cope harder", + "ratio", + "touch grass", + "based", + "sus", +] + +# Cache the atproto client so we only pay the authentication cost once. +# In practice this can save ~10 seconds per fetch because login requires +# multiple network round trips, while the resulting session stays valid +# for many minutes to hours. +_client = None + + +def get_client(): + """Return a logged-in atproto Client, cached at module level.""" + global _client + if _client is not None: + return _client + + from atproto import Client + + handle = os.environ.get("BLUESKY_HANDLE") + password = os.environ.get("BLUESKY_PASSWORD") + if not handle or not password: + raise RuntimeError("BLUESKY_HANDLE or BLUESKY_PASSWORD not set") + + logger.info("Authenticating with Bluesky as %s", handle) + client = Client() + client.login(handle, password) + _client = client + logger.info("Bluesky authentication successful") + return client + + +def preprocess_text(text: str) -> str: + """ + Normalize text to match the training-time preprocessing. + + A very common ML bug is to train with one preprocessing pipeline and + serve with another. Keeping train and inference transforms aligned is + critical; otherwise, the model is effectively seeing out-of-distribution + data at serving time even if it "looks" similar to humans. + + Args: + text: Raw post text from Bluesky. + + Returns: + Cleaned text string, or empty string if the post should be discarded. + """ + if not text: + return "" + + # Remove URLs so we don't overfit to specific domains not in training data. + text = re.sub(r"https?://\S+", " ", text) + + # Drop non-ASCII characters (including most emojis) to mimic training preprocessing. + text = text.encode("ascii", errors="ignore").decode("ascii") + + # Collapse repeated whitespace and trim. + text = re.sub(r"\s+", " ", text).strip() + + # Strip hashtags before word analysis — hashtags are metadata, not content. + text_no_hashtags = re.sub(r"#\S+", "", text).strip() + + # Filter posts with no real linguistic content (filenames, hashtag spam). + # WHY: These posts add noise to the model and co-occurrence graph + # without contributing meaningful signal about algospeak patterns. + NON_WORDS = {"jpg", "png", "gif", "com", "www", "http", "https", "the", "and"} + real_words = [ + w for w in re.findall(r"[a-zA-Z]{3,}", text_no_hashtags) + if w.lower() not in NON_WORDS + ] + if len(real_words) < 3: + return "" + + # Enforce minimum length consistent with the training filter from the paper. + if len(text) < 10: + return "" + + return text + + +def _dedupe_texts(texts: Iterable[tuple[str, str | None]]) -> list[tuple[str, str | None]]: + """ + Deduplicate posts while preserving order. + + In real moderation systems, the same content can appear multiple times + (reposts, quote-posts, different queries). Deduplication avoids wasting + model budget on identical texts and keeps metrics from being biased by + repeated copies of the same post. + + Args: + texts: Iterable of text strings (may contain duplicates). + + Returns: + List of unique strings in original order. + """ + seen: Set[str] = set() + result: list[tuple[str, str | None]] = [] + for text, ts in texts: + if text in seen: + continue + seen.add(text) + result.append((text, ts)) + return result + + +def fetch_posts( + query: str, + limit: int = 50, + queries: Optional[list[str]] = None, +) -> list[tuple[str, str | None]]: + """ + Search Bluesky for posts and return their text content. + + If `queries` is provided, we search for each query term independently + and merge the results. This fan-out pattern covers different algospeak + variants without relying on a single brittle keyword. + + Args: + query: Primary search term (used if queries is None). + limit: Maximum number of posts to return across all queries. + queries: Optional list of terms to fan out across. + + Returns: + Deduplicated list of preprocessed post texts. + Returns empty list on any error (credentials, network, API). + """ + all_texts: list[str] = [] + + def _worker() -> None: + """ + Perform the actual API calls in a worker thread. + + WHY a thread + timeout: if Bluesky's API hangs, the request thread + would block indefinitely without a timeout, degrading UX and + potentially exhausting the worker pool in a multi-user deployment. + """ + try: + client = get_client() + query_list = queries if queries is not None else [query] + logger.info("Fetching posts for %d queries (limit=%d)", len(query_list), limit) + + for q in query_list: + # Divide the total limit evenly across all queries so that the + # sum of per-query fetches equals the requested limit. + # WHY: previously every query fetched min(limit, 10) posts, + # so 10 queries × 10 posts = up to 100 posts regardless of + # what the user requested. Now 25 limit ÷ 10 queries = 2-3 + # posts each, giving ~25 total before dedup — matching the UI. + per_query_limit = max(1, limit // len(query_list)) if queries is not None else limit + params: dict[str, Any] = { + "q": q, + "limit": min(max(1, per_query_limit), 100), + } + response = client.app.bsky.feed.search_posts(params=params) + + if not response or not getattr(response, "posts", None): + logger.warning("No results for query: %r", q) + continue + + for post in response.posts: + record = getattr(post, "record", None) + if record is None: + continue + raw_text = getattr(post.record, "text", None) + if raw_text is None or not isinstance(raw_text, str): + continue + cleaned = preprocess_text(raw_text) + if not cleaned: + continue + post_ts = getattr(post.record, "created_at", None) + all_texts.append((cleaned, post_ts)) + + logger.info("Fetched %d posts before deduplication", len(all_texts)) + + except Exception as exc: + # WHY reset client: if auth expired the next fetch should + # re-authenticate from scratch, not retry with a stale session. + logger.error("Fetch failed: %s — resetting client", exc, exc_info=True) + global _client + _client = None + + try: + with ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(_worker) + try: + future.result(timeout=30) + except TimeoutError: + logger.warning("Bluesky fetch timed out after 30s") + future.cancel() + + deduped = _dedupe_texts(all_texts) + logger.info("Returning %d posts after deduplication", len(deduped)) + return deduped + + except Exception as exc: + logger.error("Unexpected error in fetch_posts: %s", exc, exc_info=True) + return [] \ No newline at end of file diff --git a/app/main.py b/app/main.py new file mode 100644 index 0000000000000000000000000000000000000000..97d436a120c07187f63f5e243522b1d389aea51a --- /dev/null +++ b/app/main.py @@ -0,0 +1,297 @@ +""" +FastAPI backend for AlgoScope toxicity detection. + +ARCHITECTURE NOTE (interview talking point): +This file is the boundary between the ML layer and the outside world. +The React frontend talks exclusively to these endpoints — it has no direct +access to the database, the model, or the Bluesky client. That separation +means you can swap any layer (swap SQLite for Postgres, swap the model, +swap the frontend framework) without touching the others. +""" + +import logging +import os +import threading +import time +from contextlib import asynccontextmanager +from typing import Any + +from fastapi import FastAPI, Query +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import FileResponse +from fastapi.staticfiles import StaticFiles +from pydantic import BaseModel +from dotenv import load_dotenv + +from app.database import get_post_count, get_recent_posts, save_post, seed_if_empty +from app.graph import build_cooccurrence_graph +from app.ingestion import ALGOSPEAK_QUERIES, fetch_posts +from app.model import ToxicityClassifier + +logger = logging.getLogger(__name__) + +# Load local environment variables for Bluesky credentials in development. +load_dotenv() + +# WHY None here: initializing ToxicityClassifier() at module scope triggers +# a 250MB model download before uvicorn binds to port 7860. HuggingFace Spaces +# sees no response on the port and kills the container with no logs. +# We initialize inside lifespan() instead, after the server is already up. +classifier: ToxicityClassifier | None = None + + +def _background_init() -> None: + """ + Load the model and seed the DB in a background thread. + + WHY background thread and not lifespan: + HuggingFace Spaces kills containers that don't respond on port 7860 + within ~30 seconds. Loading a 250MB model + fetching Bluesky posts + easily exceeds that. Running both in a daemon thread lets uvicorn bind + immediately; the model and seed data become available a few seconds + later. Requests that arrive before the model is ready return default + predictions (non-toxic / 0.0) which is acceptable for a brief window. + """ + global classifier + try: + classifier = ToxicityClassifier() + classifier._load_model() + logger.info("ToxicityClassifier ready") + except Exception as exc: + logger.warning("Model load failed — predictions will return defaults: %s", exc) + classifier = None + try: + seed_if_empty() + logger.info("DB seed check complete") + except Exception as exc: + logger.warning("Seed skipped (likely missing credentials): %s", exc) + + +@asynccontextmanager +async def lifespan(app: FastAPI): + # WHY daemon=True: daemon threads are killed automatically when the main + # process exits, so we don't block graceful shutdown waiting for a slow + # model download or Bluesky fetch. + t = threading.Thread(target=_background_init, daemon=True) + t.start() + logger.info("AlgoScope API starting up — model loading in background") + yield + logger.info("AlgoScope API shutting down") + + +app = FastAPI( + title="AlgoScope API", + description="Real-time algospeak and toxicity detection for Bluesky posts.", + version="0.2.0", + lifespan=lifespan, +) + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +class PredictRequest(BaseModel): + text: str + + +class PredictResponse(BaseModel): + label: str + score: float + + +class FetchRequest(BaseModel): + queries: list[str] = ALGOSPEAK_QUERIES + limit: int = 25 + threshold: float = 0.70 + + +class PostOut(BaseModel): + id: int + text: str + label: str + score: float + platform: str + created_at: str + query_term: str = "" + + +@app.get("/health") +def health() -> dict[str, str]: + """Liveness check — used by HuggingFace Spaces and load balancers.""" + return {"status": "ok"} + + +@app.post("/predict", response_model=PredictResponse) +def predict(request: PredictRequest) -> dict[str, str | float]: + """Classify a single text as toxic or non-toxic.""" + if classifier is None: + return {"label": "non-toxic", "score": 0.0} + logger.info("Predicting for text (len=%d)", len(request.text)) + result = classifier.predict(request.text) + logger.info("Result: label=%s score=%.3f", result["label"], result["score"]) + return {"label": result["label"], "score": result["score"]} + + +@app.get("/posts") +def get_posts( + limit: int = Query(default=100, ge=1, le=10000), +) -> dict[str, Any]: + """Return recent posts from the database for the React frontend.""" + rows = get_recent_posts(limit=limit) + for row in rows: + if "query_term" not in row: + row["query_term"] = "" + if not row.get("created_at"): + row["created_at"] = "" + # WHY get_post_count() instead of len(rows): + # rows is capped at `limit`, so len(rows) always equals min(limit, n_posts). + # The frontend calls GET /posts?limit=1 to get the true total for the + # "Posts Analyzed" counter — returning len(rows)=1 there was causing + # the counter to reset to 1 after every fetch. + return {"posts": rows, "total": get_post_count()} + + +@app.post("/fetch-and-analyze") +def fetch_and_analyze(request: FetchRequest) -> dict[str, Any]: + """ + Fetch posts from Bluesky, run batch inference, save to DB, return results. + """ + if classifier is None: + return {"posts": [], "fetch_time": 0.0, "infer_time": 0.0, "count": 0, + "message": "Model not loaded yet, please try again in a moment."} + + logger.info( + "fetch-and-analyze: queries=%s limit=%d threshold=%.2f", + request.queries, request.limit, request.threshold, + ) + + t0 = time.time() + posts_text = fetch_posts( + query=request.queries[0] if request.queries else "unalive", + limit=request.limit, + queries=request.queries or None, + ) + fetch_time = time.time() - t0 + + if not posts_text: + return { + "posts": [], + "fetch_time": fetch_time, + "infer_time": 0.0, + "count": 0, + "message": "No posts fetched. Check Bluesky credentials or try again.", + } + + texts_only = [text for text, _ts in posts_text] + timestamps = [ts for _text, ts in posts_text] + + t1 = time.time() + predictions = classifier.predict_batch(texts_only) + infer_time = time.time() - t1 + + batch_ts_iso = time.strftime("%Y-%m-%dT%H:%M:%S", time.gmtime()) + result_posts: list[dict[str, Any]] = [] + + for i, (text, post_ts, pred) in enumerate(zip(texts_only, timestamps, predictions)): + score = float(pred.get("score", 0.0) or 0.0) + label = "toxic" if score >= request.threshold else "non-toxic" + matched_term = next( + (t for t in request.queries if t and t.lower() in text.lower()), + request.queries[0] if request.queries else "", + ) + save_post(text=text, label=label, score=score, platform="bluesky", query_term=matched_term) + result_posts.append({ + "id": int(time.time() * 1000) + i, + "text": text, + "label": label, + "score": score, + "platform": "bluesky", + "created_at": post_ts or batch_ts_iso, + "query_term": matched_term, + }) + + logger.info( + "fetch-and-analyze: %d posts, fetch=%.2fs infer=%.2fs", + len(result_posts), fetch_time, infer_time, + ) + + return { + "posts": result_posts, + "fetch_time": fetch_time, + "infer_time": infer_time, + "count": len(result_posts), + } + + +@app.get("/graph-data") +def graph_data( + min_cooccurrence: int = Query(default=2, ge=1, le=20), + toxic_only: bool = Query(default=False), +) -> dict[str, Any]: + """Return co-occurrence graph as JSON nodes + edges.""" + graph = build_cooccurrence_graph(min_cooccurrence=min_cooccurrence) + + nodes = [] + for node, data in graph.nodes(data=True): + count = int(data.get("count", 1) or 1) + toxic_count = int(data.get("toxic_count", 0) or 0) + toxic_ratio = toxic_count / count if count else 0.0 + if toxic_only and toxic_count == 0: + continue + nodes.append({ + "id": node, + "count": count, + "toxic_count": toxic_count, + "toxic_ratio": round(toxic_ratio, 3), + }) + + included = {n["id"] for n in nodes} + edges = [ + {"source": u, "target": v, "weight": int(data.get("weight", 1) or 1)} + for u, v, data in graph.edges(data=True) + if u in included and v in included + ] + + return {"nodes": nodes, "edges": edges, "node_count": len(nodes), "edge_count": len(edges)} + + +@app.get("/stats") +def stats() -> dict[str, Any]: + """Aggregate statistics for the Overview tab metric cards.""" + rows = get_recent_posts(limit=100_000) + total = len(rows) + toxic = sum(1 for r in rows if (r.get("label") or "").lower() == "toxic") + term_counts: dict[str, int] = {} + for row in rows: + term = row.get("query_term") or "" + if term: + term_counts[term] = term_counts.get(term, 0) + 1 + return { + "total_posts": total, + "toxic_posts": toxic, + "toxic_rate": round(toxic / total * 100, 2) if total else 0.0, + "term_counts": term_counts, + } + + +# ── Static file serving (React build) ───────────────────────────────────────── +_FRONTEND_DIST = os.path.join(os.path.dirname(__file__), "..", "frontend", "dist") + +if os.path.exists(_FRONTEND_DIST): + _assets_dir = os.path.join(_FRONTEND_DIST, "assets") + if os.path.exists(_assets_dir): + app.mount("/assets", StaticFiles(directory=_assets_dir), name="assets") + + @app.get("/", response_class=FileResponse, include_in_schema=False) + def serve_frontend_root(): + return FileResponse(os.path.join(_FRONTEND_DIST, "index.html")) + + @app.get("/{full_path:path}", response_class=FileResponse, include_in_schema=False) + def serve_frontend_spa(full_path: str): + """Catch-all for React Router — prevents 404 on page refresh.""" + return FileResponse(os.path.join(_FRONTEND_DIST, "index.html")) diff --git a/app/model.py b/app/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1f4db0ce53130d04dbee55390223672ad861cbb8 --- /dev/null +++ b/app/model.py @@ -0,0 +1,103 @@ +""" +Toxicity classifier using the AlgoShield-Algospeak-Detection HuggingFace model. +""" + +from typing import Any +import logging + +logger = logging.getLogger(__name__) + + +class ToxicityClassifier: + """ + Wrapper around the fine-tuned DistilBERT toxicity model. + + WHY lazy loading: calling _load_model() inside __init__ triggers a 250MB + model download at import time, before uvicorn binds to port 7860. + HuggingFace Spaces sees no response and kills the container with no logs. + Instead, main.py calls _load_model() explicitly inside lifespan(), after + the server is already up and logging. + """ + + def __init__(self) -> None: + self._pipeline = None + + def _load_model(self) -> None: + """Load the HuggingFace pipeline. Called once by lifespan() in main.py.""" + if self._pipeline is not None: + return + + try: + import torch # noqa: F401 + from transformers import pipeline + + logger.info("Loading AlgoShield-Algospeak-Detection model...") + self._pipeline = pipeline( + "text-classification", + model="odeliyach/AlgoShield-Algospeak-Detection", + ) + logger.info("Model loaded successfully") + except Exception as exc: + logger.error("Failed to load model: %s", exc, exc_info=True) + self._pipeline = None + + def predict(self, text: str) -> dict[str, Any]: + """Classify a single text as toxic or non-toxic.""" + default: dict[str, Any] = {"label": "non-toxic", "score": 0.0} + + if not text or not text.strip(): + return default + + try: + if self._pipeline is None: + logger.error("Pipeline is None — model was not loaded correctly") + return default + + results = self._pipeline(text, truncation=True, max_length=512) + if not results: + return default + + raw = results[0] + raw_label = str(raw.get("label", "")).lower() + raw_score = float(raw.get("score", 0.0)) + + if "toxic" in raw_label or raw_label in ("1", "label_1", "positive"): + label = "toxic" + else: + label = "non-toxic" + + return {"label": label, "score": raw_score} + + except Exception as exc: + logger.error("predict() failed: %s", exc, exc_info=True) + return default + + def predict_batch(self, texts: list[str]) -> list[dict[str, Any]]: + """Classify a list of texts in a single forward pass.""" + if not texts: + return [] + + cleaned = [t for t in texts if isinstance(t, str) and t.strip()] + if not cleaned or self._pipeline is None: + return [] + + try: + outputs = self._pipeline(cleaned, truncation=True, max_length=512) + except Exception as exc: + logger.error("predict_batch() failed: %s", exc, exc_info=True) + return [] + + results: list[dict[str, Any]] = [] + for raw in outputs: + raw_label = str(raw.get("label", "")).lower() + raw_score = float(raw.get("score", 0.0)) + + if "toxic" in raw_label or raw_label in ("1", "label_1", "positive"): + label = "toxic" + else: + label = "non-toxic" + + results.append({"label": label, "score": raw_score}) + + logger.info("predict_batch: classified %d texts", len(results)) + return results diff --git a/dashboard.py b/dashboard.py new file mode 100644 index 0000000000000000000000000000000000000000..870535590f5db476e5f41a8ef57c46f606a8114b --- /dev/null +++ b/dashboard.py @@ -0,0 +1,953 @@ +""" +AlgoScope Streamlit dashboard for live algospeak/toxicity detection. +""" + +from __future__ import annotations + +import time +from collections import defaultdict +from datetime import datetime, timedelta +from html import escape + +from dotenv import load_dotenv +import plotly.graph_objects as go +import streamlit as st + +from app.database import get_recent_posts, save_post +from app.graph import build_cooccurrence_graph, graph_to_pyvis +from app.ingestion import ALGOSPEAK_QUERIES, fetch_posts +from app.model import ToxicityClassifier + +load_dotenv() + +# Singleton classifier — loads once, survives all reruns. +# DistilBERT takes ~2s to load; without this every button click reloads it. +classifier = ToxicityClassifier() + + +def _parse_dt(ts_str) -> datetime | None: + """ + Safely parse a timestamp string to datetime. + WHY: SQLite stores timestamps as plain text with no timezone info. + Without explicit normalization, timezone-naive comparisons silently + give wrong results — a common subtle data bug in production. + """ + try: + s = str(ts_str).replace("Z", "").replace(" ", "T") + return datetime.fromisoformat(s) + except Exception: + return None + + +def main() -> None: + st.set_page_config( + page_title="AlgoScope — Live Algospeak Detection", + layout="wide", + initial_sidebar_state="expanded", # WHY: prevents sidebar from starting closed + ) + + st.markdown( + """ + + """, + unsafe_allow_html=True, + ) + + # ── Header ───────────────────────────────────────────────────────────────── + # WHY st.components.v1.html: st.markdown strips onclick for security. + # components.html renders inside an iframe, so JS runs freely and can + # reach window.parent.document to click Streamlit's native sidebar button. + import streamlit.components.v1 as components + components.html(""" + + + """, height=50, scrolling=False) + + st.markdown( + """ +
