Am I The Asshole?

Guess what percentage of Reddit users thought the poster was the asshole

\n\n

{post.title}

{post.content}

\n\n {!result ? (\n

\n What percentage of people thought this person was the asshole?\n \n

{guess}%

\n \n

\n ) : (\n

Results

{result.message}

{result.actualPct}% of people thought OP was the asshole

(You were off by {result.difference}%)

\n \n

\n )}\n

\n );\n}\n",[],[857,863,869],{"_104":876,"_95":877,"_158":878,"_160":864,"_161":139,"_162":879,"_164":165,"_106":880,"_100":119,"_167":824,"_169":881,"_170":22,"_171":-5,"_172":882,"_98":883},"c6ec4380-67f2-11f0-8ed4-0224a6c84d84","App.tsx","01983875-e8ca-7735-b93b-c77249497ad5",12,["D",1753295286516],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { AITAGame } from \"./AITAGame.tsx\";\n\nexport function App() {\n return ;\n}",[],[857,863,877],[857,863],{"_104":886,"_95":887,"_158":888,"_160":858,"_161":139,"_162":889,"_164":165,"_106":890,"_100":119,"_167":278,"_169":891,"_170":22,"_171":-5,"_172":892,"_98":893},"c6fdc826-67f2-11f0-8c08-0224a6c84d84","favicon.svg","01983875-e8ca-7735-b93b-cf7591f06a1e",14,["D",1753295286663],"\n",[],[857,887],{"_104":895,"_95":896,"_158":897,"_160":858,"_161":139,"_162":898,"_164":165,"_106":899,"_100":119,"_167":278,"_169":900,"_170":22,"_171":-5,"_172":901,"_98":902},"c6e7bc5c-67f2-11f0-847e-0224a6c84d84","index.html","01983875-e8ca-7735-b93b-d349eb214392",11,["D",1753295286504],"\n\n \n \n \n Am I The Asshole? Guessing Game\n \n \n \n \n

