ProductHunt API

ProductHunt API is a Python-based library developed by Unrealos (unrealos.com) to automate the process of fetching, processing, and saving Product Hunt data into structured JSON format. It’s designed for developers and businesses to streamline data collection from Product Hunt categories.

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Key Features

• Automated Data Retrieval: Fetch products from any Product Hunt category using GraphQL.
• Data Parsing: Process product details and save them as structured JSON files.
• Developer-Friendly: Easy-to-use pipeline designed for automation.
• JSON Output: Save extracted product data in a standardized JSON file for analysis and reporting.

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Installation

Install from PyPI

You can install the library directly from PyPI:

pip install producthunt-api

Verify Installation

To confirm the installation was successful, run:

python -m producthunt_api --help

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Setting Up

Before running the pipeline, you need to extract some data from Product Hunt.

1. Parsing Category Data

To fetch data from a specific category, follow these steps:

1. Open the Product Hunt category page you want to scrape (e.g., AI Software).
2. Open your browser's developer tools (usually accessible via F12 or right-click > Inspect).
3. Go to the Network tab.
4. Locate the request to https://www.producthunt.com/frontend/graphql. This request contains:
   • OperationName: "CategoryPageQuery"
5. Extract the following from the request:
   • Cookie: Copy the Cookie string from the request headers.
   • SHA256 Hash: Copy the sha256Hash value from the request payload.

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Usage

Pipeline Workflow

The pipeline automates the following steps:

1. Data Fetching: Fetch products from a specified Product Hunt category.
2. Product Processing: Parse and save product details as individual JSON files.
3. Data Consolidation: Combine all individual JSON files into a single structured JSON file.

Running the Pipeline

Create a Python script and use the following example to run the pipeline:

import os
from producthunt_api import ProductHuntPipeline

# Define necessary variables
directory = os.path.join(os.getcwd(), "downloads")
cookie_value = "<YOUR_COOKIE>"
sha256_hash_value = "<YOUR_SHA256_HASH>"
slug_value = "ai-software"  # Replace with your desired category slug

# Initialize and run the pipeline
pipeline = ProductHuntPipeline(
    directory=directory,
    cookie=cookie_value,
    sha256_hash=sha256_hash_value,
    slug=slug_value
)
pipeline.run(limit=50, max_threads=10)

1. Replace <YOUR_COOKIE> and <YOUR_SHA256_HASH> with the values you extracted earlier.
2. Save the script and run it:

   python your_script_name.py
   

Output

• Individual JSON Files: Each product is saved as a separate JSON file in the downloads/products directory.
• Consolidated JSON File: All product data is combined into a single products.json file in the downloads directory.

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Testing the Library

To ensure everything is working as expected, you can run the test suite.

1. Install testing dependencies:

   pip install pytest pytest-cov
   

2. Run the tests:

   pytest -v
   

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Contributing

We welcome contributions to enhance this library. If you’d like to contribute:

1. Fork the repository on GitHub.
2. Create a feature branch.
3. Submit a pull request with your changes.

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About Unrealos

Unrealos (unrealos.com) is a software development company specializing in AI, SaaS, and PaaS solutions for businesses. With expertise in integrating artificial intelligence into scalable business processes, Unrealos delivers cutting-edge software tailored to your needs.

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Additional Resources

• Source Code: GitHub Repository
• Issue Tracker: Submit Issues Here