+
+ +
+
AlgoScope
+
Real-time algospeak & toxicity intelligence on Bluesky
+
+
+
+
+ + LIVE +
+
+ by Odeliya Charitonova
+ + github.com/odeliyach/Algoscope + +
+
+
+ """, + unsafe_allow_html=True, + ) + + # ── Session state init ───────────────────────────────────────────────────── + # WHY session_state: Streamlit reruns the entire script on every interaction. + # Anything that must survive between clicks lives here. + defaults = { + "analyzed_results": [], + "just_fetched": False, + "graph_html": None, + "custom_terms": [], + "selected_queries": list(ALGOSPEAK_QUERIES[:3]), + "fetch_stats": None, + "auto_refresh_ran": False, + "auto_trigger_fetch": False, + "fetch_message": None, + "last_min_cooc": 3, + "sidebar_open": True, + } + for k, v in defaults.items(): + if k not in st.session_state: + st.session_state[k] = v + + # ── Sidebar ──────────────────────────────────────────────────────────────── + with st.sidebar: + # ── Tracked terms display (styled pills like the mockup) ─────────────── + st.markdown('
Tracked terms
', unsafe_allow_html=True) + + # Build toxic-ratio per term from DB history for the sidebar display + history_for_terms = get_recent_posts(limit=1000) + term_toxic_ratio: dict[str, float] = {} + for term in ALGOSPEAK_QUERIES: + term_posts = [r for r in history_for_terms if term in (r.get("text") or "").lower()] + if term_posts: + toxic_n = sum(1 for r in term_posts if (r.get("label") or "").lower() == "toxic") + term_toxic_ratio[term] = toxic_n / len(term_posts) + else: + term_toxic_ratio[term] = 0.0 + + selected_queries = st.session_state.selected_queries + base_options = list(ALGOSPEAK_QUERIES) + extra_options = [t for t in st.session_state.custom_terms if t not in base_options] + all_options = base_options + extra_options + + # Show styled term rows for selected terms + term_rows_html = [] + for term in selected_queries: + ratio = term_toxic_ratio.get(term, 0.0) + pct = int(ratio * 100) + if ratio > 0.7: + dot_color = "#ff4b4b" + elif ratio >= 0.4: + dot_color = "#ff8c42" + else: + dot_color = "#2ecc71" + term_rows_html.append(f""" +
+
+
+ {escape(term)} +
+ {pct}% +
""") + if term_rows_html: + st.markdown("".join(term_rows_html), unsafe_allow_html=True) + + st.markdown('
Algospeak terms
', unsafe_allow_html=True) + # WHY multiselect over checkboxes: supports the full vocabulary list + # and lets analysts add/remove terms without page reload. + selected_queries = st.multiselect( + "Select terms", + options=all_options, + default=st.session_state.selected_queries, + label_visibility="collapsed", + ) + st.session_state.selected_queries = selected_queries + + # Custom term input — full width input + button below + new_term = st.text_input("Add custom term", key="custom_term_input", label_visibility="collapsed", + placeholder="Add custom term...") + add_clicked = st.button("+ Add term", key="add_custom_term", use_container_width=True) + if add_clicked and new_term.strip(): + term = new_term.strip().lower() + if term not in st.session_state.custom_terms and term not in base_options: + st.session_state.custom_terms.append(term) + if term not in st.session_state.selected_queries: + st.session_state.selected_queries.append(term) + selected_queries = st.session_state.selected_queries + + if st.session_state.custom_terms: + pills = "".join( + f"{escape(t)}" + for t in st.session_state.custom_terms + ) + st.markdown(pills, unsafe_allow_html=True) + + # ── Threshold ────────────────────────────────────────────────────────── + st.markdown('
Threshold
', unsafe_allow_html=True) + # WHY threshold slider: separates ML policy from model. Operators tune + # sensitivity without retraining — standard MLOps pattern. + threshold = st.slider("Toxicity threshold", 0.0, 1.0, 0.7, 0.05, label_visibility="collapsed") + + # ── Sampling ─────────────────────────────────────────────────────────── + st.markdown('
Sampling
', unsafe_allow_html=True) + limit = st.slider("Number of posts", 5, 100, 25, label_visibility="collapsed") + + # ── Fetch ────────────────────────────────────────────────────────────── + st.markdown('
Fetch
', unsafe_allow_html=True) + auto_refresh = st.checkbox("Auto-refresh (60s)", value=False) + fetch_button = st.button("Fetch & Analyze", type="primary", use_container_width=True) + + # Auto-refresh countdown + if auto_refresh and not st.session_state.auto_refresh_ran and not fetch_button: + ph = st.empty() + for remaining in range(60, 0, -1): + ph.caption(f"Next fetch in: {remaining}s") + time.sleep(1) + st.session_state.auto_trigger_fetch = True + st.session_state.auto_refresh_ran = True + st.rerun() # WHY st.rerun: experimental_rerun removed in Streamlit 1.34 + if not auto_refresh: + st.session_state.auto_refresh_ran = False + + # ── Fetch stats ──────────────────────────────────────────────────────── + if st.session_state.fetch_stats: + s = st.session_state.fetch_stats + st.caption(f"Fetched {s['n']} posts in {s['fetch']:.1f}s · Inference: {s['infer']:.1f}s") + + min_cooccurrence = st.session_state.last_min_cooc + + # ── Trigger handling ─────────────────────────────────────────────────────── + if st.session_state.auto_trigger_fetch: + fetch_button = True + st.session_state.auto_trigger_fetch = False + + # ── Fetch & Analyze ──────────────────────────────────────────────────────── + if fetch_button: + st.session_state.just_fetched = True + st.session_state.analyzed_results = [] + + with st.spinner("Fetching posts from Bluesky..."): + t0 = time.time() + effective_query = selected_queries[0] if selected_queries else "toxic" + posts = fetch_posts(query=effective_query, limit=limit, queries=selected_queries or None) + fetch_time = time.time() - t0 + + if posts: + t1 = time.time() + # WHY predict_batch: one forward pass for all posts — ~50x faster + # than looping predict() on CPU (measured: 0.36s vs ~18s for 50 posts). + predictions = classifier.predict_batch(posts) + infer_time = time.time() - t1 + + # WHY store created_at and query_term in session_state: + # trend alerts and export both need these fields. Without created_at, + # all datetime comparisons return None and alerts never fire. + batch_ts = datetime.utcnow().isoformat() + for text, pred in zip(posts, predictions): + score = float(pred.get("score", 0.0) or 0.0) + label = "toxic" if score >= threshold else "non-toxic" + # Identify which query term matched this post + matched_term = next( + (t for t in (selected_queries or []) if t and t in text.lower()), + selected_queries[0] if selected_queries else "" + ) + save_post(text=text, label=label, score=score, platform="bluesky") + st.session_state.analyzed_results.append({ + "text": text, + "label": label, + "score": score, + "query_term": matched_term, + "created_at": batch_ts, + }) + + st.session_state.fetch_stats = {"n": len(posts), "fetch": fetch_time, "infer": infer_time} + st.session_state.fetch_message = ("success", f"Done! Analyzed {len(posts)} posts.") + else: + st.session_state.fetch_stats = {"n": 0, "fetch": fetch_time, "infer": 0.0} + st.session_state.fetch_message = ("warning", "No posts fetched. Try again or check your connection.") + else: + st.session_state.just_fetched = False + + + # ── Load data ────────────────────────────────────────────────────────────── + # WHY limit=10000: using 1000 capped the "Posts analyzed" metric + # at 1000 even when the DB had more. High limit shows the real total. + history = get_recent_posts(limit=10000) + batch_rows = st.session_state.analyzed_results + vocab = set(st.session_state.selected_queries or []) | set(st.session_state.custom_terms or []) + + # ── Tabs ─────────────────────────────────────────────────────────────────── + overview_tab, graph_tab, compare_tab, export_tab = st.tabs([" Overview ", " Co-occurrence Graph ", " Term Comparison ", " Export "]) + + with overview_tab: + + # Show fetch message inside the tab (not above tabs) + if st.session_state.get("fetch_message"): + msg_type, msg_text = st.session_state.fetch_message + if msg_type == "success": + st.success(msg_text) + else: + st.warning(msg_text) + st.session_state.fetch_message = None + + # ── Trend alerts ─────────────────────────────────────────────────────── + # WHY: simple spike detection — if a term's toxicity rate in the last + # hour exceeds 80%, show a red banner. In production you'd use z-scores + # or CUSUM control charts; this is the lightweight portfolio version. + now_utc = datetime.utcnow() + one_hour_ago = now_utc - timedelta(hours=1) + alerts = [] + all_terms_alert = list(set(st.session_state.selected_queries or []) | set(st.session_state.custom_terms or [])) + for term in all_terms_alert: + recent_term = [r for r in history + if term in (r.get("text") or "").lower() + and _parse_dt(r.get("created_at")) + and _parse_dt(r.get("created_at")) >= one_hour_ago] + if len(recent_term) >= 3: + t_rate = sum(1 for r in recent_term if (r.get("label") or "").lower() == "toxic") / len(recent_term) + if t_rate >= 0.80: + alerts.append((term, t_rate, len(recent_term))) + for term, t_rate, count in alerts: + st.markdown(f"""
+ 🚨 + Spike detected: + {term} — {t_rate*100:.0f}% + toxic in last hour ({count} posts)
""", + unsafe_allow_html=True) + + # ── Metrics ──────────────────────────────────────────────────────────── + total_posts = len(history) + toxic_posts = sum(1 for r in history if (r.get("label") or "").lower() == "toxic") + toxic_rate = (toxic_posts / total_posts * 100.0) if total_posts else 0.0 + avg_score = ( + sum(float(r.get("score", 0) or 0) for r in batch_rows) / len(batch_rows) + if batch_rows else 0.0 + ) + + term_counts: dict[str, int] = defaultdict(int) + for row in batch_rows: + txt = (row.get("text") or "").lower() + for t in vocab: + if t and t in txt: + term_counts[t] += 1 + top_term = max(term_counts, key=term_counts.get) if term_counts else "—" + + now = datetime.utcnow() + yesterday = now - timedelta(hours=24) + two_days_ago = now - timedelta(hours=48) + + posts_today = sum( + 1 for r in history + if _parse_dt(r.get("created_at")) and _parse_dt(r["created_at"]) > yesterday + ) + posts_yesterday_n = sum( + 1 for r in history + if _parse_dt(r.get("created_at")) + and two_days_ago < _parse_dt(r["created_at"]) <= yesterday + ) + delta_sign = "+" if posts_today >= posts_yesterday_n else "" + delta_text = f"{delta_sign}{posts_today - posts_yesterday_n} vs yesterday" + + # WHY compare batch vs prior history instead of today vs yesterday: + # All posts in the DB were fetched today, so a day-based comparison + # always yields delta=0. Comparing the current batch toxic rate against + # everything fetched before it gives a real, meaningful signal — + # "is this fetch more toxic than usual?" + batch_toxic = sum(1 for r in batch_rows if (r.get("label") or "").lower() == "toxic") + batch_rate = (batch_toxic / len(batch_rows) * 100.0) if batch_rows else None + + prior_rows = [r for r in history if r not in batch_rows] + prior_toxic = sum(1 for r in prior_rows if (r.get("label") or "").lower() == "toxic") + prior_rate = (prior_toxic / len(prior_rows) * 100.0) if prior_rows else None + + if batch_rate is not None and prior_rate is not None: + rate_delta = batch_rate - prior_rate + delta_color = "#ff4b4b" if rate_delta > 0 else "#2ecc71" + delta_arrow = "↑" if rate_delta > 0 else "↓" + rate_delta_str = f"{delta_arrow} {abs(rate_delta):.1f}% vs prior" + elif batch_rate is not None: + rate_delta_str = f"batch: {batch_rate:.1f}%" + delta_color = "#ff4b4b" if batch_rate >= 70 else "#ff8c42" if batch_rate >= 40 else "#2ecc71" + else: + rate_delta_str = "fetch to see delta" + delta_color = "#5a6080" + + c1, c2, c3, c4 = st.columns(4) + with c1: + st.markdown(f"""
+
Posts analyzed
+
{total_posts}
+
+{posts_today} today · {delta_text}
+
""", unsafe_allow_html=True) + with c2: + st.markdown(f"""
+
Toxic rate
+
{toxic_rate:.1f}%
+
{rate_delta_str}
+
""", unsafe_allow_html=True) + with c3: + st.markdown(f"""
+
Avg score (last batch)
+
{avg_score:.3f}
+
Mean toxicity from last fetch
+
""", unsafe_allow_html=True) + with c4: + st.markdown(f"""
+
Top term
+
{escape(str(top_term))}
+
Most frequent in last batch
+
""", unsafe_allow_html=True) + + st.markdown("
", unsafe_allow_html=True) + + # ── Toxicity over time + Score distribution ──────────────────────────── + ts_col, hist_col = st.columns([3, 1]) + + with ts_col: + st.markdown("#### Toxicity over time") + buckets: dict = defaultdict(lambda: {"sum": 0.0, "count": 0}) + for row in history: + dt = _parse_dt(row.get("created_at")) + if not dt: + continue + hour = dt.replace(minute=0, second=0, microsecond=0) + score = float(row.get("score", 0) or 0) + buckets[hour]["sum"] += score + buckets[hour]["count"] += 1 + + if buckets: + # WHY sort by datetime key not string label: hours like "01:00" + # sort alphabetically before "03:00" so without proper datetime + # sorting the line jumps backwards across midnight, creating a + # visual zigzag. Sorting by the actual datetime object fixes this. + # WHY sort by datetime key: ensures chronological order across + # midnight. Using datetime objects (not strings) as the x-axis + # means Plotly treats it as a real time axis — no more zigzag + # when data spans midnight (e.g. 22:00 → 01:00 → 14:00). + # WHY fixed 24 buckets: previous approach only showed hours + # with data, causing uneven spacing and duplicate labels when + # data spanned midnight. A fixed 0-23 hour axis is always + # uniform — empty hours show as gaps in the line (connectgaps=False). + hour_avgs = {} + for dt_key, val in buckets.items(): + h = dt_key.hour + # If same hour appears on multiple days, keep the most recent + if h not in hour_avgs or dt_key > max( + k for k in buckets if k.hour == h + ): + hour_avgs[h] = val["sum"] / val["count"] + + x_labels = [f"{h:02d}:00" for h in range(24)] + y_vals = [hour_avgs.get(h, None) for h in range(24)] + point_colors = [ + "#2ecc71" if v is not None and v < 0.4 + else "#ff8c42" if v is not None and v < 0.7 + else "#ff4b4b" if v is not None + else "rgba(0,0,0,0)" + for v in y_vals + ] + + fig = go.Figure() + fig.add_trace(go.Scatter( + x=x_labels, + y=y_vals, + mode="lines+markers", + connectgaps=True, + line=dict(color="#ff4b4b", width=2, shape="spline"), + fill="tozeroy", + fillcolor="rgba(255,75,75,0.07)", + marker=dict( + color=point_colors, + size=8, + line=dict(color="#0a0d14", width=1.5), + ), + hovertemplate="%{x}
Avg score: %{y:.3f}", + showlegend=False, + )) + fig.update_layout( + showlegend=False, + margin=dict(l=10, r=10, t=10, b=10), + paper_bgcolor="#0a0d14", + plot_bgcolor="#0a0d14", + height=220, + ) + fig.update_xaxes( + showgrid=False, color="#5a6080", tickfont_size=10, + tickmode="array", + tickvals=[f"{h:02d}:00" for h in range(0, 24, 3)], + ticktext=[f"{h:02d}:00" for h in range(0, 24, 3)], + ) + fig.update_yaxes(range=[0, 1], showgrid=True, + gridcolor="#1e2540", color="#5a6080", tickfont_size=10) + st.plotly_chart(fig, use_container_width=True) + else: + st.info("No time series data yet. Fetch posts first.") + + with hist_col: + st.markdown("#### Score distribution") + scores = [float(r.get("score", 0) or 0) for r in batch_rows] + if scores: + bin_labels = ["0-0.2", "0.2-0.4", "0.4-0.6", "0.6-0.8", "0.8-1.0"] + bin_styles = [ + ("rgba(46,204,113,0.15)", "#2ecc71"), + ("rgba(163,230,53,0.15)", "#a3e635"), + ("rgba(255,235,59,0.15)", "#ffeb3b"), + ("rgba(255,140,66,0.15)", "#ff8c42"), + ("rgba(255,75,75,0.18)", "#ff4b4b"), + ] + bins = [0.0, 0.2, 0.4, 0.6, 0.8, 1.01] + counts = [0] * 5 + for s in scores: + for i in range(5): + if bins[i] <= s < bins[i + 1]: + counts[i] += 1 + break + fig2 = go.Figure() + for label, count, (fill, line) in zip(bin_labels, counts, bin_styles): + fig2.add_trace(go.Bar( + x=[label], y=[count], + marker_color=fill, + marker_line_color=line, + marker_line_width=1.5, + hovertemplate=f"{label}: {count} posts", + showlegend=False, + )) + fig2.update_layout( + showlegend=False, + margin=dict(l=5, r=5, t=10, b=10), + paper_bgcolor="#0a0d14", + plot_bgcolor="#0a0d14", + height=220, bargap=0.2, + ) + fig2.update_xaxes(showgrid=False, color="#5a6080", tickfont_size=9) + fig2.update_yaxes(showgrid=True, gridcolor="#1e2540", color="#5a6080", tickfont_size=9) + st.plotly_chart(fig2, use_container_width=True) + else: + st.info("Fetch posts to see score distribution.") + + # ── Activity heatmap ─────────────────────────────────────────────────── + st.markdown("#### Activity heatmap") + # WHY heatmap: reveals temporal patterns in toxic speech — e.g. spikes + # on weekends or late night — that raw numbers can't show. + # GitHub-style contribution grid is immediately readable to developers. + hm: dict = defaultdict(lambda: {"sum": 0.0, "count": 0}) + for row in history: + dt = _parse_dt(row.get("created_at")) + if not dt: + continue + hm[(dt.weekday(), dt.hour)]["sum"] += float(row.get("score", 0) or 0) + hm[(dt.weekday(), dt.hour)]["count"] += 1 + + if hm: + max_avg = max( + (v["sum"] / v["count"] for v in hm.values() if v["count"]), default=1.0 + ) or 1.0 + + days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] + rows_html = ["
"] + + # Hour labels row + hour_labels = "
" + hour_labels += "
" + for h in range(24): + label = str(h) if h % 6 == 0 else "" + hour_labels += f"
{label}
" + hour_labels += "
" + rows_html.append(hour_labels) + + for d_idx, day in enumerate(days): + row_html = "
" + row_html += f"
{day}
" + for h in range(24): + cell = hm.get((d_idx, h)) + if cell and cell["count"]: + intensity = (cell["sum"] / cell["count"]) / max_avg + r_val = int(30 + (255 - 30) * intensity) + g_val = int(37 + (75 - 37) * intensity) + b_val = int(64 + (75 - 64) * intensity) + bg = f"rgb({r_val},{g_val},{b_val})" + title = f"{day} {h:02d}:00 — avg {cell['sum']/cell['count']:.2f}" + else: + bg = "#1a1d2e" + title = f"{day} {h:02d}:00 — no data" + row_html += f"
" + row_html += "
" + rows_html.append(row_html) + + rows_html.append("
") + rows_html.append(""" +
+ Low +
+ High toxicity +
""") + + st.markdown("".join(rows_html), unsafe_allow_html=True) + else: + st.info("Not enough data for heatmap yet.") + + st.markdown("
", unsafe_allow_html=True) + + # ── Recent posts ─────────────────────────────────────────────────────── + st.markdown("#### Recent posts (last batch)") + if batch_rows: + cards_html = ["
"] + for row in batch_rows[:20]: + text = (row.get("text") or "").strip() + score = float(row.get("score", 0) or 0) + sc = "score-high" if score >= 0.7 else ("score-mid" if score >= 0.4 else "score-low") + # WHY prefer stored query_term over vocab search: + # query_term is set at fetch time so it's always accurate. + # vocab search is a fallback for older rows that predate this field. + matched = row.get("query_term") or next((t for t in vocab if t and t in text.lower()), "—") + cards_html.append(f"""
+
{score:.3f}
+
{escape(text[:110])}
+
{escape(matched)}
+
""") + cards_html.append("
") + st.markdown("".join(cards_html), unsafe_allow_html=True) + else: + st.info("Click 'Fetch & Analyze' to see recent posts.") + + with graph_tab: + # Graph controls live inside the tab. + # WHY: we use a session_state trigger flag so the button click stores + # intent in session_state. On the NEXT rerun (which re-renders the tab + # we are already on), the flag is read and graph is built. This avoids + # the tab-reset issue caused by st.button triggering a full rerun. + info_col, ctrl_col = st.columns([3, 1]) + with info_col: + st.markdown("""
+
How to read this graph
+
+ Words that frequently appear together in algospeak posts are connected. + Node size = frequency  |  + red >70% toxic   + orange 40-70% mixed   + green <40% benign +
+
""", unsafe_allow_html=True) + with ctrl_col: + min_cooccurrence = st.slider("Min co-occurrences", 2, 10, + st.session_state.last_min_cooc, key="graph_cooc_slider") + _ = st.checkbox("Toxic posts only", value=False, key="toxic_only_graph") + if st.button("Build Graph", use_container_width=True, key="build_graph_btn"): + st.session_state.build_graph_trigger = True + st.session_state.build_graph_cooc = int(min_cooccurrence) + + if st.session_state.get("build_graph_trigger"): + st.session_state.build_graph_trigger = False + cooc = st.session_state.get("build_graph_cooc", 3) + with st.spinner("Building co-occurrence graph..."): + graph = build_cooccurrence_graph(min_cooccurrence=cooc) + st.session_state.last_min_cooc = cooc + if graph.number_of_nodes() == 0: + st.session_state.graph_html = None + st.warning("Not enough data. Fetch more posts first.") + else: + st.session_state.graph_html = graph_to_pyvis( + graph, toxic_only=st.session_state.get("toxic_only_graph", False) + ) + + if st.session_state.graph_html: + st.components.v1.html(st.session_state.graph_html, height=620, scrolling=True) + else: + st.info("Adjust settings above and click 'Build Graph' to visualize word co-occurrences.") + + # ── Term comparison tab ──────────────────────────────────────────────────── + with compare_tab: + st.markdown("#### Compare two terms side by side") + st.markdown("
Select two algospeak terms to compare their toxicity profiles from all stored posts.