\n \n \n \n",[],[857,896],{"_104":904,"_95":905,"_158":906,"_160":858,"_161":139,"_162":119,"_164":165,"_106":907,"_100":32,"_167":824,"_169":908,"_170":22,"_171":-5,"_172":909,"_98":910},"9158d358-67ef-11f0-97a8-0224a6c84d84","index.tsx","01983875-e8ca-7735-b93b-d722f7851bd0",["D",1753293908093],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { createRoot } from \"https://esm.sh/react-dom@18.2.0/client\";\nimport { App } from \"./components/App.tsx\";\n\nconst root = document.getElementById(\"root\");\nif (!root) {\n throw new Error(\"No root element found\");\n}\n\ncreateRoot(root).render();",[],[857,905],{"_104":912,"_95":913,"_158":914,"_160":858,"_161":139,"_162":915,"_164":22,"_106":916,"_100":119,"_167":278,"_169":917,"_170":22,"_171":-5,"_172":918,"_98":919},"c6f58288-67f2-11f0-ade2-0224a6c84d84","style.css","01983875-e8ca-7735-b93b-d99a041ace23",13,["D",1753295286556],"body {\n font-family: -apple-system, Arial, sans-serif;\n color: #333;\n background-color: #f8f9fa;\n margin: 0;\n padding: 0;\n}\n\n.container {\n max-width: 800px;\n margin: 0 auto;\n padding: 20px;\n}\n\n.header {\n background-color: #ff4500; /* Reddit orange */\n color: white;\n padding: 20px 0;\n text-align: center;\n margin-bottom: 30px;\n box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);\n}\n\n.header h1 {\n margin: 0;\n font-size: 2.5rem;\n}\n\n.header p {\n margin: 10px 0 0;\n opacity: 0.9;\n}\n\n.post-card {\n background-color: white;\n border-radius: 8px;\n padding: 25px;\n margin-bottom: 30px;\n box-shadow: 0 2px 15px rgba(0, 0, 0, 0.05);\n}\n\n.post-title {\n font-size: 1.6rem;\n margin-top: 0;\n margin-bottom: 20px;\n color: #1a1a1b;\n}\n\n.post-content {\n line-height: 1.6;\n margin-bottom: 30px;\n white-space: pre-wrap;\n}\n\n.slider-container {\n margin: 40px 0;\n}\n\n.slider-label {\n display: block;\n font-weight: bold;\n margin-bottom: 15px;\n text-align: center;\n font-size: 1.2rem;\n}\n\n.slider {\n width: 100%;\n -webkit-appearance: none;\n height: 10px;\n border-radius: 5px;\n background: #d3d3d3;\n outline: none;\n margin-bottom: 10px;\n}\n\n.slider::-webkit-slider-thumb {\n -webkit-appearance: none;\n appearance: none;\n width: 25px;\n height: 25px;\n border-radius: 50%;\n background: #ff4500;\n cursor: pointer;\n}\n\n.slider::-moz-range-thumb {\n width: 25px;\n height: 25px;\n border-radius: 50%;\n background: #ff4500;\n cursor: pointer;\n border: none;\n}\n\n.slider-value {\n text-align: center;\n font-size: 1.5rem;\n font-weight: bold;\n margin-top: 10px;\n color: #ff4500;\n}\n\nbutton {\n background-color: #ff4500;\n color: white;\n border: none;\n padding: 12px 24px;\n border-radius: 4px;\n cursor: pointer;\n font-size: 1rem;\n font-weight: bold;\n display: block;\n margin: 30px auto 0;\n transition: background-color 0.2s;\n}\n\nbutton:hover {\n background-color: #e03d00;\n}\n\nbutton:disabled {\n background-color: #cccccc;\n cursor: not-allowed;\n}\n\n.result {\n background-color: #f0f7ff;\n border-radius: 8px;\n padding: 20px;\n margin-top: 30px;\n text-align: center;\n animation: fadeIn 0.5s;\n}\n\n.result-heading {\n font-size: 1.3rem;\n margin-top: 0;\n margin-bottom: 15px;\n color: #1a1a1b;\n}\n\n.result-message {\n margin-bottom: 15px;\n font-size: 1.1rem;\n}\n\n.actual-value {\n font-weight: bold;\n font-size: 1.4rem;\n color: #ff4500;\n}\n\n.difference {\n margin-top: 10px;\n font-style: italic;\n}\n\n.play-again {\n background-color: #0079d3; /* Reddit blue */\n margin-top: 20px;\n}\n\n.play-again:hover {\n background-color: #0061a9;\n}\n\n@keyframes fadeIn {\n from { opacity: 0; transform: translateY(10px); }\n to { opacity: 1; transform: translateY(0); }\n}\n\n/* Responsive adjustments */\n@media (max-width: 600px) {\n .header h1 {\n font-size: 2rem;\n }\n\n .post-title {\n font-size: 1.4rem;\n }\n\n .slider-value {\n font-size: 1.3rem;\n }\n}",[],[857,913],[857],{"_104":922,"_95":923,"_158":924,"_160":-5,"_161":139,"_162":925,"_164":165,"_106":926,"_100":32,"_167":168,"_169":-5,"_170":22,"_171":-5,"_172":927,"_98":936},"f942ed08-67fb-11f0-8ad5-0224a6c84d84","local","f942edbc-67fb-11f0-8ad5-0224a6c84d84",31,["D",1753299236474],[928],{"_104":929,"_95":930,"_158":931,"_160":924,"_161":139,"_162":101,"_164":165,"_106":932,"_100":32,"_167":278,"_169":933,"_170":22,"_171":-5,"_172":934,"_98":935},"03ba4cb8-67fc-11f0-8b06-0224a6c84d84","analyse_posts.py","03ba4d62-67fc-11f0-8b06-0224a6c84d84",["D",1753299254039],"#!