/Tiktok-Angrybird

A Python tool that scrolls endlessly on Tiktok for e-commerce data extraction

Primary LanguagePythonMIT LicenseMIT

TikTok AngryBird 🦅

A powerful TikTok video analysis tool that combines data scraping and visualization capabilities. This project consists of a desktop scraper application and a web-based analytics dashboard.

Demo Video

TikTok AngryBird Demo

🎥 Watch the full demo on YouTube

Features

Screenshot_17

Desktop Scraper (main.py)

  • 🔐 Cookie-based authentication for TikTok access
  • 🤖 Automated video data collection using Playwright
  • 🎯 Smart filtering for dropshipping-related content
  • 💾 Excel export functionality
  • 🖥️ User-friendly GUI built with CustomTkinter

Screenshot_18

Analytics Dashboard (frontend.py)

  • 📊 Interactive data visualizations with Plotly
  • 🏷️ Hashtag analysis and engagement metrics
  • 🤖 AI-powered data insights using Groq API
  • 📈 Trend analysis and competition tracking
  • 🔍 Advanced filtering and search capabilities

Requirements

Backend (main.py)

customtkinter
playwright
pandas
Pillow

Frontend (frontend.py)

streamlit
pandas
plotly
groq
python-dotenv
xlsxwriter

Installation

  1. Clone this repository
  2. Install the required packages:
pip install -r requirements.txt
  1. Install Playwright browsers:
playwright install firefox

Setup

  1. Create a .env file in the project root
  2. Add your Groq API key:
API_KEY=your_groq_api_key_here

Usage

Running the Scraper

  1. Run python main.py
  2. Enter your TikTok cookie in the input field
  3. Choose whether to filter for dropshipping content
  4. Click "Start Scraping"

Running the Analytics Dashboard

  1. Ensure you have scraped data (tiktok_video_data.xlsx should exist)
  2. Run streamlit run frontend.py
  3. Access the dashboard at http://localhost:8501

Features in Detail

Data Collection

  • Video metadata (likes, comments, shares)
  • Upload dates and descriptions
  • Music information
  • Engagement metrics

Analysis Capabilities

  • Hashtag popularity tracking
  • Engagement rate calculations
  • Content trend identification
  • AI-powered insights
  • Competition analysis

Security Notes

  • Never share your TikTok cookies
  • Store API keys securely in .env file
  • Use the tool responsibly and in accordance with TikTok's terms of service

Contributing

Feel free to submit issues and enhancement requests!

Credits

Created by DankoOfficial (https://github.com/DankoOfficial) & alexx

License

This project is licensed under the MIT License - see the LICENSE file for details