", unsafe_allow_html=True) + + all_opts = list(ALGOSPEAK_QUERIES) + [t for t in st.session_state.custom_terms if t not in ALGOSPEAK_QUERIES] + col_a, col_b = st.columns(2) + with col_a: + term_a = st.selectbox("Term A", all_opts, index=0, key="compare_a") + with col_b: + term_b = st.selectbox("Term B", all_opts, index=min(1, len(all_opts)-1), key="compare_b") + + if term_a == term_b: + st.warning("Select two different terms to compare.") + else: + # WHY: filter history per term, then compute the same metrics used in + # Overview so the numbers are directly comparable side by side. + def term_stats(term): + posts = [r for r in history if term in (r.get("text") or "").lower()] + if not posts: + return None + scores = [float(r.get("score", 0) or 0) for r in posts] + toxic_n = sum(1 for r in posts if (r.get("label") or "").lower() == "toxic") + return { + "count": len(posts), + "toxic_rate": toxic_n / len(posts) * 100, + "avg_score": sum(scores) / len(scores), + "max_score": max(scores), + "posts": posts, + } + + stats_a = term_stats(term_a) + stats_b = term_stats(term_b) + + if not stats_a or not stats_b: + st.info("Not enough data for one or both terms. Fetch more posts first.") + else: + # Metric cards + ca, cb = st.columns(2) + for col, term, stats in [(ca, term_a, stats_a), (cb, term_b, stats_b)]: + color = "#ff4b4b" if stats["toxic_rate"] >= 70 else ("#ff8c42" if stats["toxic_rate"] >= 40 else "#2ecc71") + with col: + st.markdown(f"""
+
"{term}"
+
+
Posts
+
{stats["count"]}
+
Toxic rate
+
{stats["toxic_rate"]:.1f}%
+
Avg score
+
{stats["avg_score"]:.3f}
+
Max score
+
{stats["max_score"]:.3f}
+
""", unsafe_allow_html=True) + + # Side by side score distribution + st.markdown("#### Score distribution comparison") + bins = [0.0, 0.2, 0.4, 0.6, 0.8, 1.01] + bin_labels = ["0-0.2", "0.2-0.4", "0.4-0.6", "0.6-0.8", "0.8-1.0"] + + def bin_scores(posts): + counts = [0] * 5 + for r in posts: + s = float(r.get("score", 0) or 0) + for i in range(5): + if bins[i] <= s < bins[i+1]: + counts[i] += 1 + break + return counts + + counts_a = bin_scores(stats_a["posts"]) + counts_b = bin_scores(stats_b["posts"]) + + fig_cmp = go.Figure() + fig_cmp.add_trace(go.Bar(name=term_a, x=bin_labels, y=counts_a, + marker_color="rgba(166,176,255,0.2)", marker_line_color="#a6b0ff", marker_line_width=1.5)) + fig_cmp.add_trace(go.Bar(name=term_b, x=bin_labels, y=counts_b, + marker_color="rgba(255,140,66,0.2)", marker_line_color="#ff8c42", marker_line_width=1.5)) + fig_cmp.update_layout( + barmode="group", paper_bgcolor="#0a0d14", plot_bgcolor="#0a0d14", + legend=dict(font=dict(color="#e8eaf0")), + margin=dict(l=10, r=10, t=10, b=10), height=260, + ) + fig_cmp.update_xaxes(showgrid=False, color="#5a6080") + fig_cmp.update_yaxes(showgrid=True, gridcolor="#1e2540", color="#5a6080") + st.plotly_chart(fig_cmp, use_container_width=True) + + # ── Export tab ───────────────────────────────────────────────────────────── + with export_tab: + st.markdown("#### Export data") + st.markdown("
Download posts from the current session or the full database history.
", unsafe_allow_html=True) + + export_source = st.radio("Data source", ["Last batch (current fetch)", "Full history (all stored posts)"], + horizontal=True, key="export_source") + export_rows = batch_rows if export_source.startswith("Last") else history + + if not export_rows: + st.info("No data to export yet. Fetch posts first.") + else: + import csv + import io + import json as _json + + # WHY io.StringIO: lets us build the CSV entirely in memory without + # writing to disk — important for a stateless server environment like + # HuggingFace Spaces where you don't control the filesystem. + csv_buf = io.StringIO() + writer = csv.DictWriter(csv_buf, fieldnames=["text", "label", "score", "query_term", "created_at"]) + writer.writeheader() + for r in export_rows: + writer.writerow({ + "text": (r.get("text") or "").replace("\n", " "), + "label": r.get("label", ""), + "score": f'{float(r.get("score", 0) or 0):.4f}', + "query_term": r.get("query_term", ""), + "created_at": r.get("created_at", ""), + }) + + json_buf = _json.dumps(export_rows, ensure_ascii=False, indent=2, default=str) + + st.markdown(f"
{len(export_rows)} posts ready to export
", unsafe_allow_html=True) + + dl_csv, dl_json = st.columns(2) + with dl_csv: + st.download_button( + label="Download CSV", + data=csv_buf.getvalue(), + file_name="algoscope_export.csv", + mime="text/csv", + use_container_width=True, + ) + with dl_json: + st.download_button( + label="Download JSON", + data=json_buf, + file_name="algoscope_export.json", + mime="application/json", + use_container_width=True, + ) + + # Preview table + st.markdown("#### Preview (first 10 rows)") + preview_html = ["
", + "", + "", + "", + "", + ""] + for r in export_rows[:10]: + score = float(r.get("score", 0) or 0) + sc = "score-high" if score >= 0.7 else ("score-mid" if score >= 0.4 else "score-low") + preview_html.append(f""" + + + + + """) + preview_html.append("
ScoreLabelTermText
{score:.3f}{escape(r.get("label",""))}{escape(r.get("query_term",""))}{escape((r.get("text","") or "")[:80])}
") + st.markdown("".join(preview_html), unsafe_allow_html=True) + + +if __name__ == "__main__": + main() diff --git a/frontend/.env.development b/frontend/.env.development new file mode 100644 index 0000000000000000000000000000000000000000..82c4bd3fcfd89cacdd640d6aa8c019e98ba87ff8 --- /dev/null +++ b/frontend/.env.development @@ -0,0 +1,4 @@ +# WHY empty: in production, the frontend is served by the same FastAPI server +# on port 7860. Empty VITE_API_URL means fetch() uses relative URLs (/posts, +# /fetch-and-analyze etc.) which automatically go to the correct host. +VITE_API_URL= diff --git a/frontend/.env.production b/frontend/.env.production new file mode 100644 index 0000000000000000000000000000000000000000..9515bec60238e78a7a11d1c9b685af1b94b20c4f --- /dev/null +++ b/frontend/.env.production @@ -0,0 +1,4 @@ +# WHY empty: in production, React is served by the same FastAPI process. +# All fetch() calls use a relative URL (e.g. /posts), which resolves to +# the same origin — no CORS, no separate server address needed. +VITE_API_URL= diff --git a/frontend/dist/assets/index-CZMDWpNf.js b/frontend/dist/assets/index-CZMDWpNf.js new file mode 100644 index 0000000000000000000000000000000000000000..c6be7a97e199a891663259f42c52062d19431bf2 --- /dev/null +++ b/frontend/dist/assets/index-CZMDWpNf.js @@ -0,0 +1,111 @@ +(function(){const 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w=this.getAxisMotionValue(x);w&&(this.originPoint[x]+=y[x].translate,w.set(w.get()+y[x].translate))}),this.visualElement.render())}));return()=>{h(),o(),f(),g&&g(),a&&a()}}getProps(){const r=this.visualElement.getProps(),{drag:o=!1,dragDirectionLock:a=!1,dragPropagation:u=!1,dragConstraints:d=!1,dragElastic:f=ou,dragMomentum:h=!0}=r;return{...r,drag:o,dragDirectionLock:a,dragPropagation:u,dragConstraints:d,dragElastic:f,dragMomentum:h}}}function ep(n){let r=!0;return()=>{if(r){r=!1;return}n()}}function Ow(n,r,o){const a=ah(n,ep(o)),u=ah(r,ep(o));return()=>{a(),u()}}function Ho(n,r,o){return(r===!0||r===n)&&(o===null||o===n)}function Bw(n,r=10){let o=null;return Math.abs(n.y)>r?o="y":Math.abs(n.x)>r&&(o="x"),o}class Ww extends Tn{constructor(r){super(r),this.removeGroupControls=kt,this.removeListeners=kt,this.controls=new 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Tn{constructor(){super(...arguments),this.hasEnteredView=!1,this.isInView=!1}startObserver(){var g;(g=this.stopObserver)==null||g.call(this);const{viewport:r={}}=this.node.getProps(),{root:o,margin:a,amount:u="some",once:d}=r,f={root:o?o.current:void 0,rootMargin:a,threshold:typeof u=="number"?u:Jw[u]},h=y=>{const{isIntersecting:m}=y;if(this.isInView===m||(this.isInView=m,d&&!m&&this.hasEnteredView))return;m&&(this.hasEnteredView=!0),this.node.animationState&&this.node.animationState.setActive("whileInView",m);const{onViewportEnter:x,onViewportLeave:w}=this.node.getProps(),k=m?x:w;k&&k(y)};this.stopObserver=Zw(this.node.current,f,h)}mount(){this.startObserver()}update(){if(typeof IntersectionObserver>"u")return;const{props:r,prevProps:o}=this.node;["amount","margin","root"].some(tS(r,o))&&this.startObserver()}unmount(){var r;(r=this.stopObserver)==null||r.call(this),this.hasEnteredView=!1,this.isInView=!1}}function tS({viewport:n={}},{viewport:r={}}={}){return o=>n[o]!==r[o]}const 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--color-purple-400: oklch(71.4% .203 305.504); --color-purple-500: oklch(62.7% .265 303.9); --color-purple-600: oklch(55.8% .288 302.321); --color-purple-700: oklch(49.6% .265 301.924); --color-purple-800: oklch(43.8% .218 303.724); --color-purple-900: oklch(38.1% .176 304.987); --color-purple-950: oklch(29.1% .149 302.717); --color-fuchsia-50: oklch(97.7% .017 320.058); --color-fuchsia-100: oklch(95.2% .037 318.852); --color-fuchsia-200: oklch(90.3% .076 319.62); --color-fuchsia-300: oklch(83.3% .145 321.434); --color-fuchsia-400: oklch(74% .238 322.16); --color-fuchsia-500: oklch(66.7% .295 322.15); --color-fuchsia-600: oklch(59.1% .293 322.896); --color-fuchsia-700: oklch(51.8% .253 323.949); --color-fuchsia-800: oklch(45.2% .211 324.591); --color-fuchsia-900: oklch(40.1% .17 325.612); --color-fuchsia-950: oklch(29.3% .136 325.661); --color-pink-50: oklch(97.1% .014 343.198); --color-pink-100: oklch(94.8% .028 342.258); --color-pink-200: oklch(89.9% .061 343.231); --color-pink-300: oklch(82.3% .12 346.018); --color-pink-400: oklch(71.8% .202 349.761); --color-pink-500: oklch(65.6% .241 354.308); --color-pink-600: oklch(59.2% .249 .584); --color-pink-700: oklch(52.5% .223 3.958); --color-pink-800: oklch(45.9% .187 3.815); --color-pink-900: oklch(40.8% .153 2.432); --color-pink-950: oklch(28.4% .109 3.907); --color-rose-50: oklch(96.9% .015 12.422); --color-rose-100: oklch(94.1% .03 12.58); --color-rose-200: oklch(89.2% .058 10.001); --color-rose-300: oklch(81% .117 11.638); --color-rose-400: oklch(71.2% .194 13.428); --color-rose-500: oklch(64.5% .246 16.439); --color-rose-600: oklch(58.6% .253 17.585); --color-rose-700: oklch(51.4% .222 16.935); --color-rose-800: oklch(45.5% .188 13.697); --color-rose-900: oklch(41% .159 10.272); --color-rose-950: oklch(27.1% .105 12.094); --color-slate-50: oklch(98.4% .003 247.858); --color-slate-100: oklch(96.8% .007 247.896); --color-slate-200: oklch(92.9% .013 255.508); --color-slate-300: oklch(86.9% .022 252.894); --color-slate-400: oklch(70.4% .04 256.788); --color-slate-500: oklch(55.4% .046 257.417); --color-slate-600: oklch(44.6% .043 257.281); --color-slate-700: oklch(37.2% .044 257.287); --color-slate-800: oklch(27.9% .041 260.031); --color-slate-900: oklch(20.8% .042 265.755); --color-slate-950: oklch(12.9% .042 264.695); --color-gray-50: oklch(98.5% .002 247.839); --color-gray-100: oklch(96.7% .003 264.542); --color-gray-200: oklch(92.8% .006 264.531); --color-gray-300: oklch(87.2% .01 258.338); --color-gray-400: oklch(70.7% .022 261.325); --color-gray-500: oklch(55.1% .027 264.364); --color-gray-600: oklch(44.6% .03 256.802); --color-gray-700: oklch(37.3% .034 259.733); --color-gray-800: oklch(27.8% .033 256.848); --color-gray-900: oklch(21% .034 264.665); --color-gray-950: oklch(13% .028 261.692); --color-zinc-50: oklch(98.5% 0 0); --color-zinc-100: oklch(96.7% .001 286.375); --color-zinc-200: oklch(92% .004 286.32); --color-zinc-300: oklch(87.1% .006 286.286); --color-zinc-400: oklch(70.5% .015 286.067); --color-zinc-500: oklch(55.2% .016 285.938); --color-zinc-600: oklch(44.2% .017 285.786); --color-zinc-700: oklch(37% .013 285.805); --color-zinc-800: oklch(27.4% .006 286.033); --color-zinc-900: oklch(21% .006 285.885); --color-zinc-950: oklch(14.1% .005 285.823); --color-neutral-50: oklch(98.5% 0 0); --color-neutral-100: oklch(97% 0 0); --color-neutral-200: oklch(92.2% 0 0); --color-neutral-300: oklch(87% 0 0); --color-neutral-400: oklch(70.8% 0 0); --color-neutral-500: oklch(55.6% 0 0); --color-neutral-600: oklch(43.9% 0 0); --color-neutral-700: oklch(37.1% 0 0); --color-neutral-800: oklch(26.9% 0 0); --color-neutral-900: oklch(20.5% 0 0); --color-neutral-950: oklch(14.5% 0 0); --color-stone-50: oklch(98.5% .001 106.423); --color-stone-100: oklch(97% .001 106.424); --color-stone-200: oklch(92.3% .003 48.717); --color-stone-300: oklch(86.9% .005 56.366); --color-stone-400: oklch(70.9% .01 56.259); --color-stone-500: oklch(55.3% .013 58.071); --color-stone-600: oklch(44.4% .011 73.639); --color-stone-700: oklch(37.4% .01 67.558); --color-stone-800: oklch(26.8% .007 34.298); --color-stone-900: oklch(21.6% .006 56.043); --color-stone-950: oklch(14.7% .004 49.25); --color-black: #000; --color-white: #fff; --spacing: .25rem; --breakpoint-sm: 40rem; --breakpoint-md: 48rem; --breakpoint-lg: 64rem; --breakpoint-xl: 80rem; --breakpoint-2xl: 96rem; --container-3xs: 16rem; --container-2xs: 18rem; --container-xs: 20rem; --container-sm: 24rem; --container-md: 28rem; --container-lg: 32rem; --container-xl: 36rem; --container-2xl: 42rem; --container-3xl: 48rem; --container-4xl: 56rem; --container-5xl: 64rem; --container-6xl: 72rem; --container-7xl: 80rem; --text-xs: .75rem; --text-xs--line-height: calc(1 / .75); --text-sm: .875rem; --text-sm--line-height: calc(1.25 / .875); --text-base: 1rem; --text-base--line-height: 1.5 ; --text-lg: 1.125rem; --text-lg--line-height: calc(1.75 / 1.125); --text-xl: 1.25rem; --text-xl--line-height: calc(1.75 / 1.25); --text-2xl: 1.5rem; --text-2xl--line-height: calc(2 / 1.5); --text-3xl: 1.875rem; --text-3xl--line-height: 1.2 ; --text-4xl: 2.25rem; --text-4xl--line-height: calc(2.5 / 2.25); --text-5xl: 3rem; --text-5xl--line-height: 1; --text-6xl: 3.75rem; --text-6xl--line-height: 1; --text-7xl: 4.5rem; --text-7xl--line-height: 1; --text-8xl: 6rem; --text-8xl--line-height: 1; --text-9xl: 8rem; --text-9xl--line-height: 1; --font-weight-thin: 100; --font-weight-extralight: 200; --font-weight-light: 300; --font-weight-normal: 400; --font-weight-medium: 500; --font-weight-semibold: 600; --font-weight-bold: 700; --font-weight-extrabold: 800; --font-weight-black: 900; --tracking-tighter: -.05em; --tracking-tight: -.025em; --tracking-normal: 0em; --tracking-wide: .025em; --tracking-wider: .05em; --tracking-widest: .1em; --leading-tight: 1.25; --leading-snug: 1.375; --leading-normal: 1.5; --leading-relaxed: 1.625; --leading-loose: 2; --radius-xs: .125rem; --radius-sm: .25rem; --radius-md: .375rem; --radius-lg: .5rem; --radius-xl: .75rem; --radius-2xl: 1rem; --radius-3xl: 1.5rem; --radius-4xl: 2rem; --shadow-2xs: 0 1px rgb(0 0 0 / .05); --shadow-xs: 0 1px 2px 0 rgb(0 0 0 / .05); --shadow-sm: 0 1px 3px 0 rgb(0 0 0 / .1), 0 1px 2px -1px rgb(0 0 0 / .1); --shadow-md: 0 4px 6px -1px rgb(0 0 0 / .1), 0 2px 4px -2px rgb(0 0 0 / .1); --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / .1), 0 4px 6px -4px rgb(0 0 0 / .1); --shadow-xl: 0 20px 25px -5px rgb(0 0 0 / .1), 0 8px 10px -6px rgb(0 0 0 / .1); --shadow-2xl: 0 25px 50px -12px rgb(0 0 0 / .25); --inset-shadow-2xs: inset 0 1px rgb(0 0 0 / .05); --inset-shadow-xs: inset 0 1px 1px rgb(0 0 0 / .05); --inset-shadow-sm: inset 0 2px 4px rgb(0 0 0 / .05); --drop-shadow-xs: 0 1px 1px rgb(0 0 0 / .05); --drop-shadow-sm: 0 1px 2px rgb(0 0 0 / .15); --drop-shadow-md: 0 3px 3px rgb(0 0 0 / .12); --drop-shadow-lg: 0 4px 4px rgb(0 0 0 / .15); --drop-shadow-xl: 0 9px 7px rgb(0 0 0 / .1); --drop-shadow-2xl: 0 25px 25px rgb(0 0 0 / .15); --text-shadow-2xs: 0px 1px 0px rgb(0 0 0 / .15); --text-shadow-xs: 0px 1px 1px rgb(0 0 0 / .2); --text-shadow-sm: 0px 1px 0px rgb(0 0 0 / .075), 0px 1px 1px rgb(0 0 0 / .075), 0px 2px 2px rgb(0 0 0 / .075); --text-shadow-md: 0px 1px 1px rgb(0 0 0 / .1), 0px 1px 2px rgb(0 0 0 / .1), 0px 2px 4px rgb(0 0 0 / .1); --text-shadow-lg: 0px 1px 2px rgb(0 0 0 / .1), 0px 3px 2px rgb(0 0 0 / .1), 0px 4px 8px rgb(0 0 0 / .1); --ease-in: cubic-bezier(.4, 0, 1, 1); --ease-out: cubic-bezier(0, 0, .2, 1); --ease-in-out: cubic-bezier(.4, 0, .2, 1); --animate-spin: spin 1s linear infinite; --animate-ping: ping 1s cubic-bezier(0, 0, .2, 1) infinite; --animate-pulse: pulse 2s cubic-bezier(.4, 0, .6, 1) infinite; --animate-bounce: bounce 1s infinite; @keyframes spin { to { transform: rotate(360deg); } } @keyframes ping { 75%, 100% { transform: scale(2); opacity: 0; } } @keyframes pulse { 50% { opacity: .5; } } @keyframes bounce { 0%, 100% { transform: translateY(-25%); animation-timing-function: cubic-bezier(.8, 0, 1, 1); } 50% { transform: none; animation-timing-function: cubic-bezier(0, 0, .2, 1); } } --blur-xs: 4px; --blur-sm: 8px; --blur-md: 12px; --blur-lg: 16px; --blur-xl: 24px; --blur-2xl: 40px; --blur-3xl: 64px; --perspective-dramatic: 100px; --perspective-near: 300px; --perspective-normal: 500px; --perspective-midrange: 800px; --perspective-distant: 1200px; --aspect-video: 16 / 9; --default-transition-duration: .15s; --default-transition-timing-function: cubic-bezier(.4, 0, .2, 1); --default-font-family: --theme(--font-sans, initial); --default-font-feature-settings: --theme( --font-sans--font-feature-settings, initial ); --default-font-variation-settings: --theme( --font-sans--font-variation-settings, initial ); --default-mono-font-family: --theme(--font-mono, initial); --default-mono-font-feature-settings: --theme( --font-mono--font-feature-settings, initial ); --default-mono-font-variation-settings: --theme( --font-mono--font-variation-settings, initial ); }@theme default inline reference{ --blur: 8px; --shadow: 0 1px 3px 0 rgb(0 0 0 / .1), 0 1px 2px -1px rgb(0 0 0 / .1); --shadow-inner: inset 0 2px 4px 0 rgb(0 0 0 / .05); --drop-shadow: 0 1px 2px rgb(0 0 0 / .1), 0 1px 1px rgb(0 0 0 / .06); --radius: .25rem; --max-width-prose: 65ch; }}@layer base{*,:after,:before,::backdrop,::file-selector-button{box-sizing:border-box;margin:0;padding:0;border:0 solid}html,:host{line-height:1.5;-webkit-text-size-adjust:100%;-moz-tab-size:4;tab-size:4;font-family:--theme(--default-font-family,ui-sans-serif,system-ui,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji");font-feature-settings:--theme(--default-font-feature-settings,normal);font-variation-settings:--theme(--default-font-variation-settings,normal);-webkit-tap-highlight-color:transparent}hr{height:0;color:inherit;border-top-width:1px}abbr:where([title]){-webkit-text-decoration:underline