| Name | producthunt-api JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | https://github.com/markolofsen/producthunt-api |
| Summary | Easily scrape and export detailed product data from Product Hunt categories into JSON files using a Python library. Automate data collection for analysis and reporting. |
| upload_time | 2025-01-22 05:10:19 |
| maintainer | None |
| docs_url | None |
| author | Unrealos |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# ProductHunt API
**ProductHunt API** is a Python-based library developed by **Unrealos** ([unrealos.com](https://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.
---
## 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.
---
## Installation
### Install from PyPI
You can install the library directly from PyPI:
```bash
pip install producthunt-api
```
### Verify Installation
To confirm the installation was successful, run:
```bash
python -m producthunt_api --help
```
---
## 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](https://www.producthunt.com/categories/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.
---
## 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:
```python
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:
```bash
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.
---
## Testing the Library
To ensure everything is working as expected, you can run the test suite.
1. Install testing dependencies:
```bash
pip install pytest pytest-cov
```
2. Run the tests:
```bash
pytest -v
```
---
## 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.
---
## About Unrealos
**Unrealos** ([unrealos.com](https://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.
---
## Additional Resources
- **Source Code**: [GitHub Repository](https://github.com/markolofsen/producthunt-api)
- **Issue Tracker**: [Submit Issues Here](https://github.com/markolofsen/producthunt-api/issues)
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"description": "# ProductHunt API\n\n**ProductHunt API** is a Python-based library developed by **Unrealos** ([unrealos.com](https://unrealos.com)) to automate the process of fetching, processing, and saving Product Hunt data into structured JSON format. It\u2019s designed for developers and businesses to streamline data collection from Product Hunt categories.\n\n---\n\n## Key Features\n- **Automated Data Retrieval**: Fetch products from any Product Hunt category using GraphQL.\n- **Data Parsing**: Process product details and save them as structured JSON files.\n- **Developer-Friendly**: Easy-to-use pipeline designed for automation.\n- **JSON Output**: Save extracted product data in a standardized JSON file for analysis and reporting.\n\n---\n\n## Installation\n\n### Install from PyPI\nYou can install the library directly from PyPI:\n```bash\npip install producthunt-api\n```\n\n### Verify Installation\nTo confirm the installation was successful, run:\n```bash\npython -m producthunt_api --help\n```\n\n---\n\n## Setting Up\n\nBefore running the pipeline, you need to extract some data from Product Hunt.\n\n### 1. Parsing Category Data\nTo fetch data from a specific category, follow these steps:\n\n1. Open the Product Hunt category page you want to scrape (e.g., [AI Software](https://www.producthunt.com/categories/ai-software)).\n2. Open your browser's developer tools (usually accessible via `F12` or right-click > `Inspect`).\n3. Go to the **Network** tab.\n4. Locate the request to `https://www.producthunt.com/frontend/graphql`. This request contains:\n - **OperationName**: `\"CategoryPageQuery\"`\n5. Extract the following from the request:\n - **Cookie**: Copy the `Cookie` string from the request headers.\n - **SHA256 Hash**: Copy the `sha256Hash` value from the request payload.\n\n---\n\n## Usage\n\n### Pipeline Workflow\nThe pipeline automates the following steps:\n1. **Data Fetching**: Fetch products from a specified Product Hunt category.\n2. **Product Processing**: Parse and save product details as individual JSON files.\n3. **Data Consolidation**: Combine all individual JSON files into a single structured JSON file.\n\n### Running the Pipeline\nCreate a Python script and use the following example to run the pipeline:\n\n```python\nimport os\nfrom producthunt_api import ProductHuntPipeline\n\n# Define necessary variables\ndirectory = os.path.join(os.getcwd(), \"downloads\")\ncookie_value = \"<YOUR_COOKIE>\"\nsha256_hash_value = \"<YOUR_SHA256_HASH>\"\nslug_value = \"ai-software\" # Replace with your desired category slug\n\n# Initialize and run the pipeline\npipeline = ProductHuntPipeline(\n directory=directory,\n cookie=cookie_value,\n sha256_hash=sha256_hash_value,\n slug=slug_value\n)\npipeline.run(limit=50, max_threads=10)\n```\n\n1. Replace `<YOUR_COOKIE>` and `<YOUR_SHA256_HASH>` with the values you extracted earlier.\n2. Save the script and run it:\n ```bash\n python your_script_name.py\n ```\n\n### Output\n- **Individual JSON Files**: Each product is saved as a separate JSON file in the `downloads/products` directory.\n- **Consolidated JSON File**: All product data is combined into a single `products.json` file in the `downloads` directory.\n\n---\n\n## Testing the Library\n\nTo ensure everything is working as expected, you can run the test suite.\n\n1. Install testing dependencies:\n ```bash\n pip install pytest pytest-cov\n ```\n\n2. Run the tests:\n ```bash\n pytest -v\n ```\n\n---\n\n## Contributing\n\nWe welcome contributions to enhance this library. If you\u2019d like to contribute:\n1. Fork the repository on GitHub.\n2. Create a feature branch.\n3. Submit a pull request with your changes.\n\n---\n\n## About Unrealos\n\n**Unrealos** ([unrealos.com](https://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.\n\n---\n\n## Additional Resources\n\n- **Source Code**: [GitHub Repository](https://github.com/markolofsen/producthunt-api)\n- **Issue Tracker**: [Submit Issues Here](https://github.com/markolofsen/producthunt-api/issues)\n",
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