/usr/bin/env python3\n\nimport os\nimport sqlite3\nimport sys\nimport re\nimport json\nfrom pathlib import Path\n\n\"\"\"\nThis script analyzes the top 100 posts from top100.sqlite and calculates\nthe percentage of comments that contain \"YTA\" (You're The Asshole).\nResults are stored in a new database with enhanced submission data and a JSONL file.\n\"\"\"\n\nSOURCE_DB_PATH = \"../db/top100.sqlite\"\nTARGET_DB_PATH = \"../db/top100_analyzed.sqlite\"\nOUTPUT_JSONL_PATH = \"../backend/data/top100_analyzed.jsonl\"\n\n\ndef contains_yta(comment_text):\n \"\"\"\n Check if a comment contains YTA (You're The Asshole).\n This looks for YTA as a standalone word to avoid false positives.\n \"\"\"\n if not comment_text:\n return False\n \n # Use regex to find YTA as a standalone word (case insensitive)\n # \\b ensures word boundaries to avoid matching \"YTAL\" or \"MYTA\" etc.\n pattern = r'\\bYTA\\b'\n return bool(re.search(pattern, comment_text, re.IGNORECASE))\n\n\ndef main():\n print(f\"Analyzing top 100 posts from {SOURCE_DB_PATH}...\")\n\n # Check if source file exists\n source_path = Path(SOURCE_DB_PATH)\n if not source_path.exists():\n print(f\"Error: Source database file not found: {SOURCE_DB_PATH}\")\n print(\"Please run extract_top_posts.py first to create the top100.sqlite database.\")\n sys.exit(1)\n\n # Create directory for target database if needed\n target_dir = os.path.dirname(TARGET_DB_PATH)\n os.makedirs(target_dir, exist_ok=True)\n print(f\"Ensured directory {target_dir} exists.\")\n\n # Remove target files if they exist\n if os.path.exists(TARGET_DB_PATH):\n os.remove(TARGET_DB_PATH)\n print(\"Removed existing target database.\")\n \n if os.path.exists(OUTPUT_JSONL_PATH):\n os.remove(OUTPUT_JSONL_PATH)\n print(\"Removed existing JSONL file.\")\n\n try:\n # Open source database\n print(\"Opening source database...\")\n source_conn = sqlite3.connect(SOURCE_DB_PATH)\n source_conn.row_factory = sqlite3.Row\n source_cursor = source_conn.cursor()\n print(\"Source database opened successfully.\")\n\n # Create target database\n print(\"Creating target database...\")\n target_conn = sqlite3.connect(TARGET_DB_PATH)\n target_conn.row_factory = sqlite3.Row\n target_cursor = target_conn.cursor()\n print(\"Target database created successfully.\")\n\n # Create enhanced submission table with YTA percentage - separate statements\n target_cursor.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS \"submission_analysis\" (\n \"id\" INTEGER NOT NULL PRIMARY KEY,\n \"submission_id\" VARCHAR(255) NOT NULL,\n \"title\" TEXT NOT NULL,\n \"selftext\" TEXT NOT NULL,\n \"created_utc\" DATE NOT NULL,\n \"permalink\" TEXT NOT NULL,\n \"score\" INTEGER NOT NULL,\n \"total_comments\" INTEGER NOT NULL DEFAULT 0,\n \"yta_comments\" INTEGER NOT NULL DEFAULT 0,\n \"yta_percentage\" REAL NOT NULL DEFAULT 0.0\n )\n \"\"\")\n \n # Create the index in a separate statement\n target_cursor.execute(\"\"\"\n CREATE UNIQUE INDEX \"submission_analysis_submission_id\" ON \"submission_analysis\" (\"submission_id\")\n \"\"\")\n \n print(\"Target database table created.\")\n\n # Begin transaction\n target_conn.execute('BEGIN TRANSACTION')\n\n # Get all submissions from source\n source_cursor.execute(\"\"\"\n SELECT id, submission_id, title, selftext, created_utc, permalink, score \n FROM submission \n ORDER BY score DESC\n \"\"\")\n submissions = source_cursor.fetchall()\n print(f\"Found {len(submissions)} submissions to analyze.