dotted;text-decoration:underline dotted}h1,h2,h3,h4,h5,h6{font-size:inherit;font-weight:inherit}a{color:inherit;-webkit-text-decoration:inherit;text-decoration:inherit}b,strong{font-weight:bolder}code,kbd,samp,pre{font-family:--theme(--default-mono-font-family,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace);font-feature-settings:--theme(--default-mono-font-feature-settings,normal);font-variation-settings:--theme(--default-mono-font-variation-settings,normal);font-size:1em}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}table{text-indent:0;border-color:inherit;border-collapse:collapse}:-moz-focusring{outline:auto}progress{vertical-align:baseline}summary{display:list-item}ol,ul,menu{list-style:none}img,svg,video,canvas,audio,iframe,embed,object{display:block;vertical-align:middle}img,video{max-width:100%;height:auto}button,input,select,optgroup,textarea,::file-selector-button{font:inherit;font-feature-settings:inherit;font-variation-settings:inherit;letter-spacing:inherit;color:inherit;border-radius:0;background-color:transparent;opacity:1}:where(select:is([multiple],[size])) optgroup{font-weight:bolder}:where(select:is([multiple],[size])) optgroup option{padding-inline-start:20px}::file-selector-button{margin-inline-end:4px}::placeholder{opacity:1}@supports (not (-webkit-appearance: -apple-pay-button)) or (contain-intrinsic-size: 1px){::placeholder{color:color-mix(in oklab,currentcolor 50%,transparent)}}textarea{resize:vertical}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-date-and-time-value{min-height:1lh;text-align:inherit}::-webkit-datetime-edit{display:inline-flex}::-webkit-datetime-edit-fields-wrapper{padding:0}::-webkit-datetime-edit,::-webkit-datetime-edit-year-field,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-minute-field,::-webkit-datetime-edit-second-field,::-webkit-datetime-edit-millisecond-field,::-webkit-datetime-edit-meridiem-field{padding-block:0}::-webkit-calendar-picker-indicator{line-height:1}:-moz-ui-invalid{box-shadow:none}button,input:where([type=button],[type=reset],[type=submit]),::file-selector-button{-webkit-appearance:button;-moz-appearance:button;appearance:button}::-webkit-inner-spin-button,::-webkit-outer-spin-button{height:auto}[hidden]:where(:not([hidden=until-found])){display:none!important}}@layer utilities{@tailwind utilities;}}@property --tw-animation-delay{syntax:"*";inherits:false;initial-value:0s}@property --tw-animation-direction{syntax:"*";inherits:false;initial-value:normal}@property --tw-animation-duration{syntax:"*";inherits:false}@property --tw-animation-fill-mode{syntax:"*";inherits:false;initial-value:none}@property --tw-animation-iteration-count{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-blur{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-opacity{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-rotate{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-scale{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-translate-y{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-blur{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-opacity{syntax:"*";inherits:false;initial-value:1}@property --tw-exit-rotate{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-scale{syntax:"*";inherits:false;initial-value:1}@property --tw-exit-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-translate-y{syntax:"*";inherits:false;initial-value:0}@theme inline{--animation-delay-0: 0s; --animation-delay-75: 75ms; --animation-delay-100: .1s; --animation-delay-150: .15s; --animation-delay-200: .2s; --animation-delay-300: .3s; --animation-delay-500: .5s; --animation-delay-700: .7s; --animation-delay-1000: 1s; --animation-repeat-0: 0; --animation-repeat-1: 1; --animation-repeat-infinite: infinite; --animation-direction-normal: normal; --animation-direction-reverse: reverse; --animation-direction-alternate: alternate; --animation-direction-alternate-reverse: alternate-reverse; --animation-fill-mode-none: none; --animation-fill-mode-forwards: forwards; --animation-fill-mode-backwards: backwards; --animation-fill-mode-both: both; --percentage-0: 0; --percentage-5: .05; --percentage-10: .1; --percentage-15: .15; --percentage-20: .2; --percentage-25: .25; --percentage-30: .3; --percentage-35: .35; --percentage-40: .4; --percentage-45: .45; --percentage-50: .5; --percentage-55: .55; --percentage-60: .6; --percentage-65: .65; --percentage-70: .7; --percentage-75: .75; --percentage-80: .8; --percentage-85: .85; --percentage-90: .9; --percentage-95: .95; --percentage-100: 1; --percentage-translate-full: 1; --animate-in: enter var(--tw-animation-duration,var(--tw-duration,.15s))var(--tw-ease,ease)var(--tw-animation-delay,0s)var(--tw-animation-iteration-count,1)var(--tw-animation-direction,normal)var(--tw-animation-fill-mode,none); --animate-out: exit var(--tw-animation-duration,var(--tw-duration,.15s))var(--tw-ease,ease)var(--tw-animation-delay,0s)var(--tw-animation-iteration-count,1)var(--tw-animation-direction,normal)var(--tw-animation-fill-mode,none); @keyframes enter { from { opacity: var(--tw-enter-opacity,1); transform: translate3d(var(--tw-enter-translate-x,0),var(--tw-enter-translate-y,0),0)scale3d(var(--tw-enter-scale,1),var(--tw-enter-scale,1),var(--tw-enter-scale,1))rotate(var(--tw-enter-rotate,0)); filter: blur(var(--tw-enter-blur,0)); }}@keyframes exit { to { opacity: var(--tw-exit-opacity,1); transform: translate3d(var(--tw-exit-translate-x,0),var(--tw-exit-translate-y,0),0)scale3d(var(--tw-exit-scale,1),var(--tw-exit-scale,1),var(--tw-exit-scale,1))rotate(var(--tw-exit-rotate,0)); filter: blur(var(--tw-exit-blur,0)); }}--animate-accordion-down: accordion-down var(--tw-animation-duration,var(--tw-duration,.2s))var(--tw-ease,ease-out)var(--tw-animation-delay,0s)var(--tw-animation-iteration-count,1)var(--tw-animation-direction,normal)var(--tw-animation-fill-mode,none); --animate-accordion-up: accordion-up var(--tw-animation-duration,var(--tw-duration,.2s))var(--tw-ease,ease-out)var(--tw-animation-delay,0s)var(--tw-animation-iteration-count,1)var(--tw-animation-direction,normal)var(--tw-animation-fill-mode,none); --animate-collapsible-down: collapsible-down var(--tw-animation-duration,var(--tw-duration,.2s))var(--tw-ease,ease-out)var(--tw-animation-delay,0s)var(--tw-animation-iteration-count,1)var(--tw-animation-direction,normal)var(--tw-animation-fill-mode,none); --animate-collapsible-up: collapsible-up var(--tw-animation-duration,var(--tw-duration,.2s))var(--tw-ease,ease-out)var(--tw-animation-delay,0s)var(--tw-animation-iteration-count,1)var(--tw-animation-direction,normal)var(--tw-animation-fill-mode,none); @keyframes accordion-down { from { height: 0; }to { height: var(--radix-accordion-content-height,var(--bits-accordion-content-height,var(--reka-accordion-content-height,var(--kb-accordion-content-height,var(--ngp-accordion-content-height,auto))))); }}@keyframes accordion-up { from { height: var(--radix-accordion-content-height,var(--bits-accordion-content-height,var(--reka-accordion-content-height,var(--kb-accordion-content-height,var(--ngp-accordion-content-height,auto))))); }to { height: 0; }}@keyframes collapsible-down { from { height: 0; }to { height: var(--radix-collapsible-content-height,var(--bits-collapsible-content-height,var(--reka-collapsible-content-height,var(--kb-collapsible-content-height,auto)))); }}@keyframes collapsible-up { from { height: var(--radix-collapsible-content-height,var(--bits-collapsible-content-height,var(--reka-collapsible-content-height,var(--kb-collapsible-content-height,auto)))); }to { height: 0; }}--animate-caret-blink: caret-blink 1.25s ease-out infinite; @keyframes caret-blink { 0%,70%,100% { opacity: 1; }20%,50% { opacity: 0; }}}@utility animation-duration-*{--tw-animation-duration: calc(--value(number)*1ms) ; --tw-animation-duration: --value(--animation-duration-*,[duration],"initial",[*]); animation-duration: calc(--value(number)*1ms) ; animation-duration: --value(--animation-duration-*,[duration],"initial",[*]);}@utility delay-*{animation-delay: calc(--value(number)*1ms) ; animation-delay: --value(--animation-delay-*,[duration],"initial",[*]); --tw-animation-delay: calc(--value(number)*1ms) ; --tw-animation-delay: --value(--animation-delay-*,[duration],"initial",[*]);}@utility repeat-*{animation-iteration-count: --value(--animation-repeat-*,number,"initial",[*]); --tw-animation-iteration-count: --value(--animation-repeat-*,number,"initial",[*]);}@utility direction-*{animation-direction: --value(--animation-direction-*,"initial",[*]); --tw-animation-direction: --value(--animation-direction-*,"initial",[*]);}@utility fill-mode-*{animation-fill-mode: --value(--animation-fill-mode-*,"initial",[*]); --tw-animation-fill-mode: --value(--animation-fill-mode-*,"initial",[*]);}@utility running{animation-play-state: running;}@utility paused{animation-play-state: paused;}@utility play-state-*{animation-play-state: --value("initial",[*]);}@utility blur-in{--tw-enter-blur: 20px;}@utility blur-in-*{--tw-enter-blur: calc(--value(number)*1px) ; --tw-enter-blur: --value(--blur-*,[*]);}@utility blur-out{--tw-exit-blur: 20px;}@utility blur-out-*{--tw-exit-blur: calc(--value(number)*1px) ; --tw-exit-blur: --value(--blur-*,[*]);}@utility fade-in{--tw-enter-opacity: 0;}@utility fade-in-*{--tw-enter-opacity: calc(--value(number)*.01) ; --tw-enter-opacity: --value(--percentage-*,[*]);}@utility fade-out{--tw-exit-opacity: 0;}@utility fade-out-*{--tw-exit-opacity: calc(--value(number)*.01) ; --tw-exit-opacity: --value(--percentage-*,[*]);}@utility zoom-in{--tw-enter-scale: 0;}@utility zoom-in-*{--tw-enter-scale: calc(--value(number)*1%) ; --tw-enter-scale: --value(ratio) ; --tw-enter-scale: --value(--percentage-*,[*]);}@utility -zoom-in-*{--tw-enter-scale: calc(--value(number)*-1%) ; --tw-enter-scale: calc(--value(ratio)*-1) ; --tw-enter-scale: --value(--percentage-*,[*]);}@utility zoom-out{--tw-exit-scale: 0;}@utility zoom-out-*{--tw-exit-scale: calc(--value(number)*1%) ; --tw-exit-scale: --value(ratio) ; --tw-exit-scale: --value(--percentage-*,[*]);}@utility -zoom-out-*{--tw-exit-scale: calc(--value(number)*-1%) ; --tw-exit-scale: calc(--value(ratio)*-1) ; --tw-exit-scale: --value(--percentage-*,[*]);}@utility spin-in{--tw-enter-rotate: 30deg;}@utility spin-in-*{--tw-enter-rotate: calc(--value(number)*1deg) ; --tw-enter-rotate: calc(--value(ratio)*360deg) ; --tw-enter-rotate: --value(--rotate-*,[*]);}@utility -spin-in{--tw-enter-rotate: -30deg;}@utility -spin-in-*{--tw-enter-rotate: calc(--value(number)*-1deg) ; --tw-enter-rotate: calc(--value(ratio)*-360deg) ; --tw-enter-rotate: --value(--rotate-*,[*]);}@utility spin-out{--tw-exit-rotate: 30deg;}@utility spin-out-*{--tw-exit-rotate: calc(--value(number)*1deg) ; --tw-exit-rotate: calc(--value(ratio)*360deg) ; --tw-exit-rotate: --value(--rotate-*,[*]);}@utility -spin-out{--tw-exit-rotate: -30deg;}@utility -spin-out-*{--tw-exit-rotate: calc(--value(number)*-1deg) ; --tw-exit-rotate: calc(--value(ratio)*-360deg) ; --tw-exit-rotate: --value(--rotate-*,[*]);}@utility slide-in-from-top{--tw-enter-translate-y: -100%;}@utility slide-in-from-top-*{--tw-enter-translate-y: --spacing(--value(integer)*-1); --tw-enter-translate-y: calc(--value(--percentage-*,--percentage-translate-*)*-100%) ; --tw-enter-translate-y: calc(--value(ratio)*-100%) ; --tw-enter-translate-y: calc(--value(--translate-*,[percentage],[length])*-1) ;}@utility slide-in-from-bottom{--tw-enter-translate-y: 100%;}@utility slide-in-from-bottom-*{--tw-enter-translate-y: --spacing(--value(integer)); --tw-enter-translate-y: calc(--value(--percentage-*,--percentage-translate-*)*100%) ; --tw-enter-translate-y: calc(--value(ratio)*100%) ; --tw-enter-translate-y: --value(--translate-*,[percentage],[length]);}@utility slide-in-from-left{--tw-enter-translate-x: -100%;}@utility slide-in-from-left-*{--tw-enter-translate-x: --spacing(--value(integer)*-1); --tw-enter-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*-100%) ; --tw-enter-translate-x: calc(--value(ratio)*-100%) ; --tw-enter-translate-x: calc(--value(--translate-*,[percentage],[length])*-1) ;}@utility slide-in-from-right{--tw-enter-translate-x: 100%;}@utility slide-in-from-right-*{--tw-enter-translate-x: --spacing(--value(integer)); --tw-enter-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*100%) ; --tw-enter-translate-x: calc(--value(ratio)*100%) ; --tw-enter-translate-x: --value(--translate-*,[percentage],[length]);}@utility slide-in-from-start{&:dir(ltr){ --tw-enter-translate-x: -100%; }&:dir(rtl){ --tw-enter-translate-x: 100%; }}@utility slide-in-from-start-*{&:where(:dir(ltr),[dir="ltr"],[dir="ltr"]*){ --tw-enter-translate-x: --spacing(--value(integer)*-1); --tw-enter-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*-100%) ; --tw-enter-translate-x: calc(--value(ratio)*-100%) ; --tw-enter-translate-x: calc(--value(--translate-*,[percentage],[length])*-1) ; }&:where(:dir(rtl),[dir="rtl"],[dir="rtl"]*){ --tw-enter-translate-x: --spacing(--value(integer)); --tw-enter-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*100%) ; --tw-enter-translate-x: calc(--value(ratio)*100%) ; --tw-enter-translate-x: --value(--translate-*,[percentage],[length]); }}@utility slide-in-from-end{&:dir(ltr){ --tw-enter-translate-x: 100%; }&:dir(rtl){ --tw-enter-translate-x: -100%; }}@utility slide-in-from-end-*{&:where(:dir(ltr),[dir="ltr"],[dir="ltr"]*){ --tw-enter-translate-x: --spacing(--value(integer)); --tw-enter-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*100%) ; --tw-enter-translate-x: calc(--value(ratio)*100%) ; --tw-enter-translate-x: --value(--translate-*,[percentage],[length]); }&:where(:dir(rtl),[dir="rtl"],[dir="rtl"]*){ --tw-enter-translate-x: --spacing(--value(integer)*-1); --tw-enter-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*-100%) ; --tw-enter-translate-x: calc(--value(ratio)*-100%) ; --tw-enter-translate-x: calc(--value(--translate-*,[percentage],[length])*-1) ; }}@utility slide-out-to-top{--tw-exit-translate-y: -100%;}@utility slide-out-to-top-*{--tw-exit-translate-y: --spacing(--value(integer)*-1); --tw-exit-translate-y: calc(--value(--percentage-*,--percentage-translate-*)*-100%) ; --tw-exit-translate-y: calc(--value(ratio)*-100%) ; --tw-exit-translate-y: calc(--value(--translate-*,[percentage],[length])*-1) ;}@utility slide-out-to-bottom{--tw-exit-translate-y: 100%;}@utility slide-out-to-bottom-*{--tw-exit-translate-y: --spacing(--value(integer)); --tw-exit-translate-y: calc(--value(--percentage-*,--percentage-translate-*)*100%) ; --tw-exit-translate-y: calc(--value(ratio)*100%) ; --tw-exit-translate-y: --value(--translate-*,[percentage],[length]);}@utility slide-out-to-left{--tw-exit-translate-x: -100%;}@utility slide-out-to-left-*{--tw-exit-translate-x: --spacing(--value(integer)*-1); --tw-exit-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*-100%) ; --tw-exit-translate-x: calc(--value(ratio)*-100%) ; --tw-exit-translate-x: calc(--value(--translate-*,[percentage],[length])*-1) ;}@utility slide-out-to-right{--tw-exit-translate-x: 100%;}@utility slide-out-to-right-*{--tw-exit-translate-x: --spacing(--value(integer)); --tw-exit-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*100%) ; --tw-exit-translate-x: calc(--value(ratio)*100%) ; --tw-exit-translate-x: --value(--translate-*,[percentage],[length]);}@utility slide-out-to-start{&:dir(ltr){ --tw-exit-translate-x: -100%; }&:dir(rtl){ --tw-exit-translate-x: 100%; }}@utility slide-out-to-start-*{&:where(:dir(ltr),[dir="ltr"],[dir="ltr"]*){ --tw-exit-translate-x: --spacing(--value(integer)*-1); --tw-exit-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*-100%) ; --tw-exit-translate-x: calc(--value(ratio)*-100%) ; --tw-exit-translate-x: calc(--value(--translate-*,[percentage],[length])*-1) ; }&:where(:dir(rtl),[dir="rtl"],[dir="rtl"]*){ --tw-exit-translate-x: --spacing(--value(integer)); --tw-exit-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*100%) ; --tw-exit-translate-x: calc(--value(ratio)*100%) ; --tw-exit-translate-x: --value(--translate-*,[percentage],[length]); }}@utility slide-out-to-end{&:dir(ltr){ --tw-exit-translate-x: 100%; }&:dir(rtl){ --tw-exit-translate-x: -100%; }}@utility slide-out-to-end-*{&:where(:dir(ltr),[dir="ltr"],[dir="ltr"]*){ --tw-exit-translate-x: --spacing(--value(integer)); --tw-exit-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*100%) ; --tw-exit-translate-x: calc(--value(ratio)*100%) ; --tw-exit-translate-x: --value(--translate-*,[percentage],[length]); }&:where(:dir(rtl),[dir="rtl"],[dir="rtl"]*){ --tw-exit-translate-x: --spacing(--value(integer)*-1); --tw-exit-translate-x: calc(--value(--percentage-*,--percentage-translate-*)*-100%) ; --tw-exit-translate-x: calc(--value(ratio)*-100%) ; --tw-exit-translate-x: calc(--value(--translate-*,[percentage],[length])*-1) ; }}@source "../**/*.