X-POSE

AI-powered Twitter personality analyzer — savage, sharable, screenshot-worthy.

中文版

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Overview

X-POSE is a single-user Twitter personality analyzer. Enter a username → scrape their profile & latest tweets → AI generates a 15-dimension personality report with cards, roast, strengths, weaknesses, and account valuation.

Inspired by Wordware's viral Twitter personality project.

Screenshots

English report for @elonmusk. See case/cn/ for Chinese examples.

Features

• Scrape — fetches profile info + latest tweets via Playwright/CloakBrowser
• Analyze — sends profile + tweets to DeepSeek V4 Pro and returns structured JSON
• 15 Report Cards — about, roast, strengths, weaknesses, love life, money, health, and more
• Download as Image — one-click PNG screenshot via html2canvas
• Account Valuation — estimates the account's market value using a weighted formula (followers, engagement, verified status)
• Share to X — pre-filled tweet with your report
• Bilingual UI — Simplified Chinese / English toggle
• 1-Click Concurrency Limit — prevents server overload on Streamlit Cloud

Quick Start

Try it online

👉 xpose7.streamlit.app

Run locally

# 1. Clone
git clone https://github.com/gokuscraper/x-pose.git
cd x-pose

# 2. Install Python deps
pip install -r requirements.txt

# 3. Install Playwright browsers (required by CloakBrowser)
playwright install chromium

# 4. Configure API key
# Create .streamlit/secrets.toml with:
# SILICON_API_KEY = "sk-your-key-here"

# 5. Run
streamlit run streamlit_app.py

API Key

X-POSE uses SiliconFlow DeepSeek V4 Pro. Get your API key from SiliconFlow Console.

Project Structure

x-pose/
├── streamlit_app.py        # Home page — username input → scrape
├── pages/
│   └── 1_Analysis.py       # Analysis report page
├── lib/
│   ├── ai.py               # AI prompt & API call
│   ├── tweet_utils.py      # Tweet formatting & stats
│   └── sidebar.py          # Sidebar navigation
├── scraper/
│   ├── worker.py           # Subprocess entry point (avoids event-loop conflict)
│   ├── subprocess_client.py# Subprocess communication
│   ├── client.py           # CloakBrowser wrapper
│   ├── fetcher.py          # Fetch user + tweets
│   ├── parser.py           # Parse raw API data → models
│   ├── models.py           # TwitterUser, Tweet dataclasses
│   ├── storage.py          # Local JSON caching
│   └── config.py           # API endpoints config
├── locales/
│   ├── zh.json             # Chinese UI strings
│   └── en.json             # English UI strings
├── i18n.py                 # i18n helper
├── requirements.txt
└── packages.txt            # System deps for Streamlit Cloud

How It Works

flowchart LR
    A[User Input] --> B[Playwright Subprocess]
    B --> C[Profile JSON + Tweets]
    C --> D[DeepSeek V4 Pro]
    D --> E[15-Field JSON Report]
    E --> F[Card Grid UI]
    F --> G[Download PNG / Share to X]

Loading

1. Input — username (supports @user, https://x.com/user, bare name)
2. Scrape — subprocess runs Playwright via CloakBrowser to bypass Vercel challenges
3. Analyze — profile + tweets sent to DeepSeek V4 Pro with a savage, witty prompt
4. Report — 15 cards rendered in a responsive grid
5. Share — download as PNG or share to X with pre-filled text

Tech Stack

Layer	Technology
Frontend	Streamlit (single-page app)
AI Model	DeepSeek V4 Pro via SiliconFlow API
Scraper	CloakBrowser (Playwright-based)
Deployment	Streamlit Cloud
License	Apache 2.0

License

Apache 2.0