GigAnalyzer

GigAnalyzer allows users to search for gigs on the Fiverr platform based on a specified keyword. It utilizes web scraping techniques to extract gig details such as title, number of orders, price, and tags from the search results.

Preview

Live

Requirements

Install Python on Your Device

Environment Variables

GOOGLE_API_KEY = AIabcdefghijklmnopqrstuvwxyz

How to run

# Clone the repository
git clone https://github.com/makmodol1173/GigAnalyzer.git
cd GigAnalyzer

# Install required module
pip install -r requirements.txt

# Run the app
python main.py

Features

Keyword Analysis

• Frequency Analysis: Most commonly used keywords
• Correlation Detection: Keywords that appear together
• Unique vs Duplicate: Distribution of keyword usage
• Success Keywords: Tags associated with high-performing gigs

Price Analysis

• Distribution Charts: Visual representation of pricing patterns
• Price vs Performance: Correlation between pricing and completed orders

Success Metrics

• Performance Indicators: High-order, high-rating gigs
• Success Rate Calculation: Percentage of successful gigs
• Category Performance: Success rates by service category
• Common Success Factors: Patterns in successful gigs

Export Options

• Excel: All the fetch gig details with title, description, price, tags attribute.
• Text: Comma seperated unique keyword.

AI Recommendation

• Generate a SEO optimized title, description and tags.

Customization

The application currently uses mock data. To integrate real scraping:

Modify data_fetcher.py:

• Implement API integrations (Fiverr, Upwork, etc.)
• Add web scraping capabilities
• Handle rate limiting and authentication

Contribution Ideas

1. Implement web scrapping resolving policy issue
2. Include seller level, rating, resonse time, delivary time etc in gig details
3. Download ai recommended description as .txt file