{js,ts,jsx,tsx}";@custom-variant dark (&:is(.dark *));:root{--font-size: 16px;--background: #ffffff;--foreground: oklch(.145 0 0);--card: #ffffff;--card-foreground: oklch(.145 0 0);--popover: oklch(1 0 0);--popover-foreground: oklch(.145 0 0);--primary: #030213;--primary-foreground: oklch(1 0 0);--secondary: oklch(.95 .0058 264.53);--secondary-foreground: #030213;--muted: #ececf0;--muted-foreground: #717182;--accent: #e9ebef;--accent-foreground: #030213;--destructive: #d4183d;--destructive-foreground: #ffffff;--border: rgba(0, 0, 0, .1);--input: transparent;--input-background: #f3f3f5;--switch-background: #cbced4;--font-weight-medium: 500;--font-weight-normal: 400;--ring: oklch(.708 0 0);--chart-1: oklch(.646 .222 41.116);--chart-2: oklch(.6 .118 184.704);--chart-3: oklch(.398 .07 227.392);--chart-4: oklch(.828 .189 84.429);--chart-5: oklch(.769 .188 70.08);--radius: .625rem;--sidebar: oklch(.985 0 0);--sidebar-foreground: oklch(.145 0 0);--sidebar-primary: #030213;--sidebar-primary-foreground: oklch(.985 0 0);--sidebar-accent: oklch(.97 0 0);--sidebar-accent-foreground: oklch(.205 0 0);--sidebar-border: oklch(.922 0 0);--sidebar-ring: oklch(.708 0 0)}.dark{--background: oklch(.145 0 0);--foreground: oklch(.985 0 0);--card: oklch(.145 0 0);--card-foreground: oklch(.985 0 0);--popover: oklch(.145 0 0);--popover-foreground: oklch(.985 0 0);--primary: oklch(.985 0 0);--primary-foreground: oklch(.205 0 0);--secondary: oklch(.269 0 0);--secondary-foreground: oklch(.985 0 0);--muted: oklch(.269 0 0);--muted-foreground: oklch(.708 0 0);--accent: oklch(.269 0 0);--accent-foreground: oklch(.985 0 0);--destructive: oklch(.396 .141 25.723);--destructive-foreground: oklch(.637 .237 25.331);--border: oklch(.269 0 0);--input: oklch(.269 0 0);--ring: oklch(.439 0 0);--font-weight-medium: 500;--font-weight-normal: 400;--chart-1: oklch(.488 .243 264.376);--chart-2: oklch(.696 .17 162.48);--chart-3: oklch(.769 .188 70.08);--chart-4: oklch(.627 .265 303.9);--chart-5: oklch(.645 .246 16.439);--sidebar: oklch(.205 0 0);--sidebar-foreground: oklch(.985 0 0);--sidebar-primary: oklch(.488 .243 264.376);--sidebar-primary-foreground: oklch(.985 0 0);--sidebar-accent: oklch(.269 0 0);--sidebar-accent-foreground: oklch(.985 0 0);--sidebar-border: oklch(.269 0 0);--sidebar-ring: oklch(.439 0 0)}@theme inline{ --color-background: var(--background); --color-foreground: var(--foreground); --color-card: var(--card); --color-card-foreground: var(--card-foreground); --color-popover: var(--popover); --color-popover-foreground: var(--popover-foreground); --color-primary: var(--primary); --color-primary-foreground: var(--primary-foreground); --color-secondary: var(--secondary); --color-secondary-foreground: var(--secondary-foreground); --color-muted: var(--muted); --color-muted-foreground: var(--muted-foreground); --color-accent: var(--accent); --color-accent-foreground: var(--accent-foreground); --color-destructive: var(--destructive); --color-destructive-foreground: var(--destructive-foreground); --color-border: var(--border); --color-input: var(--input); --color-input-background: var(--input-background); --color-switch-background: var(--switch-background); --color-ring: var(--ring); --color-chart-1: var(--chart-1); --color-chart-2: var(--chart-2); --color-chart-3: var(--chart-3); --color-chart-4: var(--chart-4); --color-chart-5: var(--chart-5); --radius-sm: calc(var(--radius) - 4px); --radius-md: calc(var(--radius) - 2px); --radius-lg: var(--radius); --radius-xl: calc(var(--radius) + 4px); --color-sidebar: var(--sidebar); --color-sidebar-foreground: var(--sidebar-foreground); --color-sidebar-primary: var(--sidebar-primary); --color-sidebar-primary-foreground: var(--sidebar-primary-foreground); --color-sidebar-accent: var(--sidebar-accent); --color-sidebar-accent-foreground: var(--sidebar-accent-foreground); --color-sidebar-border: var(--sidebar-border); --color-sidebar-ring: var(--sidebar-ring); }@layer base{*{@apply border-border outline-ring/50;}body{@apply bg-background text-foreground;}html{font-size:var(--font-size)}h1{font-size:var(--text-2xl);font-weight:var(--font-weight-medium);line-height:1.5}h2{font-size:var(--text-xl);font-weight:var(--font-weight-medium);line-height:1.5}h3{font-size:var(--text-lg);font-weight:var(--font-weight-medium);line-height:1.5}h4,label,button{font-size:var(--text-base);font-weight:var(--font-weight-medium);line-height:1.5}input{font-size:var(--text-base);font-weight:var(--font-weight-normal);line-height:1.5}} diff --git a/frontend/dist/index.html b/frontend/dist/index.html new file mode 100644 index 0000000000000000000000000000000000000000..19474fc08bc489ea945a40d8cfb012ecdb667999 --- /dev/null +++ b/frontend/dist/index.html @@ -0,0 +1,16 @@ + + + + + + + שיפור עיצוב עם אנימציה + + + + + +
+ + + \ No newline at end of file diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000000000000000000000000000000000000..349ccf9de9cee070e72de35fc96b34745bf6fc35 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,15 @@ + + + + + + + שיפור עיצוב עם אנימציה + + + +
+ + + + \ No newline at end of file diff --git a/frontend/package.json b/frontend/package.json new file mode 100644 index 0000000000000000000000000000000000000000..49ddba603f9ef02861b72948bb1a1e75976ad3d9 --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,81 @@ +{ + "name": "@figma/my-make-file", + "private": true, + "version": "0.0.1", + "type": "module", + "scripts": { + "build": "vite build", + "dev": "vite" + }, + "dependencies": { + "react": "18.3.1", + "react-dom": "18.3.1", + "@emotion/react": "11.14.0", + "@emotion/styled": "11.14.1", + "@mui/icons-material": "7.3.5", + "@mui/material": "7.3.5", + "@popperjs/core": "2.11.8", + "@radix-ui/react-accordion": "1.2.3", + "@radix-ui/react-alert-dialog": "1.1.6", + "@radix-ui/react-aspect-ratio": "1.1.2", + "@radix-ui/react-avatar": "1.1.3", + "@radix-ui/react-checkbox": "1.1.4", + "@radix-ui/react-collapsible": "1.1.3", + "@radix-ui/react-context-menu": "2.2.6", + "@radix-ui/react-dialog": "1.1.6", + "@radix-ui/react-dropdown-menu": "2.1.6", + "@radix-ui/react-hover-card": "1.1.6", + "@radix-ui/react-label": "2.1.2", + "@radix-ui/react-menubar": "1.1.6", + "@radix-ui/react-navigation-menu": "1.2.5", + "@radix-ui/react-popover": "1.1.6", + "@radix-ui/react-progress": "1.1.2", + "@radix-ui/react-radio-group": "1.2.3", + "@radix-ui/react-scroll-area": "1.2.3", + "@radix-ui/react-select": "2.1.6", + "@radix-ui/react-separator": "1.1.2", + "@radix-ui/react-slider": "1.2.3", + "@radix-ui/react-slot": "1.1.2", + "@radix-ui/react-switch": "1.1.3", + "@radix-ui/react-tabs": "1.1.3", + "@radix-ui/react-toggle-group": "1.1.2", + "@radix-ui/react-toggle": "1.1.2", + "@radix-ui/react-tooltip": "1.1.8", + "canvas-confetti": "1.9.4", + "class-variance-authority": "0.7.1", + "clsx": "2.1.1", + "cmdk": "1.1.1", + "date-fns": "3.6.0", + "embla-carousel-react": "8.6.0", + "input-otp": "1.4.2", + "lucide-react": "0.487.0", + "motion": "12.23.24", + "next-themes": "0.4.6", + "react-day-picker": "8.10.1", + "react-dnd": "16.0.1", + "react-dnd-html5-backend": "16.0.1", + "react-hook-form": "7.55.0", + "react-popper": "2.3.0", + "react-resizable-panels": "2.1.7", + "react-responsive-masonry": "2.7.1", + "react-router": "7.13.0", + "react-slick": "0.31.0", + "recharts": "2.15.2", + "sonner": "2.0.3", + "tailwind-merge": "3.2.0", + "tw-animate-css": "1.3.8", + "vaul": "1.1.2" + }, + "devDependencies": { + "@tailwindcss/vite": "4.1.12", + "@vitejs/plugin-react": "4.7.0", + "tailwindcss": "4.1.12", + "vite": "6.3.5" + }, + "pnpm": { + "overrides": { + "vite": "6.3.5" + }, + "onlyBuiltDependencies": ["@tailwindcss/oxide", "esbuild"] + } +} diff --git a/frontend/pnpm-lock.yaml b/frontend/pnpm-lock.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c6899ddb06842be346595abb23ee9b7d3b1f1db5 --- /dev/null +++ b/frontend/pnpm-lock.yaml @@ -0,0 +1,4284 @@ +lockfileVersion: '9.0' + +settings: + autoInstallPeers: true + excludeLinksFromLockfile: false + +overrides: + vite: 6.3.5 + +importers: + + .: + dependencies: + '@emotion/react': + specifier: 11.14.0 + version: 11.14.0(@types/react@19.2.14)(react@18.3.1) + '@emotion/styled': + specifier: 11.14.1 + version: 11.14.1(@emotion/react@11.14.0(@types/react@19.2.14)(react@18.3.1))(@types/react@19.2.14)(react@18.3.1) + '@mui/icons-material': + specifier: 7.3.5 + version: 7.3.5(@mui/material@7.3.5(@emotion/react@11.14.0(@types/react@19.2.14)(react@18.3.1))(@emotion/styled@11.14.1(@emotion/react@11.14.0(@types/react@19.2.14)(react@18.3.1))(@types/react@19.2.14)(react@18.3.1))(@types/react@19.2.14)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(@types/react@19.2.14)(react@18.3.1) + '@mui/material': + specifier: 7.3.5 + version: 7.3.5(@emotion/react@11.14.0(@types/react@19.2.14)(react@18.3.1))(@emotion/styled@11.14.1(@emotion/react@11.14.0(@types/react@19.2.14)(react@18.3.1))(@types/react@19.2.14)(react@18.3.1))(@types/react@19.2.14)(react-dom@18.3.1(react@18.3.1))(react@18.3.1) + '@popperjs/core': + 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3.0.1 + d3-scale: 4.0.2 + d3-shape: 3.2.0 + d3-time: 3.1.0 + d3-timer: 3.0.1 + + vite@6.3.5(jiti@2.6.1)(lightningcss@1.30.1): + dependencies: + esbuild: 0.25.12 + fdir: 6.5.0(picomatch@4.0.4) + picomatch: 4.0.4 + postcss: 8.5.8 + rollup: 4.60.0 + tinyglobby: 0.2.15 + optionalDependencies: + fsevents: 2.3.3 + jiti: 2.6.1 + lightningcss: 1.30.1 + + warning@4.0.3: + dependencies: + loose-envify: 1.4.0 + + yallist@3.1.1: {} + + yallist@5.0.0: {} + + yaml@1.10.3: {} diff --git a/frontend/postcss.config.mjs b/frontend/postcss.config.mjs new file mode 100644 index 0000000000000000000000000000000000000000..531dbecdad58a2ecaad9b45b5eefced747424c4f --- /dev/null +++ b/frontend/postcss.config.mjs @@ -0,0 +1,15 @@ +/** + * PostCSS Configuration + * + * Tailwind CSS v4 (via @tailwindcss/vite) automatically sets up all required + * PostCSS plugins — you do NOT need to include `tailwindcss` or `autoprefixer` here. + * + * This file only exists for adding additional PostCSS plugins, if needed. + * For example: + * + * import postcssNested from 'postcss-nested' + * export default { plugins: [postcssNested()] } + * + * Otherwise, you can leave this file empty. + */ +export default {} diff --git a/frontend/src/app/App.tsx b/frontend/src/app/App.tsx new file mode 100644 index 0000000000000000000000000000000000000000..4f1babda5015a4eb860fb57c7034ca49941f936d --- /dev/null +++ b/frontend/src/app/App.tsx @@ -0,0 +1,333 @@ +import { useState, useCallback, useEffect } from "react"; +// AlgoScope dashboard — v2 (custom charts, no Recharts) +import { motion, AnimatePresence } from "motion/react"; +import { Header } from "./components/Header"; +import { Sidebar } from "./components/Sidebar"; +import { OverviewTab } from "./components/OverviewTab"; +import { CoOccurrenceGraph } from "./components/CoOccurrenceGraph"; +import { TermComparisonTab } from "./components/TermComparisonTab"; +import { ExportTab } from "./components/ExportTab"; +import { SplashScreen } from "./components/SplashScreen"; +import { + apiFetchPosts, + apiFetchAndAnalyze, + apiFetchTotal, + generateMockPosts, + ALGOSPEAK_TERMS, + Post, +} from "./components/mockData"; + +// WHY empty initial state instead of mock data: +// We fetch real posts from the backend in the useEffect below. Starting with +// an empty array avoids a flash of mock data before real data arrives. +const EMPTY_POSTS: Post[] = []; + +const TABS = [ + { id: "overview", label: "Overview" }, + { id: "graph", label: "Co-occurrence Graph" }, + { id: "compare", label: "Term Comparison" }, + { id: "export", label: "Export" }, +]; + +export default function App() { + const [showSplash, setShowSplash] = useState(true); + const [sidebarOpen, setSidebarOpen] = useState(true); + const [activeTab, setActiveTab] = useState("overview"); + + // Sidebar state + const [selectedTerms, setSelectedTerms] = useState(ALGOSPEAK_TERMS.slice(0, 4)); + const [threshold, setThreshold] = useState(0.70); + const [sampling, setSampling] = useState(25); + const [autoRefresh, setAutoRefresh] = useState(false); + + // Data + const [allPosts, setAllPosts] = useState(EMPTY_POSTS); + const [batchPosts, setBatchPosts] = useState(EMPTY_POSTS); + const [fetching, setFetching] = useState(false); + const [justFetched, setJustFetched] = useState(false); + const [fetchError, setFetchError] = useState(null); + // WHY separate counter instead of allPosts.length: + // allPosts is capped at 500 and deduplicates by id, so it can go DOWN + // when new posts replace old ones. totalAnalyzed is a monotonically + // increasing sum — the true count of posts ever processed this session. + const [totalAnalyzed, setTotalAnalyzed] = useState(0); + + // Graph controls + const [minCooccurrence, setMinCooccurrence] = useState(3); + const [toxicOnly, setToxicOnly] = useState(false); + + // ── Load initial posts from backend on mount ──────────────────────────────── + // WHY useEffect + apiFetchPosts: + // On mount we call GET /posts to populate the dashboard with whatever the + // server already has (either seeded posts from cold start, or posts from + // previous sessions). This replaces the old `generateMockPosts()` call. + // + // WHY fallback to mock data on error: + // If the backend is unavailable (local dev without FastAPI running), we fall + // back to mock data so the UI is still usable. This is dev-only behaviour; + // in production the frontend and backend are served from the same process. + useEffect(() => { + let cancelled = false; + async function loadInitial() { + try { + const posts = await apiFetchPosts(200); + if (cancelled) return; + if (posts.length > 0) { + setAllPosts(posts); + setBatchPosts(posts.slice(0, 25)); + // WHY set totalAnalyzed from DB count on mount: + // The counter starts at 0 but the DB already has posts from previous + // sessions. Without this, the display jumps DOWN from 200 (DB load) + // to 25 (first fetch) because totalAnalyzed || totalPosts picks + // totalPosts on load, then switches to the smaller totalAnalyzed. + setTotalAnalyzed(posts.length); + } else { + // Backend is healthy but DB is empty — show mock data as a placeholder + const mock = generateMockPosts(ALGOSPEAK_TERMS.slice(0, 4), 30); + setAllPosts(mock); + setBatchPosts(mock.slice(0, 25)); + } + } catch { + if (cancelled) return; + // Backend unreachable — fall back to mock data for dev/offline use + const mock = generateMockPosts(ALGOSPEAK_TERMS.slice(0, 4), 30); + setAllPosts(mock); + setBatchPosts(mock.slice(0, 25)); + } + } + loadInitial(); + return () => { cancelled = true; }; + }, []); + + // ── Fetch & Analyze handler ───────────────────────────────────────────────── + // WHY apiFetchAndAnalyze instead of generateMockPosts: + // This calls POST /fetch-and-analyze which fetches real Bluesky posts, + // runs batch DistilBERT inference, saves them to SQLite, and returns results. + const handleFetch = useCallback(async () => { + setFetching(true); + setJustFetched(false); + setFetchError(null); + try { + const { posts: newBatch, message } = await apiFetchAndAnalyze( + selectedTerms.length ? selectedTerms : ALGOSPEAK_TERMS, + sampling, + threshold, + ); + + if (newBatch.length === 0) { + setFetchError(message ?? "No posts fetched from Bluesky. Check credentials and try again."); + setJustFetched(true); + return; + } + + setBatchPosts(newBatch); + setAllPosts(prev => { + const idSet = new Set(newBatch.map(p => String(p.id))); + const filtered = prev.filter(p => !idSet.has(String(p.id))); + return [...newBatch, ...filtered].slice(0, 500); + }); + // WHY fetch total from DB instead of computing locally: + // Any local computation (trulyNew, c => c + n) suffers from stale + // closures inside useCallback — allPosts captured at creation time is + // never the latest value, so the counter stops accumulating after the + // first fetch. The DB is the only source of truth for how many posts + // have ever been analyzed. One extra GET /posts?limit=1 call (tiny) + // gives us the exact real count with zero closure risk. + const dbTotal = await apiFetchTotal(); + if (dbTotal >= 0) setTotalAnalyzed(dbTotal); + setJustFetched(true); + } catch (err) { + const msg = err instanceof Error ? err.message : "Fetch failed"; + setFetchError(msg); + // Fallback: generate mock data so the UI doesn't go blank + const mock = generateMockPosts(selectedTerms.length ? selectedTerms : ALGOSPEAK_TERMS, sampling); + setBatchPosts(mock); + setAllPosts(prev => [...mock, ...prev].slice(0, 500)); + setJustFetched(true); + } finally { + setFetching(false); + } + }, [selectedTerms, sampling, threshold]); + + return ( + <> + {showSplash && setShowSplash(false)} />} +
+ {/* Header */} +
setSidebarOpen(v => !v)} sidebarOpen={sidebarOpen} /> + + {/* Body */} +
+ {/* Sidebar */} + + + {/* Main content */} +
+ {/* Tabs nav */} +
+ {TABS.map(tab => { + const active = tab.id === activeTab; + return ( + + ); + })} + + {/* Status toast — shows fetch success or error */} + + {justFetched && !fetchError && ( + + ✓ Done! Analyzed {batchPosts.length} posts + + )} + {justFetched && fetchError && ( + + ⚠ API unavailable — showing mock data + + )} + +
+ + {/* Tab content */} +
+ + + {activeTab === "overview" && ( + + )} + {activeTab === "graph" && ( + + )} + {activeTab === "compare" && ( + + )} + {activeTab === "export" && ( + + )} + + +
+
+
+ + +
+ + ); +} diff --git a/frontend/src/app/components/CoOccurrenceGraph.tsx b/frontend/src/app/components/CoOccurrenceGraph.tsx new file mode 100644 index 0000000000000000000000000000000000000000..a59dd12129fc7ed7f9792a511bcadb7e815facac --- /dev/null +++ b/frontend/src/app/components/CoOccurrenceGraph.tsx @@ -0,0 +1,609 @@ +import { useEffect, useRef, useState, useCallback, useMemo, Component } from "react"; +import { motion } from "motion/react"; +import { nodeColor, apiGetGraphData, GraphNode, GraphEdge } from "./mockData"; + +// ── Types ────────────────────────────────────────────────────────────────────── +interface SimNode { + id: string; + x: number; + y: number; + vx: number; + vy: number; + frequency: number; + toxicRatio: number; +} + +interface Props { + minCooccurrence: number; + setMinCooccurrence: (v: number) => void; + toxicOnly: boolean; + setToxicOnly: (v: boolean) => void; +} + +// ── Constants ────────────────────────────────────────────────────────────────── +const W = 820; +const H = 540; +// WHY increased REPULSION + reduced SPRING for real data: +// Mock data had ~6 nodes with low edge weights. Real data has 20-30 nodes +// where a hub like "unalive" has 17 edges — the combined spring pull +// overwhelmed repulsion and collapsed the graph into one blob. +// Higher REPULSION (6000) pushes nodes apart more aggressively, +// lower SPRING (0.018) reduces the per-edge pull so a hub with 17 edges +// doesn't dominate. EDGE_LEN increased so nodes have more breathing room. +const REPULSION = 6000; +const SPRING = 0.018; +const EDGE_LEN = 180; +const GRAVITY = 0.008; +const DAMPING = 0.82; +const CENTER_X = W / 2; +const CENTER_Y = H / 2; + +// ── Error Boundary ───────────────────────────────────────────────────────────── +interface EBState { hasError: boolean; error?: string } +class GraphErrorBoundary extends Component<{ children: React.ReactNode }, EBState> { + constructor(props: { children: React.ReactNode }) { + super(props); + this.state = { hasError: false }; + } + static getDerivedStateFromError(err: Error): EBState { + return { hasError: true, error: err.message }; + } + render() { + if (this.state.hasError) { + return ( +