\")\n\n # Analyze each submission\n insert_sql = \"\"\"\n INSERT INTO submission_analysis \n (id, submission_id, title, selftext, created_utc, permalink, score, \n total_comments, yta_comments, yta_percentage) \n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n \"\"\"\n\n # Open JSONL file for writing\n with open(OUTPUT_JSONL_PATH, 'w', encoding='utf-8') as jsonl_file:\n total_analyzed = 0\n analyzed_posts = []\n \n for i, submission in enumerate(submissions):\n submission_db_id = submission['submission_id']\n \n # Get all comments for this submission\n source_cursor.execute(\"\"\"\n SELECT message FROM comment \n WHERE submission_id = ?\n \"\"\", (submission_db_id,))\n \n comments = source_cursor.fetchall()\n total_comments = len(comments)\n \n # Count YTA comments\n yta_comments = 0\n for comment in comments:\n if contains_yta(comment['message']):\n yta_comments += 1\n \n # Calculate percentage\n yta_percentage = (yta_comments / total_comments * 100) if total_comments > 0 else 0.0\n \n # Create a post object\n post_data = {\n \"id\": submission['id'],\n \"submission_id\": submission['submission_id'],\n \"title\": submission['title'],\n \"selftext\": submission['selftext'],\n \"created_utc\": submission['created_utc'],\n \"permalink\": submission['permalink'],\n \"score\": submission['score'],\n \"total_comments\": total_comments,\n \"yta_comments\": yta_comments,\n \"yta_percentage\": round(yta_percentage, 2)\n }\n \n # Insert into target database\n target_cursor.execute(insert_sql, (\n submission['id'],\n submission['submission_id'],\n submission['title'],\n submission['selftext'],\n submission['created_utc'],\n submission['permalink'],\n submission['score'],\n total_comments,\n yta_comments,\n round(yta_percentage, 2)\n ))\n \n # Write to JSONL file\n jsonl_file.write(json.dumps(post_data) + '\\n')\n \n total_analyzed += 1\n \n # Show progress for first 10 and every 10th after that\n if i < 10 or (i + 1) % 10 == 0:\n print(f\"Analyzed submission {i+1}: '{submission['submission_id']}' - \"\n f\"{total_comments} comments, {yta_comments} YTA ({yta_percentage:.1f}%)\")\n\n # Commit transaction\n target_conn.execute('COMMIT')\n print('Database transaction committed successfully.')\n\n # Show summary statistics\n target_cursor.execute(\"\"\"\n SELECT \n COUNT(*) as total_posts,\n AVG(yta_percentage) as avg_yta_percentage,\n MIN(yta_percentage) as min_yta_percentage,\n MAX(yta_percentage) as max_yta_percentage,\n SUM(total_comments) as total_comments_analyzed,\n SUM(yta_comments) as total_yta_comments\n FROM submission_analysis\n \"\"\")\n \n stats = target_cursor.fetchone()\n \n print(\"\\n=== ANALYSIS SUMMARY ===\")\n print(f\"Total posts analyzed: {stats['total_posts']}\")\n print(f\"Total comments analyzed: {stats['total_comments_analyzed']}\")\n print(f\"Total YTA comments: {stats['total_yta_comments']}\")\n print(f\"Average YTA percentage: {stats['avg_yta_percentage']:.2f}%\")\n print(f\"Minimum YTA percentage: {stats['min_yta_percentage']:.2f}%\")\n print(f\"Maximum YTA percentage: {stats['max_yta_percentage']:.2f}%\")\n\n # Show top 5 posts with highest YTA percentage\n target_cursor.execute(\"\"\"\n SELECT submission_id, title, total_comments, yta_comments, yta_percentage\n FROM submission_analysis \n WHERE total_comments > 0\n ORDER BY yta_percentage DESC \n LIMIT 5\n \"\"\")\n \n top_yta_posts = target_cursor.fetchall()\n \n print(\"\\n=== TOP 5 POSTS WITH HIGHEST YTA PERCENTAGE ===\")\n for i, post in enumerate(top_yta_posts, 1):\n print(f\"{i}. {post['submission_id']} ({post['yta_percentage']:.1f}% YTA)\")\n print(f\" Comments: {post['yta_comments']}/{post['total_comments']}\")\n print(f\" Title: {post['title'][:80]}{'...' if len(post['title']) > 80 else ''}\")\n print()\n\n # Close connections\n source_conn.close()\n target_conn.close()\n\n print(\"Analysis complete!\")\n print(f\"Source database: {SOURCE_DB_PATH}\")\n print(f\"Analysis results database: {TARGET_DB_PATH}\")\n print(f\"Analysis results JSONL: {OUTPUT_JSONL_PATH}\")\n print(f\"Total posts analyzed: {total_analyzed}\")\n\n except sqlite3.Error as e:\n print(f\"SQLite error: {e}\")\n try:\n # Check if transaction is active before rolling back\n target_conn.execute(\"SELECT 1 FROM sqlite_master LIMIT 1\")\n target_conn.execute('ROLLBACK')\n print('Transaction rolled back due to error.')