Metadata-Version: 2.4
Name: scholr
Version: 0.2.0
Summary: A lightweight scraper for Google Scholar
Home-page: https://github.com/An4s0/Scholr
Author: Anas AlMutary
Author-email: me@ianas.me
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: beautifulsoup4
Requires-Dist: requests
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Scholr

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[![License](https://img.shields.io/github/license/An4s0/scholr.svg)](https://github.com/An4s0/scholr/blob/main/LICENSE)
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[![Issues](https://img.shields.io/github/issues/An4s0/scholr.svg)](https://github.com/An4s0/scholr/issues)


---

## Overview

**Scholr** is a lightweight, developer-friendly Python library for extracting structured academic data from Google Scholar.  
It provides an easy way to retrieve:  

- Author profile information (name, affiliation, research interests, photo)  
- Citation statistics (total citations, h-index, i10-index)  
- Complete publication lists  
- Publication details (title, date, citations, link)

Scholr is designed for simplicity, speed, and ease of integration into scripts or pipelines.  

---

## Installation

```bash
pip install scholr
```

---

## Usage

### Fetch an author profile

```python
from scholr import get_scholar_profile

user_id = "EXAMPLE_ID"

profile = get_scholar_profile(user_id)

print("Profile info:")
print(profile)
```

Example output:

```json
{
  "name": "John Doe",
  "affiliation": "Department of Computer Science, Example University",
  "interests": ["machine learning", "data mining"],
  "photo_url": "https://scholar.googleusercontent.com/photos/example.jpg",
  "citations_all": "1234",
  "citations_since_2019": "987",
  "h_index_all": "15",
  "h_index_since_2019": "12",
  "i10_index_all": "10",
  "i10_index_since_2019": "8",
  "publications": [
    {"title": "A Study on Machine Learning", "url": "https://scholar.google.com/..." },
    {"title": "Data Mining Techniques", "url": "https://scholar.google.com/..."}
  ]
}
```

---

### Fetch publication details

```python
from scholr import get_publication_details

publication_url = "https://scholar.google.com/citations?view_op=view_citation&user=EXAMPLE_ID&citation_for_view=EXAMPLE_CITATION"

publication = get_publication_details(publication_url)

print("Publication details:")
print(publication)
```

Example output:

```json
{
  "title": "A Study on Machine Learning",
  "url": "https://scholar.google.com/...",
  "date": "2020/5/15",
  "citations": "45"
}
```

---

## Example Test Script

```python
from scholr import get_scholar_profile, get_publication_details

profile_url = "https://scholar.google.com/citations?user=EXAMPLE_ID"

profile = get_scholar_profile(profile_url)
print("Profile info:", profile)

for pub in profile.get("publications", []):
    publication = get_publication_details(pub.get("url"))
    print("Publication details:", publication)
```

---

## Notes

- Scholr fetches **all publications** of a profile automatically using pagination.
- Rate-limiting may occur if too many requests are sent in a short time.
- This library uses only **BeautifulSoup**; no browser automation is required.