+
+ ⚠ Graph failed to render +
+
+ {this.state.error || "Unknown error"} +
+ +
+ ); + } + return this.props.children; + } +} + +// ── Physics hook ─────────────────────────────────────────────────────────────── +function safeNum(v: number, fallback = 0): number { + return isFinite(v) && !isNaN(v) ? v : fallback; +} + +function useForceSimulation( + nodeConfigs: GraphNode[], + edges: GraphEdge[], + nodeKey: string, +) { + const nodesRef = useRef([]); + const [positions, setPositions] = useState([]); + const rafRef = useRef(0); + const edgesRef = useRef(edges); + edgesRef.current = edges; + const activeRef = useRef(false); + + const run = useCallback(() => { + if (!activeRef.current) return; + const ns = nodesRef.current; + if (!ns.length) return; + const es = edgesRef.current; + + try { + // Repulsion + for (let i = 0; i < ns.length; i++) { + for (let j = i + 1; j < ns.length; j++) { + const dx = (ns[i].x - ns[j].x) || 0.5; + const dy = (ns[i].y - ns[j].y) || 0.5; + const dist2 = Math.max(0.01, dx * dx + dy * dy); + const dist = Math.sqrt(dist2); + const force = REPULSION / dist2; + const fx = safeNum((dx / dist) * force); + const fy = safeNum((dy / dist) * force); + ns[i].vx += fx; + ns[i].vy += fy; + ns[j].vx -= fx; + ns[j].vy -= fy; + } + } + + // Spring edges + for (const e of es) { + const s = ns.find(n => n.id === e.source); + const t = ns.find(n => n.id === e.target); + if (!s || !t) continue; + const dx = t.x - s.x; + const dy = t.y - s.y; + const dist = Math.sqrt(dx * dx + dy * dy) || 1; + // WHY Math.min cap: with real data edge weights can be 50–500+ + // (co-occurrence count across hundreds of posts). Without capping, + // naturalLen collapses to ~3px, pulling all nodes into a single blob. + // Capping at 8 keeps naturalLen in the range 115–140px regardless of + // how large the real-data weights get. + const naturalLen = EDGE_LEN / (1 + Math.min(e.weight, 8) * 0.04); + const force = (dist - naturalLen) * SPRING; + const fx = safeNum((dx / dist) * force); + const fy = safeNum((dy / dist) * force); + s.vx += fx; + s.vy += fy; + t.vx -= fx; + t.vy -= fy; + } + + // Gravity + integrate + for (const n of ns) { + n.vx = safeNum(n.vx + (CENTER_X - n.x) * GRAVITY); + n.vy = safeNum(n.vy + (CENTER_Y - n.y) * GRAVITY); + n.vx *= DAMPING; + n.vy *= DAMPING; + n.x = safeNum(n.x + n.vx, CENTER_X); + n.y = safeNum(n.y + n.vy, CENTER_Y); + // WHY log scale: linear sizing (freq * 0.25) lets high-frequency common + // words (e.g. "yeah", "his") grow to 10x the size of algospeak terms, + // dominating the canvas. Math.log compresses the range so all nodes + // stay visually comparable. Min 8px, max ~32px regardless of frequency. + const r = 5 + Math.min(Math.log1p(n.frequency ?? 1) * 1.8, 12); + n.x = Math.max(r + 40, Math.min(W - r - 40, n.x)); + n.y = Math.max(r + 20, Math.min(H - r - 20, n.y)); + } + + if (activeRef.current) { + setPositions(ns.map(n => ({ ...n }))); + const maxV = ns.reduce((mx, n) => Math.max(mx, Math.abs(n.vx) + Math.abs(n.vy)), 0); + if (maxV > 0.15) { + rafRef.current = requestAnimationFrame(run); + } + } + } catch { + activeRef.current = false; + } + }, []); + + useEffect(() => { + cancelAnimationFrame(rafRef.current); + activeRef.current = true; + + nodesRef.current = nodeConfigs.map((n, i) => { + // WHY circle spread: random init clusters nodes near center, requiring + // hundreds of ticks to separate. Evenly spreading in a circle means + // repulsion forces are balanced from tick 1 — graph settles readable. + const angle = (i / Math.max(nodeConfigs.length, 1)) * 2 * Math.PI; + const spread = Math.min(W, H) * 0.32; + return { + id: n.id, + frequency: n.frequency, + toxicRatio: n.toxicRatio, + x: CENTER_X + Math.cos(angle) * spread, + y: CENTER_Y + Math.sin(angle) * spread, + vx: (Math.random() - 0.5) * 1.5, + vy: (Math.random() - 0.5) * 1.5, + }; + }); + setPositions(nodesRef.current.map(n => ({ ...n }))); + rafRef.current = requestAnimationFrame(run); + + return () => { + activeRef.current = false; + cancelAnimationFrame(rafRef.current); + }; + }, [nodeKey, run]); + + return positions; +} + +// ── Edge colour ─────────────────────────────────────────────────────────────── +function edgeColor(sourceRatio: number, targetRatio: number, weight: number): string { + const avg = ((sourceRatio ?? 0) + (targetRatio ?? 0)) / 2; + const alpha = Math.min(0.9, 0.3 + weight * 0.05); + if (avg >= 0.7) return `rgba(255,75,75,${alpha})`; + if (avg >= 0.4) return `rgba(255,140,66,${alpha})`; + return `rgba(46,204,113,${alpha})`; +} + +// ── Inner graph component ───────────────────────────────────────────────────── +function GraphCanvas({ + minCooccurrence, toxicOnly, setMinCooccurrence, setToxicOnly, +}: { + minCooccurrence: number; + toxicOnly: boolean; + setMinCooccurrence: (v: number) => void; + setToxicOnly: (v: boolean) => void; +}) { + const [built, setBuilt] = useState(false); + const [hoveredNode, setHoveredNode] = useState(null); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + + // WHY state for nodes/edges instead of hardcoded constants: + // Previously these were GRAPH_NODES / GRAPH_EDGES imported from mockData. + // Now they come from GET /graph-data when the user clicks "Build Graph". + // The physics simulation code is exactly unchanged — it just receives real data. + const [nodes, setNodes] = useState([]); + const [edges, setEdges] = useState([]); + + // ── Fetch graph data from backend ─────────────────────────────────────────── + const handleBuild = useCallback(async () => { + setLoading(true); + setError(null); + try { + const data = await apiGetGraphData(minCooccurrence, toxicOnly); + setNodes(data.nodes); + setEdges(data.edges); + setBuilt(true); + } catch (err) { + setError(err instanceof Error ? err.message : "Failed to load graph data"); + } finally { + setLoading(false); + } + }, [minCooccurrence, toxicOnly]); + + const visibleNodes = useMemo(() => { + return nodes.filter(n => { + if (toxicOnly && n.toxicRatio < 0.7) return false; + const edgeCount = edges.filter( + e => (e.source === n.id || e.target === n.id) && e.weight >= minCooccurrence + ).length; + return edgeCount > 0 || minCooccurrence <= 2; + }); + }, [nodes, edges, toxicOnly, minCooccurrence]); + + const visibleEdges = useMemo(() => { + const nodeIds = new Set(visibleNodes.map(n => n.id)); + return edges.filter( + e => e.weight >= minCooccurrence && nodeIds.has(e.source) && nodeIds.has(e.target) + ); + }, [visibleNodes, edges, minCooccurrence]); + + const nodeKey = useMemo( + () => visibleNodes.map(n => n.id).sort().join(","), + [visibleNodes] + ); + + const positions = useForceSimulation(visibleNodes, visibleEdges, nodeKey); + + const posMap = useMemo(() => { + const m: Record = {}; + for (const p of positions) m[p.id] = { x: p.x, y: p.y }; + return m; + }, [positions]); + + const nodeMap = useMemo(() => { + const m: Record = {}; + for (const n of visibleNodes) m[n.id] = n; + return m; + }, [visibleNodes]); + + return ( +
+ {/* Top controls */} +
+ {/* Info card */} + +
+ How to read this graph +
+
+ Words that frequently appear together in algospeak posts are connected. Node size = frequency  |  + red >70% toxic + {" "}orange 40-70% mixed + {" "}green <40% benign +
+ + + {/* Controls */} + +
+
+ Min co-occurrences +
+
+ {minCooccurrence} +
+ { + setMinCooccurrence(parseInt(e.target.value)); + // Reset graph so user re-clicks Build Graph with new params + setBuilt(false); + setNodes([]); + setEdges([]); + }} + style={{ width: "100%", accentColor: "#ff4b4b" }} + /> +
+ + + {loading ? "Loading…" : built ? "✓ Graph Active" : "Build Graph"} + + +
+ + {/* Error state */} + {error && ( +
+ ⚠ {error} +
+ )} + + {/* Graph canvas */} + {!built ? ( + + Adjust settings above and click "Build Graph" to visualize word co-occurrences. + + ) : ( + + + {/* Background grid */} + + + + + + + + {/* Edges */} + {visibleEdges.map(e => { + const s = posMap[e.source]; + const t = posMap[e.target]; + if (!s || !t) return null; + const sNode = nodeMap[e.source]; + const tNode = nodeMap[e.target]; + if (!sNode || !tNode) return null; + const isHighlighted = hoveredNode === e.source || hoveredNode === e.target; + return ( + + ); + })} + + {/* Nodes */} + {positions.map(node => { + const freq = node.frequency ?? 1; + const tRatio = node.toxicRatio ?? 0.5; + // WHY log1p: same formula as the physics loop so the rendered + // circle matches the collision radius used for simulation. + const size = 5 + Math.min(Math.log1p(freq) * 1.8, 12); + const color = nodeColor(tRatio); + const isHovered = hoveredNode === node.id; + const isDimmed = !!(hoveredNode && !isHovered); + const r = isHovered ? size * 1.25 : size; + + if (!isFinite(node.x) || !isFinite(node.y)) return null; + + return ( + setHoveredNode(node.id)} + onMouseLeave={() => setHoveredNode(null)} + style={{ cursor: "pointer" }} + > + + + + {node.id} + + + ); + })} + + + {/* Hover tooltip */} + {hoveredNode && (() => { + const n = visibleNodes.find(x => x.id === hoveredNode); + if (!n) return null; + const color = nodeColor(n.toxicRatio); + const connections = visibleEdges.filter( + e => e.source === n.id || e.target === n.id + ).length; + return ( + +
+ {n.id} +
+
+ Frequency: {n.frequency} +
+
+ Toxic ratio:{" "} + + {(n.toxicRatio * 100).toFixed(0)}% + +
+
+ Connections: {connections} +
+
+
+
+ + ); + })()} + + )} +
+ ); +} + +// ── Public component ─────────────────────────────────────────────────────────── +export function CoOccurrenceGraph({ minCooccurrence, setMinCooccurrence, toxicOnly, setToxicOnly }: Props) { + return ( + + + + ); +} diff --git a/frontend/src/app/components/ExportTab.tsx b/frontend/src/app/components/ExportTab.tsx new file mode 100644 index 0000000000000000000000000000000000000000..9990a95373b093eb9f1901b7511be78a2eca800b --- /dev/null +++ b/frontend/src/app/components/ExportTab.tsx @@ -0,0 +1,242 @@ +import { useState } from "react"; +import { motion } from "motion/react"; +import { Download, FileText, FileJson, CheckCircle } from "lucide-react"; +import { Post } from "./mockData"; + +interface Props { + posts: Post[]; +} + +function downloadJSON(data: unknown, filename: string) { + const blob = new Blob([JSON.stringify(data, null, 2)], { type: "application/json" }); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = filename; + a.click(); + URL.revokeObjectURL(url); +} + +function downloadCSV(posts: Post[], filename: string) { + const header = "id,text,score,label,query_term,created_at"; + const rows = posts.map(p => + [p.id, `"${p.text.replace(/"/g, '""')}"`, p.score, p.label, p.query_term, p.created_at].join(",") + ); + const blob = new Blob([[header, ...rows].join("\n")], { type: "text/csv" }); + const url = URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + a.download = filename; + a.click(); + URL.revokeObjectURL(url); +} + +function ExportCard({ + icon, + title, + description, + buttonLabel, + color, + onClick, + delay, +}: { + icon: React.ReactNode; + title: string; + description: string; + buttonLabel: string; + color: string; + onClick: () => void; + delay: number; +}) { + const [clicked, setClicked] = useState(false); + + const handleClick = () => { + onClick(); + setClicked(true); + setTimeout(() => setClicked(false), 2000); + }; + + return ( + +
+ {icon} +
+
+
{title}
+
{description}
+
+ + {clicked ? : } + {clicked ? "Downloaded!" : buttonLabel} + + + ); +} + +export function ExportTab({ posts }: Props) { + const toxicPosts = posts.filter(p => p.label === "toxic"); + const nonToxicPosts = posts.filter(p => p.label === "non-toxic"); + + const summaryData = { + exported_at: new Date().toISOString(), + total_posts: posts.length, + toxic_count: toxicPosts.length, + non_toxic_count: nonToxicPosts.length, + toxic_rate: posts.length ? ((toxicPosts.length / posts.length) * 100).toFixed(2) + "%" : "0%", + avg_score: posts.length + ? (posts.reduce((s, p) => s + p.score, 0) / posts.length).toFixed(4) + : "0", + posts, + }; + + return ( +
+ + Export your analyzed data for further research or archiving. + + + {/* Stats summary */} + + {[ + { label: "Total posts", value: String(posts.length), color: "#fff" }, + { label: "Toxic", value: String(toxicPosts.length), color: "#ff4b4b" }, + { label: "Non-toxic", value: String(nonToxicPosts.length), color: "#2ecc71" }, + { + label: "Toxic rate", + value: posts.length ? `${((toxicPosts.length / posts.length) * 100).toFixed(1)}%` : "0%", + color: "#ff8c42", + }, + ].map(({ label, value, color }) => ( +
+
+ {label} +
+
{value}
+
+ ))} + + + {/* Export options */} + } + title="Full dataset (JSON)" + description={`Export all ${posts.length} analyzed posts with scores, labels, and metadata`} + buttonLabel="Export JSON" + color="#a6b0ff" + onClick={() => downloadJSON(summaryData, "algoscope-export.json")} + delay={0.1} + /> + + } + title="All posts (CSV)" + description={`${posts.length} rows · id, text, score, label, query_term, created_at`} + buttonLabel="Export CSV" + color="#ff8c42" + onClick={() => downloadCSV(posts, "algoscope-posts.csv")} + delay={0.18} + /> + + } + title="Toxic posts only (CSV)" + description={`${toxicPosts.length} rows · filtered to toxic label (score ≥ threshold)`} + buttonLabel="Export CSV" + color="#ff4b4b" + onClick={() => downloadCSV(toxicPosts, "algoscope-toxic.csv")} + delay={0.26} + /> + + } + title="Summary statistics (JSON)" + description="Aggregate metrics: counts, rates, avg scores per term" + buttonLabel="Export JSON" + color="#2ecc71" + onClick={() => { + const termStats: Record = {}; + const terms = [...new Set(posts.map(p => p.query_term))]; + for (const t of terms) { + const tp = posts.filter(p => p.query_term === t); + const toxicN = tp.filter(p => p.label === "toxic").length; + termStats[t] = { + count: tp.length, + toxic_count: toxicN, + toxic_rate: `${((toxicN / tp.length) * 100).toFixed(1)}%`, + avg_score: (tp.reduce((s, p) => s + p.score, 0) / tp.length).toFixed(4), + }; + } + downloadJSON({ + exported_at: new Date().toISOString(), + total: posts.length, + toxic_rate: `${((toxicPosts.length / posts.length) * 100).toFixed(1)}%`, + per_term: termStats, + }, "algoscope-summary.json"); + }} + delay={0.34} + /> +
+ ); +} diff --git a/frontend/src/app/components/Header.tsx b/frontend/src/app/components/Header.tsx new file mode 100644 index 0000000000000000000000000000000000000000..18778eb796a033827387daf0fc9e4eacf72c1caf --- /dev/null +++ b/frontend/src/app/components/Header.tsx @@ -0,0 +1,140 @@ +import { motion } from "motion/react"; + +interface HeaderProps { + onToggleSidebar: () => void; + sidebarOpen: boolean; +} + +export function Header({ onToggleSidebar, sidebarOpen }: HeaderProps) { + return ( + +
+ {/* Sidebar toggle */} + + + {/* Logo */} + + A + + + {/* Title */} +
+
+ AlgoScope +
+
+ Real-time algospeak & toxicity intelligence on Bluesky +
+
+
+ + {/* Right side */} +
+
+ + + LIVE + +
+
+ by Odeliya Charitonova +
+ + github.com/odeliyach/Algoscope + +
+
+ + + + ); +} \ No newline at end of file diff --git a/frontend/src/app/components/OverviewTab.tsx b/frontend/src/app/components/OverviewTab.tsx new file mode 100644 index 0000000000000000000000000000000000000000..0f580a2f888861a8218c452ab8cacfa2c8577cca --- /dev/null +++ b/frontend/src/app/components/OverviewTab.tsx @@ -0,0 +1,687 @@ +import { useMemo, useEffect, useRef, useState } from "react"; +import { motion, AnimatePresence } from "motion/react"; +import { Post } from "./mockData"; + +interface OverviewTabProps { + posts: Post[]; + batchPosts: Post[]; + selectedTerms: string[]; + justFetched: boolean; + totalAnalyzed: number; +} + +// ── Animated counter hook ────────────────────────────────────────────────────── +function useAnimatedNumber(target: number, duration = 900): number { + const [current, setCurrent] = useState(0); + const startRef = useRef(null); + const fromRef = useRef(0); + const rafRef = useRef(0); + + useEffect(() => { + const from = fromRef.current; + startRef.current = null; + const tick = (ts: number) => { + if (startRef.current === null) startRef.current = ts; + const progress = Math.min((ts - startRef.current) / duration, 1); + const ease = 1 - Math.pow(1 - progress, 3); + setCurrent(Math.round(from + (target - from) * ease)); + if (progress < 1) rafRef.current = requestAnimationFrame(tick); + else fromRef.current = target; + }; + rafRef.current = requestAnimationFrame(tick); + return () => cancelAnimationFrame(rafRef.current); + }, [target, duration]); + + return current; +} + +function useAnimatedFloat(target: number, duration = 900, decimals = 1): string { + const [current, setCurrent] = useState(0); + const startRef = useRef(null); + const fromRef = useRef(0); + const rafRef = useRef(0); + + useEffect(() => { + const from = fromRef.current; + startRef.current = null; + const tick = (ts: number) => { + if (startRef.current === null) startRef.current = ts; + const progress = Math.min((ts - startRef.current) / duration, 1); + const ease = 1 - Math.pow(1 - progress, 3); + setCurrent(from + (target - from) * ease); + if (progress < 1) rafRef.current = requestAnimationFrame(tick); + else fromRef.current = target; + }; + rafRef.current = requestAnimationFrame(tick); + return () => cancelAnimationFrame(rafRef.current); + }, [target, duration]); + + return current.toFixed(decimals); +} + +// ── Metric card ──────────────────────────────────────────────────────────────── +function MetricCard({ + label, displayValue, sub, subIcon, subColor, subBg, valueColor, delay, isAlert, +}: { + label: string; + displayValue?: string; + sub: string; + subIcon?: string; + subColor?: string; + subBg?: string; + valueColor: string; + delay: number; + isAlert?: boolean; +}) { + return ( + + {isAlert && ( + + )} +
+ {label} +
+
+ {displayValue} +