\n except Exception as rollback_error:\n print(f\"Error during rollback: {rollback_error}\")\n sys.exit(1)\n except Exception as e:\n print(f\"Error during analysis: {e}\")\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n main()",[],[923,930],[923],{"_104":938,"_95":274,"_158":939,"_160":-5,"_161":139,"_162":940,"_164":22,"_106":941,"_100":163,"_167":278,"_169":942,"_170":22,"_171":-5,"_172":943,"_98":944},"9d860d18-67f3-11f0-9b67-0224a6c84d84","78f0477a-67f3-11f0-9b33-0224a6c84d84",29,["D",1753295646586],"vcs.xml\nstate.json\n./data/",[],[274],{"_104":946,"_95":947,"_158":948,"_160":-5,"_161":139,"_162":949,"_164":22,"_106":950,"_100":163,"_167":278,"_169":951,"_170":22,"_171":-5,"_172":952,"_98":953},"9b5cdfd0-67f3-11f0-8195-0224a6c84d84",".vtignore","c6c4281e-67f2-11f0-84e8-0224a6c84d84",27,["D",1753295642961],".git\n.vscode\n.cursorrules\n.DS_Store\nnode_modules\nvendor\n./data/",[],[947],{"_104":955,"_95":956,"_158":957,"_160":-5,"_161":139,"_162":958,"_164":165,"_106":959,"_100":119,"_167":278,"_169":960,"_170":22,"_171":-5,"_172":961,"_98":962},"c709d896-67f2-11f0-8ec1-0224a6c84d84","README.md","01983875-e8ca-7735-b93b-e26da749ec93",16,["D",1753295286718],"# Am I The Asshole? Guessing Game\n\nA web-based guessing game built with React and Hono where users try to guess what percentage of Reddit users thought the poster was the asshole in posts from the r/AmITheAsshole subreddit.\n\n## Game Description\n\nIn this game, players are presented with a post from the \"Am I The Asshole?\" subreddit, where people share situations and ask if they were in the wrong. Players use a slider to guess what percentage of Reddit users voted that the original poster was the asshole. After submitting their guess, they see how close they were to the actual percentage.\n\n## Features\n\n- Beautiful, Reddit-inspired UI\n- Interactive slider for making guesses\n- Feedback on how close your guess was to the actual percentage\n- Responsive design that works on mobile and desktop\n\n## Technical Structure\n\n- **Frontend**: React with TypeScript, modern CSS\n- **Backend**: Hono (lightweight web framework)\n- **Data**: Currently uses static data, with plans to connect to SQL later\n\n## Project Structure\n\n- `/backend/index.ts` - The main server entry point\n- `/backend/data/posts.ts` - Static AITA post data\n- `/frontend/components/AITAGame.tsx` - The main game component\n- `/frontend/components/App.tsx` - The root React component\n- `/frontend/style.css` - Styles for the application\n\n## Future Enhancements\n\n- Connect to a SQL database for storing and retrieving posts\n- Add multiple posts with the ability to cycle through them\n- Add user accounts to track scores\n- Add social sharing features",[],[956],{"_104":964,"_95":965,"_158":966,"_160":-5,"_161":139,"_162":967,"_164":165,"_106":968,"_100":32,"_167":278,"_169":969,"_170":22,"_171":-5,"_172":970,"_98":971},"c6b532c8-67f2-11f0-97a0-0224a6c84d84","deno.json","c6b53372-67f2-11f0-97a0-0224a6c84d84",5,["D",1753295286180],"{\n \"$schema\": \"https://raw.githubusercontent.com/denoland/deno/348900b8b79f4a434cab4c74b3bc8d4d2fa8ee74/cli/schemas/config-file.v1.json\",\n \"lock\": false,\n \"compilerOptions\": {\n \"noImplicitAny\": false,\n \"strict\": false,\n \"types\": [\n \"https://www.val.town/types/valtown.d.ts\"\n ],\n \"lib\": [\n \"dom\",\n \"dom.iterable\",\n \"dom.asynciterable\",\n \"deno.ns\",\n \"deno.unstable\"\n ]\n },\n \"lint\": {\n \"files\": {\n \"include\": [\n \"deno:/https/esm.town/**/*\"\n ]\n },\n \"rules\": {\n \"exclude\": [\n \"no-explicit-any\"\n ]\n }\n },\n \"node_modules_dir\": false,\n \"experimental\": {\n \"unstable-node-globals\": true,\n \"unstable-temporal\": true,\n \"unstable-worker-options\": true,\n \"unstable-sloppy-imports\": true\n 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