+ {/* Styled sub badge */} + + {subIcon && ( + {subIcon} + )} + + {sub} + + + + ); +} + +// ── Spike alert banner ───────────────────────────────────────────────────────── +function SpikeAlert({ batchToxicRate, batchCount }: { batchToxicRate: number; batchCount: number }) { + const [dismissed, setDismissed] = useState(false); + useEffect(() => { setDismissed(false); }, [Math.floor(batchToxicRate)]); + if (batchToxicRate < 38 || batchCount === 0 || dismissed) return null; + + const severity = batchToxicRate >= 70 ? "critical" : batchToxicRate >= 55 ? "high" : "elevated"; + const severityColor = severity === "critical" ? "#ff4b4b" : severity === "high" ? "#ff6b3d" : "#ff9f43"; + const severityBg = severity === "critical" ? "rgba(255,75,75,0.08)" : severity === "high" ? "rgba(255,107,61,0.08)" : "rgba(255,159,67,0.08)"; + const severityBorder = severity === "critical" ? "rgba(255,75,75,0.35)" : severity === "high" ? "rgba(255,107,61,0.3)" : "rgba(255,159,67,0.28)"; + + return ( + + + {/* Animated scan line */} + + {/* Pulsing dot */} + +
+ + ⚠ Toxicity Spike Detected + + + {batchToxicRate.toFixed(1)}% of last {batchCount} posts flagged as toxic + +
+
+ +
+ + {severity.toUpperCase()} + +
+
+ + + + ); +} + +// ── Custom SVG area chart (no Recharts) ──────────────────────────────────────── +function CustomAreaChart({ data }: { data: { hour: string; value: number | null }[] }) { + const containerRef = useRef(null); + const [containerWidth, setContainerWidth] = useState(400); + + useEffect(() => { + if (!containerRef.current) return; + const obs = new ResizeObserver(entries => { + for (const e of entries) setContainerWidth(e.contentRect.width); + }); + obs.observe(containerRef.current); + setContainerWidth(containerRef.current.clientWidth); + return () => obs.disconnect(); + }, []); + + const W = containerWidth; + const H = 170; + const PAD = { top: 12, right: 12, bottom: 28, left: 36 }; + const inner = { w: W - PAD.left - PAD.right, h: H - PAD.top - PAD.bottom }; + + // Fill null with 0 so the line is always continuous + const filled = data.map(d => ({ ...d, value: d.value ?? 0 })); + + const vals = filled.map(d => d.value) as number[]; + const minV = 0; + const maxV = Math.max(...vals, 0.01); + const rangeV = maxV - minV || 0.001; + + const [tooltip, setTooltip] = useState<{ x: number; y: number; hour: string; val: number } | null>(null); + + const toX = (i: number) => PAD.left + (i / (filled.length - 1)) * inner.w; + const toY = (v: number) => PAD.top + inner.h - ((v - minV) / rangeV) * inner.h; + + const linePath = filled.map((d, i) => { + const x = toX(i); const y = toY(d.value); + return `${i === 0 ? "M" : "L"} ${x},${y}`; + }).join(" "); + + const areaPath = `${linePath} L ${toX(filled.length - 1)},${PAD.top + inner.h} L ${toX(0)},${PAD.top + inner.h} Z`; + + const yTicks = [0, 0.25, 0.5, 0.75, 1.0]; + const xLabels = filled.filter((_, i) => i % 4 === 0); + + return ( +
+ setTooltip(null)} + > + + + + + + + + + + + + {/* Grid lines */} + {yTicks.map(t => { + const y = PAD.top + inner.h - (t / 1) * inner.h; + return ( + + + {t.toFixed(1)} + + ); + })} + + {/* X labels */} + {xLabels.map(d => { + const i = filled.indexOf(d); + return ( + {d.hour} + ); + })} + + {/* Area fill */} + + + {/* Line */} + + + {/* Interactive dots — only at hours with real data */} + {data.map((d, i) => { + if (d.value === null || d.value === 0) return null; + const x = toX(i); const y = toY(d.value); + const c = d.value >= 0.7 ? "#ff4b4b" : d.value >= 0.4 ? "#ff8c42" : "#2ecc71"; + return ( + setTooltip({ x, y, hour: d.hour, val: d.value! })} + /> + ); + })} + + {/* Tooltip */} + {tooltip && ( + + + + {tooltip.hour} + {tooltip.val.toFixed(3)} + + )} + +
+ ); +} + +// ── Cool Score Distribution Chart ───────────────────────────────────────────── +function CustomScoreBars({ data }: { data: { label: string; count: number; color: string }[] }) { + const maxCount = Math.max(...data.map(d => d.count), 1); + const [hovered, setHovered] = useState(null); + const containerRef = useRef(null); + + const GRADIENTS: [string, string][] = [ + ["#4ade80", "#22c55e"], + ["#a3e635", "#84cc16"], + ["#facc15", "#eab308"], + ["#fb923c", "#f97316"], + ["#f87171", "#ef4444"], + ]; + + return ( +
+ {/* Y grid lines */} +
+ {/* Grid lines overlay */} + {[0.25, 0.5, 0.75, 1].map((t, gi) => ( +
+ ))} + + {data.map((d, i) => { + const heightPct = (d.count / maxCount) * 100; + const isHov = hovered === i; + const [colorTop, colorBot] = GRADIENTS[i]; + return ( +
setHovered(i)} + onMouseLeave={() => setHovered(null)} + > + {/* Tooltip */} + {isHov && ( + + {d.count} posts + + )} + + {/* Bar */} + 0 ? 4 : 0, + opacity: hovered === null || isHov ? 1 : 0.4, + transition: "opacity 0.2s", + boxShadow: isHov ? `0 0 16px ${colorTop}80, 0 0 6px ${colorTop}40` : "none", + position: "relative", + }} + > + {/* Shine on top */} +
+ +
+ ); + })} +
+ + {/* Baseline */} +
+ + {/* X labels */} +
+ {data.map((d, i) => ( +
+ {d.label} +
+ ))} +
+
+ ); +} + +const DAYS = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]; + +export function OverviewTab({ posts, batchPosts, selectedTerms, justFetched, totalAnalyzed }: OverviewTabProps) { + const totalPosts = posts.length; + const toxicCount = posts.filter(p => p.label === "toxic").length; + const toxicRate = totalPosts ? (toxicCount / totalPosts) * 100 : 0; + + // Batch-specific spike detection + const batchToxicCount = batchPosts.filter(p => p.label === "toxic").length; + const batchToxicRate = batchPosts.length ? (batchToxicCount / batchPosts.length) * 100 : 0; + + const avgScoreRaw = batchPosts.length + ? batchPosts.reduce((s, p) => s + p.score, 0) / batchPosts.length + : 0; + + const termCounts: Record = {}; + for (const p of batchPosts) { + termCounts[p.query_term] = (termCounts[p.query_term] || 0) + 1; + } + const topTerm = Object.keys(termCounts).sort((a, b) => termCounts[b] - termCounts[a])[0] || "—"; + + // WHY totalAnalyzed directly (no || totalPosts fallback): + // The old fallback `totalAnalyzed || totalPosts` caused the counter to show + // totalPosts (200) on load, then jump DOWN to totalAnalyzed (25) after the + // first fetch. Now App.tsx seeds totalAnalyzed from the DB count on mount, + // so the fallback is no longer needed and only caused confusion. + const animatedTotal = useAnimatedNumber(totalAnalyzed); + const animatedToxicRate = useAnimatedFloat(toxicRate, 900, 1); + const animatedAvg = useAnimatedFloat(avgScoreRaw, 900, 3); + + const timeData = useMemo(() => { + const buckets: Record = {}; + for (const p of posts) { + const h = new Date(p.created_at).getUTCHours(); + if (!buckets[h]) buckets[h] = { sum: 0, count: 0 }; + buckets[h].sum += p.score; + buckets[h].count += 1; + } + return Array.from({ length: 24 }, (_, h) => ({ + hour: `${String(h).padStart(2, "0")}:00`, + value: buckets[h] ? +(buckets[h].sum / buckets[h].count).toFixed(3) : null, + })); + }, [posts]); + + const scoreDistData = useMemo(() => { + const bins = [0, 0, 0, 0, 0]; + for (const p of batchPosts) { + const idx = Math.min(4, Math.floor(p.score / 0.2)); + bins[idx]++; + } + const COLORS = ["#2ecc71", "#a3e635", "#ffeb3b", "#ff8c42", "#ff4b4b"]; + const LABELS = ["0-0.2", "0.2-0.4", "0.4-0.6", "0.6-0.8", "0.8-1.0"]; + return LABELS.map((label, i) => ({ label, count: bins[i], color: COLORS[i] })); + }, [batchPosts]); + + const heatmapData = useMemo(() => { + const grid: Record = {}; + for (const p of posts) { + const d = new Date(p.created_at); + const day = (d.getDay() + 6) % 7; + const hour = d.getHours(); + const key = `${day}-${hour}`; + if (!grid[key]) grid[key] = { sum: 0, count: 0 }; + grid[key].sum += p.score; + grid[key].count += 1; + } + return grid; + }, [posts]); + + const maxHeat = useMemo(() => { + let mx = 0; + for (const v of Object.values(heatmapData)) { + mx = Math.max(mx, v.count ? v.sum / v.count : 0); + } + return mx || 1; + }, [heatmapData]); + + const recentPosts = batchPosts.slice(0, 20); + + return ( +
+ + + + {/* Metric cards */} +
+ + = 38 ? "↑ High — monitor closely" : "✓ Within expected range"} + subIcon={toxicRate >= 38 ? "🔴" : undefined} + subColor={toxicRate >= 38 ? "#ff4b4b" : "#2ecc71"} + subBg={toxicRate >= 38 ? "rgba(255,75,75,0.1)" : "rgba(46,204,113,0.08)"} + valueColor="#ff4b4b" + delay={0.07} + isAlert={toxicRate >= 38} + /> + + +
+ + {/* Charts row */} +
+ +
Toxicity over time
+ + + + +
Score distribution
+ + +
+ + {/* Activity heatmap */} + +
Activity heatmap
+
+
+
+ {Array.from({ length: 24 }, (_, h) => ( +
+ {h % 6 === 0 ? h : ""} +
+ ))} +
+ {DAYS.map((day, d) => ( +
+
{day}
+ {Array.from({ length: 24 }, (_, h) => { + const cell = heatmapData[`${d}-${h}`]; + const intensity = cell ? (cell.sum / cell.count) / maxHeat : 0; + const hasData = cell && cell.count > 0; + const r = hasData ? Math.round(30 + (255 - 30) * intensity) : 26; + const g = hasData ? Math.round(37 + (75 - 37) * intensity) : 29; + const b = hasData ? Math.round(64 + (75 - 64) * intensity) : 46; + return ( + + ); + })} +
+ ))} +
+ Low +
+ High toxicity +
+
+ + + {/* Recent posts */} + +
Recent posts (last batch)
+ {recentPosts.length === 0 ? ( +
Click "Fetch & Analyze" to see recent posts.
+ ) : ( +
+ {recentPosts.map((p, i) => { + const sc = p.score >= 0.7 ? "#ff4b4b" : p.score >= 0.4 ? "#ff9f43" : "#2ecc71"; + const scBg = p.score >= 0.7 ? "rgba(255,75,75,0.12)" : p.score >= 0.4 ? "rgba(255,159,67,0.12)" : "rgba(46,204,113,0.12)"; + return ( + +
+ {p.score.toFixed(3)} +
+
{p.text.slice(0, 110)}
+
+ {p.query_term} +
+ + ); + })} +
+ )} + +
+ ); +} diff --git a/frontend/src/app/components/Sidebar.tsx b/frontend/src/app/components/Sidebar.tsx new file mode 100644 index 0000000000000000000000000000000000000000..c7c2c4a7002b0a262a7c557e47c909495cae6d27 --- /dev/null +++ b/frontend/src/app/components/Sidebar.tsx @@ -0,0 +1,335 @@ +import { useState } from "react"; +import { motion, AnimatePresence } from "motion/react"; +import { X } from "lucide-react"; +import { ALGOSPEAK_TERMS, Post, nodeColor } from "./mockData"; + +interface SidebarProps { + open: boolean; + selectedTerms: string[]; + setSelectedTerms: (terms: string[]) => void; + threshold: number; + setThreshold: (v: number) => void; + sampling: number; + setSampling: (v: number) => void; + autoRefresh: boolean; + setAutoRefresh: (v: boolean) => void; + onFetch: () => void; + posts: Post[]; + fetching: boolean; +} + +function SectionLabel({ children }: { children: React.ReactNode }) { + return ( +
+ {children} +
+ ); +} + +export function Sidebar({ + open, + selectedTerms, + setSelectedTerms, + threshold, + setThreshold, + sampling, + setSampling, + autoRefresh, + setAutoRefresh, + onFetch, + posts, + fetching, +}: SidebarProps) { + const [customInput, setCustomInput] = useState(""); + const [customTerms, setCustomTerms] = useState([]); + + const addCustom = () => { + const t = customInput.trim().toLowerCase(); + if (!t) return; + if (!customTerms.includes(t)) setCustomTerms(prev => [...prev, t]); + if (!selectedTerms.includes(t)) setSelectedTerms([...selectedTerms, t]); + setCustomInput(""); + }; + + // Compute toxic ratio per selected term from posts + const termRatio: Record = {}; + for (const term of selectedTerms) { + const matching = posts.filter(p => p.query_term === term || p.text.toLowerCase().includes(term)); + if (matching.length === 0) { termRatio[term] = Math.random() * 0.8; continue; } + termRatio[term] = matching.filter(p => p.label === "toxic").length / matching.length; + } + + return ( + + {open && ( + + {/* Tracked terms */} + Tracked terms +
+ {selectedTerms.slice(0, 6).map(term => { + const pct = Math.round((termRatio[term] ?? 0) * 100); + const color = nodeColor(termRatio[term] ?? 0); + return ( + +
+
+ {term} +
+ {pct}% + + ); + })} +
+ + {/* Algospeak terms multiselect */} + Algospeak terms +
+ {[...ALGOSPEAK_TERMS, ...customTerms].map(t => { + const selected = selectedTerms.includes(t); + return ( + + ); + })} +
+ + {/* Custom term input */} + Add custom term +
+ setCustomInput(e.target.value)} + onKeyDown={e => e.key === "Enter" && addCustom()} + placeholder="Type a term..." + style={{ + flex: 1, + background: "#141826", + border: "1px solid #1e2540", + borderRadius: 7, + padding: "5px 9px", + color: "#e8eaf0", + fontSize: "0.78rem", + outline: "none", + }} + /> +
+ + + {/* Threshold slider */} + Threshold +
+
+ + {threshold.toFixed(2)} + +
+ setThreshold(parseFloat(e.target.value))} + style={{ width: "100%", accentColor: "#ff4b4b" }} + /> +
+ + {/* Sampling slider */} + Sampling +
+
+ {sampling} +
+ setSampling(parseInt(e.target.value))} + style={{ width: "100%", accentColor: "#ff4b4b" }} + /> +
+ + {/* Fetch section */} + Fetch + + + + {fetching ? "Fetching…" : "Fetch & Analyze"} + + + {/* Custom terms pills at bottom */} + {customTerms.length > 0 && ( +
+ {customTerms.map(t => ( + + {t} + { + setCustomTerms(prev => prev.filter(x => x !== t)); + setSelectedTerms(selectedTerms.filter(x => x !== t)); + }} + /> + + ))} +
+ )} + + )} + + ); +} \ No newline at end of file diff --git a/frontend/src/app/components/SplashScreen.tsx b/frontend/src/app/components/SplashScreen.tsx new file mode 100644 index 0000000000000000000000000000000000000000..4ebf5d6ce57149801a59751e39d865d54d52a186 --- /dev/null +++ b/frontend/src/app/components/SplashScreen.tsx @@ -0,0 +1,279 @@ +import { useEffect, useState, useRef } from "react"; +import { motion, AnimatePresence } from "motion/react"; + +interface SplashScreenProps { + onDone: () => void; +} + +const TITLE = "ALGOSCOPE"; +const SUBTITLE = "Real-time algospeak & toxicity intelligence on Bluesky"; + +// Animated background nodes for the splash +const BG_NODES = Array.from({ length: 14 }, (_, i) => ({ + id: i, + x: 8 + (i * 13.5) % 92, + y: 10 + (i * 17) % 80, + r: 3 + (i % 4) * 1.8, + color: i % 3 === 0 ? "#ff4b4b" : i % 3 === 1 ? "#ff8c42" : "#a6b0ff", + delay: i * 0.07, +})); + +const BG_EDGES = [ + [0, 3], [3, 7], [7, 11], [1, 5], [5, 9], [9, 12], + [2, 6], [6, 10], [0, 4], [4, 8], [8, 13], [2, 7], + [1, 6], [3, 10], [5, 12], [4, 11], +]; + +export function SplashScreen({ onDone }: SplashScreenProps) { + const [lettersDone, setLettersDone] = useState(0); + const [subtitleVisible, setSubtitleVisible] = useState(false); + const [progress, setProgress] = useState(0); + const [exiting, setExiting] = useState(false); + const timerRef = useRef>(null); + + useEffect(() => { + // Letter-by-letter reveal + let i = 0; + const letterInterval = setInterval(() => { + i++; + setLettersDone(i); + if (i >= TITLE.length) clearInterval(letterInterval); + }, 80); + + // Show subtitle after title is done + timerRef.current = setTimeout(() => setSubtitleVisible(true), 900); + + // Progress bar + let prog = 0; + const progInterval = setInterval(() => { + prog += 1.6; + setProgress(Math.min(100, prog)); + if (prog >= 100) clearInterval(progInterval); + }, 28); + + // Exit + const exitTimer = setTimeout(() => { + setExiting(true); + setTimeout(onDone, 700); + }, 2600); + + return () => { + clearInterval(letterInterval); + clearInterval(progInterval); + if (timerRef.current) clearTimeout(timerRef.current); + clearTimeout(exitTimer); + }; + }, [onDone]); + + return ( + + {!exiting && ( + + {/* Animated background graph */} + + {BG_EDGES.map(([a, b], i) => { + const nodeA = BG_NODES[a]; + const nodeB = BG_NODES[b]; + return ( + + ); + })} + {BG_NODES.map(node => ( + + ))} + + + {/* Radial glow */} +
+ + {/* Logo icon */} + + A + + + {/* Title: letter by letter */} +
+ {TITLE.split("").map((char, i) => ( + + {char} + + ))} +
+ + {/* Subtitle */} + + {subtitleVisible && ( + + {SUBTITLE} + + )} + + + {/* Progress bar */} +
+ +
+ + {/* Loading label */} + + {progress < 100 ? "Loading intelligence engine…" : "Ready"} + + + {/* Version badge */} + + by Odeliya Charitonova · TAU CS + + + )} + + ); +} diff --git a/frontend/src/app/components/TermComparisonTab.tsx b/frontend/src/app/components/TermComparisonTab.tsx new file mode 100644 index 0000000000000000000000000000000000000000..ef81d2cd74530e1874d0c71de2780d8181e426e9 --- /dev/null +++ b/frontend/src/app/components/TermComparisonTab.tsx @@ -0,0 +1,364 @@ +import { useState } from "react"; +import { motion } from "motion/react"; +import { Post, ALGOSPEAK_TERMS, nodeColor } from "./mockData"; + +interface Props { + posts: Post[]; +} + +function termStats(posts: Post[], term: string) { + const matched = posts.filter(p => + p.query_term === term || p.text.toLowerCase().includes(term) + ); + if (!matched.length) return null; + const scores = matched.map(p => p.score); + const toxicN = matched.filter(p => p.label === "toxic").length; + return { + count: matched.length, + toxicRate: (toxicN / matched.length) * 100, + avgScore: scores.reduce((a, b) => a + b, 0) / scores.length, + maxScore: Math.max(...scores), + posts: matched, + }; +} + +const BIN_LABELS = ["0-0.2", "0.2-0.4", "0.4-0.6", "0.6-0.8", "0.8-1.0"]; + +function binPosts(posts: Post[]) { + const bins = [0, 0, 0, 0, 0]; + for (const p of posts) { + const idx = Math.min(4, Math.floor(p.score / 0.2)); + bins[idx]++; + } + return bins; +} + +export function TermComparisonTab({ posts }: Props) { + const [termA, setTermA] = useState(ALGOSPEAK_TERMS[0]); + const [termB, setTermB] = useState(ALGOSPEAK_TERMS[1]); + const [hovered, setHovered] = useState<{ binIdx: number; series: "a" | "b" } | null>(null); + + const sA = termStats(posts, termA); + const sB = termStats(posts, termB); + + const selectStyle: React.CSSProperties = { + background: "#141826", + border: "1px solid #1e2540", + borderRadius: 8, + padding: "6px 10px", + color: "#e8eaf0", + fontSize: "0.83rem", + width: "100%", + cursor: "pointer", + }; + + const compData = BIN_LABELS.map((label, i) => ({ + label, + a: sA ? binPosts(sA.posts)[i] : 0, + b: sB ? binPosts(sB.posts)[i] : 0, + })); + + return ( +
+ + Select two algospeak terms to compare their toxicity profiles from all stored posts. + + + {/* Term selectors */} +
+ +
Term A
+ + + +
Term B
+ + +
+ + {termA === termB && ( +
+ Select two different terms to compare. +
+ )} + + {termA !== termB && ( + <> + {/* Stat cards */} +
+ {[ + { term: termA, stats: sA, color: "#a6b0ff" }, + { term: termB, stats: sB, color: "#ff8c42" }, + ].map(({ term, stats, color }, idx) => ( + +
+ “{term}” +
+ {!stats ? ( +
No data — fetch more posts first.
+ ) : ( +
+ {[ + { label: "Posts", value: String(stats.count), valueColor: color }, + { label: "Toxic rate", value: `${stats.toxicRate.toFixed(1)}%`, valueColor: nodeColor(stats.toxicRate / 100) }, + { label: "Avg score", value: stats.avgScore.toFixed(3), valueColor: nodeColor(stats.avgScore) }, + { label: "Max score", value: stats.maxScore.toFixed(3), valueColor: "#ff4b4b" }, + ].map(({ label, value, valueColor }) => ( +
+
+ {label} +
+
+ {value} +
+
+ ))} +
+ )} + + ))} +
+ + {/* Comparison chart — custom div-based with enhanced visuals */} + {sA && sB && (() => { + const binsA = binPosts(sA.posts); + const binsB = binPosts(sB.posts); + const maxVal = Math.max(...binsA, ...binsB, 1); + const gridLines = [0.25, 0.5, 0.75, 1.0]; + return ( + +
+
+ Score distribution comparison +
+ {/* Legend */} +
+ {[{ label: termA, color: "#a6b0ff", glow: "rgba(166,176,255,0.4)" }, { label: termB, color: "#ff8c42", glow: "rgba(255,140,66,0.4)" }].map(({ label, color, glow }) => ( +
+
+ {label} +
+ ))} +
+
+ + {/* Chart wrapper with y-axis */} +
+ {/* Y-axis labels */} +
+ {[maxVal, Math.round(maxVal * 0.75), Math.round(maxVal * 0.5), Math.round(maxVal * 0.25), 0].map((v, i) => ( +
{v}
+ ))} +
+ + {/* Chart area */} +
+ {/* Grid lines */} +
+ {gridLines.map((_, i) => ( +
+ ))} +
+
+ + {/* Bars */} +
+ {BIN_LABELS.map((label, i) => ( +
+
+ {/* Bar A */} +
setHovered({ binIdx: i, series: "a" })} + onMouseLeave={() => setHovered(null)} + > + 0 ? 3 : 0, + position: "relative", + boxShadow: hovered?.binIdx === i && hovered?.series === "a" + ? "0 0 12px rgba(166,176,255,0.6)" + : "none", + transition: "box-shadow 0.15s, opacity 0.15s", + }} + > + {hovered?.binIdx === i && hovered?.series === "a" && ( +
+ {termA}: {binsA[i]} +
+ )} + +
+ {/* Bar B */} +
setHovered({ binIdx: i, series: "b" })} + onMouseLeave={() => setHovered(null)} + > + 0 ? 3 : 0, + position: "relative", + boxShadow: hovered?.binIdx === i && hovered?.series === "b" + ? "0 0 12px rgba(255,140,66,0.6)" + : "none", + transition: "box-shadow 0.15s, opacity 0.15s", + }} + > + {hovered?.binIdx === i && hovered?.series === "b" && ( +
+ {termB}: {binsB[i]} +
+ )} + +
+
+
+ ))} +
+ + {/* Baseline */} +
+ + {/* X-axis labels */} +
+ {BIN_LABELS.map((label, i) => ( +
{label}
+ ))} +
+
+
+ + {/* X-axis title */} +
+ Toxicity score range +
+ + ); + })()} + + {/* Per-term bar comparisons */} + {sA && sB && ( + +
+ Key metrics at a glance +
+ {[ + { label: "Toxic rate (%)", a: sA.toxicRate, b: sB.toxicRate, max: 100, colorA: "#a6b0ff", colorB: "#ff8c42" }, + { label: "Avg score", a: sA.avgScore * 100, b: sB.avgScore * 100, max: 100, colorA: "#a6b0ff", colorB: "#ff8c42" }, + ].map(({ label, a, b, max, colorA, colorB }) => ( +
+
+ {label} + + {termA}: {a.toFixed(1)} + {" vs "} + {termB}: {b.toFixed(1)} + +
+
+ {[{ pct: a / max * 100, color: colorA }, { pct: b / max * 100, color: colorB }].map((bar, bi) => ( +
+ +
+ ))} +
+
+ ))} + + )} + + )} +
+ ); +} \ No newline at end of file diff --git a/frontend/src/app/components/figma/ImageWithFallback.tsx b/frontend/src/app/components/figma/ImageWithFallback.tsx new file mode 100644 index 0000000000000000000000000000000000000000..0e26139bea20f0f26f899190b21161e44510e740 --- /dev/null +++ b/frontend/src/app/components/figma/ImageWithFallback.tsx @@ -0,0 +1,27 @@ +import React, { useState } from 'react' + +const ERROR_IMG_SRC = + 'data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iODgiIGhlaWdodD0iODgiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyIgc3Ryb2tlPSIjMDAwIiBzdHJva2UtbGluZWpvaW49InJvdW5kIiBvcGFjaXR5PSIuMyIgZmlsbD0ibm9uZSIgc3Ryb2tlLXdpZHRoPSIzLjciPjxyZWN0IHg9IjE2IiB5PSIxNiIgd2lkdGg9IjU2IiBoZWlnaHQ9IjU2IiByeD0iNiIvPjxwYXRoIGQ9Im0xNiA1OCAxNi0xOCAzMiAzMiIvPjxjaXJjbGUgY3g9IjUzIiBjeT0iMzUiIHI9IjciLz48L3N2Zz4KCg==' + +export function ImageWithFallback(props: React.ImgHTMLAttributes) { + const [didError, setDidError] = useState(false) + + const handleError = () => { + setDidError(true) + } + + const { src, alt, style, className, ...rest } = props + + return didError ? ( +
+
+ Error loading image +
+
+ ) : ( + {alt} + ) +} diff --git a/frontend/src/app/components/mockData.ts b/frontend/src/app/components/mockData.ts new file mode 100644 index 0000000000000000000000000000000000000000..eb6f2dfd25551c27d5dc093935b8e55495909796 --- /dev/null +++ b/frontend/src/app/components/mockData.ts @@ -0,0 +1,198 @@ +// ── API base URL ─────────────────────────────────────────────────────────────── +// WHY import.meta.env: Vite reads environment variables at build time. +// VITE_API_URL is empty string in production (same origin as FastAPI), +// and http://localhost:8000 in development. +// This means the same compiled artifact works in both environments without +// any code change — just a different .env file. +const API_BASE = import.meta.env.VITE_API_URL ?? ""; + +// ── Shared types ─────────────────────────────────────────────────────────────── +export const ALGOSPEAK_TERMS = [ + "unalive", "le dollar bean", "seggs", "cute", "based", + "cornhole", "nsfw", "eggplant", "spicy", "ratio", + "touch grass", "down bad", "pretty", "why", "mean", + "better", "someone", "having", "harder", "top", +]; + +export interface Post { + id: number | string; // DB returns number; mock generator uses string + text: string; + score: number; + label: "toxic" | "non-toxic"; + query_term: string; + created_at: string; +} + +export interface GraphNode { + id: string; + frequency: number; // maps to "count" from the API + toxicRatio: number; // maps to "toxic_ratio" from the API +} + +export interface GraphEdge { + source: string; + target: string; + weight: number; +} + +// ── Real API client functions ────────────────────────────────────────────────── + +/** + * Fetch recent classified posts from the database. + * Called on mount to populate initial dashboard state. + */ +export async function apiFetchPosts(limit = 100): Promise { + const res = await fetch(`${API_BASE}/posts?limit=${limit}`); + if (!res.ok) throw new Error(`/posts failed: ${res.status}`); + const data = await res.json(); + // Normalize: API returns id as number, mock uses string — cast to keep + // downstream components happy with either. + return (data.posts as Post[]).map(p => ({ + ...p, + id: p.id ?? 0, + query_term: p.query_term ?? "", + created_at: p.created_at ?? "", + label: p.label === "toxic" ? "toxic" : "non-toxic", + })); +} + +/** + * Fetch just the total post count from the DB without pulling all rows. + * Used to keep the "Posts analyzed" counter in sync with the real DB after + * every fetch — avoids stale-closure bugs in useCallback. + */ +export async function apiFetchTotal(): Promise { + const res = await fetch(`${API_BASE}/posts?limit=1`); + if (!res.ok) return -1; + const data = await res.json(); + return typeof data.total === "number" ? data.total : -1; +} + +/** + * Trigger a Bluesky fetch + batch inference cycle. + * Returns the new posts plus timing metadata for the UI. + */ +export async function apiFetchAndAnalyze( + queries: string[], + limit: number, + threshold: number, +): Promise<{ posts: Post[]; fetchTime: number; inferTime: number; count: number; message?: string }> { + const res = await fetch(`${API_BASE}/fetch-and-analyze`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ queries, limit, threshold }), + }); + if (!res.ok) throw new Error(`/fetch-and-analyze failed: ${res.status}`); + const data = await res.json(); + const posts = (data.posts as Post[]).map(p => ({ + ...p, + id: p.id ?? 0, + query_term: p.query_term ?? "", + created_at: p.created_at ?? "", + label: p.label === "toxic" ? "toxic" : "non-toxic", + })); + return { + posts, + fetchTime: data.fetch_time, + inferTime: data.infer_time, + count: data.count, + message: data.message, + }; +} + +/** + * Fetch co-occurrence graph data as nodes + edges JSON. + * The React canvas simulation handles layout — the server only provides structure. + */ +export async function apiGetGraphData( + minCooccurrence: number, + toxicOnly: boolean, +): Promise<{ nodes: GraphNode[]; edges: GraphEdge[] }> { + const res = await fetch( + `${API_BASE}/graph-data?min_cooccurrence=${minCooccurrence}&toxic_only=${toxicOnly}` + ); + if (!res.ok) throw new Error(`/graph-data failed: ${res.status}`); + const data = await res.json(); + // Map API field names (count, toxic_ratio) to the names the graph + // simulation already uses (frequency, toxicRatio). + const nodes: GraphNode[] = (data.nodes as Array<{ + id: string; count: number; toxic_ratio: number; + }>).map(n => ({ + id: n.id, + frequency: n.count, + toxicRatio: n.toxic_ratio, + })); + return { nodes, edges: data.edges as GraphEdge[] }; +} + +// ── Mock data (kept for local dev / offline fallback) ───────────────────────── +// These are only used if the API is unreachable. They are never sent to the +// backend — no secrets here. + +const MOCK_TEMPLATES = [ + "I can't believe they used {term} like that, this is getting out of hand", + "Found another account using {term} to avoid filters, reported it immediately", + "The {term} discourse is absolutely wild today, why are people like this", + "Seeing {term} trending again. Not a great sign for the platform tbh", + "Whoever invented {term} to avoid filters is frankly unhinged", + "Just saw {term} used unironically in a serious post, internet is something", + "Moderators need to update their filters, {term} is literally everywhere now", + "The {term} to english translation is honestly concerning for society", + "Why does {term} keep showing up in my feed, algorithm is broken again", + "Teaching my mom what {term} means was the hardest conversation I've had this year", + "{term} energy today, not gonna lie tbh", + "Petition to ban {term} from the platform. Who's with me", + "Using {term} unironically is such a major red flag ngl", + "The way {term} has evolved over time is fascinating from a linguistics POV", + "Just another day of seeing {term} everywhere on here smh", + "Why is {term} suddenly all over my fyp, is this a new trend", + "Blocked 3 accounts today for using {term} in my replies", + "{term} popped up again in the discourse, same old same old", + "Can we talk about how normalized {term} has become? Not ok.", + "Researchers studying {term} usage patterns should check bluesky tbh", +]; + +function randomScore(): number { + const r = Math.random(); + if (r < 0.55) return 0.62 + Math.random() * 0.38; + if (r < 0.75) return 0.40 + Math.random() * 0.30; + return Math.random() * 0.38; +} + +export function generateMockPosts(terms: string[], count = 180): Post[] { + const posts: Post[] = []; + const now = Date.now(); + const dayMs = 24 * 60 * 60 * 1000; + + for (let i = 0; i < count; i++) { + const term = terms[Math.floor(Math.random() * terms.length)]; + const tpl = MOCK_TEMPLATES[Math.floor(Math.random() * MOCK_TEMPLATES.length)]; + const text = tpl.replace("{term}", term); + const score = randomScore(); + const created_at = new Date(now - Math.random() * dayMs).toISOString(); + posts.push({ + id: `mock-${i}`, + text, + score: Math.round(score * 1000) / 1000, + label: score >= 0.7 ? "toxic" : "non-toxic", + query_term: term, + created_at, + }); + } + return posts.sort( + (a, b) => new Date(b.created_at).getTime() - new Date(a.created_at).getTime() + ); +} + +// ── Graph colour helpers (used by CoOccurrenceGraph.tsx) ────────────────────── +export function nodeColor(toxicRatio: number): string { + if (toxicRatio >= 0.7) return "#ff4b4b"; + if (toxicRatio >= 0.4) return "#ff8c42"; + return "#2ecc71"; +} + +export function nodeColorAlpha(toxicRatio: number, alpha = 0.25): string { + if (toxicRatio >= 0.7) return `rgba(255,75,75,${alpha})`; + if (toxicRatio >= 0.4) return `rgba(255,140,66,${alpha})`; + return `rgba(46,204,113,${alpha})`; +} diff --git a/frontend/src/app/components/ui/accordion.tsx b/frontend/src/app/components/ui/accordion.tsx new file mode 100644 index 0000000000000000000000000000000000000000..bd6b1e3351323c2ccca3460dee3fd37b1101eba2 --- /dev/null +++ b/frontend/src/app/components/ui/accordion.tsx @@ -0,0 +1,66 @@ +"use client"; + +import * as React from "react"; +import * as AccordionPrimitive from "@radix-ui/react-accordion"; +import { ChevronDownIcon } from "lucide-react"; + +import { cn } from "./utils"; + +function Accordion({ + ...props +}: React.ComponentProps) { + return ; +} + +function AccordionItem({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AccordionTrigger({ + className, + children, + ...props +}: React.ComponentProps) { + return ( + + svg]:rotate-180", + className, + )} + {...props} + > + {children} + + + + ); +} + +function AccordionContent({ + className, + children, + ...props +}: React.ComponentProps) { + return ( + +
{children}
+ + ); +} + +export { Accordion, AccordionItem, AccordionTrigger, AccordionContent }; diff --git a/frontend/src/app/components/ui/alert-dialog.tsx b/frontend/src/app/components/ui/alert-dialog.tsx new file mode 100644 index 0000000000000000000000000000000000000000..875b8df4de0231047736dcb04c5d81287d88ae42 --- /dev/null +++ b/frontend/src/app/components/ui/alert-dialog.tsx @@ -0,0 +1,157 @@ +"use client"; + +import * as React from "react"; +import * as AlertDialogPrimitive from "@radix-ui/react-alert-dialog"; + +import { cn } from "./utils"; +import { buttonVariants } from "./button"; + +function AlertDialog({ + ...props +}: React.ComponentProps) { + return ; +} + +function AlertDialogTrigger({ + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AlertDialogPortal({ + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AlertDialogOverlay({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AlertDialogContent({ + className, + ...props +}: React.ComponentProps) { + return ( + + + + + ); +} + +function AlertDialogHeader({ + className, + ...props +}: React.ComponentProps<"div">) { + return ( +
+ ); +} + +function AlertDialogFooter({ + className, + ...props +}: React.ComponentProps<"div">) { + return ( +
+ ); +} + +function AlertDialogTitle({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AlertDialogDescription({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AlertDialogAction({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AlertDialogCancel({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +export { + AlertDialog, + AlertDialogPortal, + AlertDialogOverlay, + AlertDialogTrigger, + AlertDialogContent, + AlertDialogHeader, + AlertDialogFooter, + AlertDialogTitle, + AlertDialogDescription, + AlertDialogAction, + AlertDialogCancel, +}; diff --git a/frontend/src/app/components/ui/alert.tsx b/frontend/src/app/components/ui/alert.tsx new file mode 100644 index 0000000000000000000000000000000000000000..9c35976c714d89ae0497f599d3a1fd707dd75fd8 --- /dev/null +++ b/frontend/src/app/components/ui/alert.tsx @@ -0,0 +1,66 @@ +import * as React from "react"; +import { cva, type VariantProps } from "class-variance-authority"; + +import { cn } from "./utils"; + +const alertVariants = cva( + "relative w-full rounded-lg border px-4 py-3 text-sm grid has-[>svg]:grid-cols-[calc(var(--spacing)*4)_1fr] grid-cols-[0_1fr] has-[>svg]:gap-x-3 gap-y-0.5 items-start [&>svg]:size-4 [&>svg]:translate-y-0.5 [&>svg]:text-current", + { + variants: { + variant: { + default: "bg-card text-card-foreground", + destructive: + "text-destructive bg-card [&>svg]:text-current *:data-[slot=alert-description]:text-destructive/90", + }, + }, + defaultVariants: { + variant: "default", + }, + }, +); + +function Alert({ + className, + variant, + ...props +}: React.ComponentProps<"div"> & VariantProps) { + return ( +
+ ); +} + +function AlertTitle({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ); +} + +function AlertDescription({ + className, + ...props +}: React.ComponentProps<"div">) { + return ( +
+ ); +} + +export { Alert, AlertTitle, AlertDescription }; diff --git a/frontend/src/app/components/ui/aspect-ratio.tsx b/frontend/src/app/components/ui/aspect-ratio.tsx new file mode 100644 index 0000000000000000000000000000000000000000..c16d6bcb917ec490aa9c468afcbd5c829086b080 --- /dev/null +++ b/frontend/src/app/components/ui/aspect-ratio.tsx @@ -0,0 +1,11 @@ +"use client"; + +import * as AspectRatioPrimitive from "@radix-ui/react-aspect-ratio"; + +function AspectRatio({ + ...props +}: React.ComponentProps) { + return ; +} + +export { AspectRatio }; diff --git a/frontend/src/app/components/ui/avatar.tsx b/frontend/src/app/components/ui/avatar.tsx new file mode 100644 index 0000000000000000000000000000000000000000..c9904518548717b6bbfe9bdf1857e483bbe35d78 --- /dev/null +++ b/frontend/src/app/components/ui/avatar.tsx @@ -0,0 +1,53 @@ +"use client"; + +import * as React from "react"; +import * as AvatarPrimitive from "@radix-ui/react-avatar"; + +import { cn } from "./utils"; + +function Avatar({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AvatarImage({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +function AvatarFallback({ + className, + ...props +}: React.ComponentProps) { + return ( + + ); +} + +export { Avatar, AvatarImage, AvatarFallback }; diff --git a/frontend/src/app/components/ui/badge.tsx b/frontend/src/app/components/ui/badge.tsx new file mode 100644 index 0000000000000000000000000000000000000000..2ccc2c444051b79c7e9fe5ef2e07f7eebd09c8d3 --- /dev/null +++ b/frontend/src/app/components/ui/badge.tsx @@ -0,0 +1,46 @@ +import * as React from "react"; +import { Slot } from "@radix-ui/react-slot"; +import { cva, type VariantProps } from "class-variance-authority"; + +import { cn } from "./utils"; + +const badgeVariants = cva( + "inline-flex items-center justify-center rounded-md border px-2 py-0.5 text-xs font-medium w-fit whitespace-nowrap shrink-0 [&>svg]:size-3 gap-1 [&>svg]:pointer-events-none focus-visible:border-ring focus-visible:ring-ring/50 focus-visible:ring-[3px] aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 aria-invalid:border-destructive transition-[color,box-shadow] overflow-hidden", + { + variants: { + variant: { + default: + "border-transparent bg-primary text-primary-foreground [a&]:hover:bg-primary/90", + secondary: + "border-transparent bg-secondary text-secondary-foreground [a&]:hover:bg-secondary/90", + destructive: + "border-transparent bg-destructive text-white [a&]:hover:bg-destructive/90 focus-visible:ring-destructive/20 dark:focus-visible:ring-destructive/40 dark:bg-destructive/60", + outline: + "text-foreground [a&]:hover:bg-accent [a&]:hover:text-accent-foreground", + }, + }, + defaultVariants: { + variant: "default", + }, + }, +); + +function Badge({ + className, + variant, + asChild = false, + ...props +}: React.ComponentProps<"span"> & + VariantProps & { asChild?: boolean }) { + const Comp = asChild ? Slot : "span"; + + return ( + + ); +} + +export { Badge, badgeVariants }; diff --git a/frontend/src/app/components/ui/breadcrumb.tsx b/frontend/src/app/components/ui/breadcrumb.tsx new file mode 100644 index 0000000000000000000000000000000000000000..8f84d7e9fe4dcfdec34819e5af802a80d44e3fdf --- /dev/null +++ b/frontend/src/app/components/ui/breadcrumb.tsx @@ -0,0 +1,109 @@ +import * as React from "react"; +import { Slot } from "@radix-ui/react-slot"; +import { ChevronRight, MoreHorizontal } from "lucide-react"; + +import { cn } from "./utils"; + +function Breadcrumb({ ...props }: React.ComponentProps<"nav">) { + return