# PeerSight - AI Academic Paper Reviewer

**Version:** 0.1.1

PeerSight is an AI-powered command-line tool designed to automate and enhance the academic paper review process. It acts like a virtual journal reviewer, leveraging local Large Language Models (LLMs) via Ollama to provide structured peer reviews of research papers supplied as plain text files.

The goal is to simulate the *output* of an expert reviewer – clean, concise, and decision-oriented – without exposing the underlying step-by-step reasoning process of the LLM.

## ✨ Features

*   📄 Accepts academic papers as plain text files (`.txt`).
*   🧠 Uses a locally hosted LLM via **Ollama** for review generation.
*   ⚙️ Configurable LLM model, API endpoint, and generation parameters (temperature, top_k, top_p) via `.env` file and CLI flags.
*   📝 Generates structured reviews including:
    *   `## Summary`
    *   `## Strengths` (considering novelty, significance, methodology, clarity, evidence)
    *   `## Weaknesses / Areas for Improvement` (considering same criteria)
    *   `## Recommendation` (Accept, Minor Revision, Major Revision, Reject)
*   🧹 Post-processes LLM output to remove extraneous thinking steps or conversational filler.
*   💾 Outputs review in human-readable **Text** format (default) or machine-readable **JSON** format (`--json` flag).
*    M️ Warns if input paper exceeds a configurable length threshold (`MAX_PAPER_LENGTH_WARN_THRESHOLD`).
*   💻 Simple and clean Command-Line Interface (CLI).
*   📦 Installable Python package via `pip`.
*   ✅ Includes unit tests and code quality checks (`pytest`, `black`, `ruff`, `pre-commit`).
*   🔬 **Experimental:** Attempts to extract references from the paper for future analysis (`--check-references` flag).

## ⚙️ Configuration

PeerSight uses a `.env` file in the project root directory for base configuration. Create this file if it doesn't exist (it's ignored by git).

**`.env` File Example:**

```dotenv
# --- REQUIRED ---
# Specify the Ollama model to use by default
# Make sure Ollama is serving this model!
OLLAMA_MODEL="llama3:latest" # Or deepseek-coder:latest, mistral:latest, etc.

# --- OPTIONAL ---
# Override the default Ollama API endpoint if needed
# OLLAMA_API_URL="http://custom-host:11434/api/generate"

# Override default generation parameters
# OLLAMA_TEMPERATURE=0.5  # Default: 0.7 (Lower = more deterministic)
# OLLAMA_TOP_K=40         # Default: -1 (disabled) (Consider only top K tokens)
# OLLAMA_TOP_P=0.9        # Default: -1.0 (disabled) (Consider tokens cumulative prob > P)

# Warning threshold for long papers (characters)
# MAX_PAPER_LENGTH_WARN_THRESHOLD=20000 # Default: 15000
```

**Command-Line Overrides:**

You can override the model, API URL, temperature, top-k, and top-p for a single run using CLI flags:

```bash
--model "model-name:tag"
# e.g., --model "llama3:8b"
# e.g., --model "deepseek-coder:latest"
```

```bash
--api-url "http://host:port/api/endpoint"
# e.g., --api-url "http://localhost:11434/api/generate"
```

```bash
-t TEMPERATURE or --temperature TEMPERATURE (e.g., -t 0.5)
# e.g., --temperature 0.3
# e.g., -t 0.7
```

```bash
--top-k K (e.g., --top-k 40)
# e.g., --top-k 20
# e.g., --top-k 0
```

```bash
--top-p P (e.g., --top-p 0.9)
# e.g., --top-p 0.95
```

CLI flags take precedence over .env settings.

## 🚀 Installation

Ensure you have Python >= 3.9 and pip installed. Ollama must also be installed and running separately (ollama.com).

- Clone the repository:
```bash
git clone https://github.com/RAHAMNIabdelkaderseifelislem/PeerSight
cd PeerSight
```

- Create and activate a virtual environment:

```bash
python -m venv venv
# Windows
.\venv\Scripts\activate
# macOS/Linux
# source venv/bin/activate
```

- Install the package:

    - For regular use:

    ```bash
    pip install .
    ```

    - For development (includes testing and quality tools):

    ```bash
    pip install -e .[dev]
    ```

- Set up .env: Copy the example above into a .env file in the project root and set your desired OLLAMA_MODEL.

* (Crucial) Ensure Ollama is running and the model specified in your config (OLLAMA_MODEL) is downloaded and available (e.g., ollama run llama3).

## 🛠️ Usage

Once installed, use the peersight command.

Basic:

* Review paper, print Text output to console
```bash
peersight path/to/your/paper.txt
```

* Get help/see all options
```bash
peersight --help
```

* Check version
```bash
peersight --version
```

**Reference Handling (Experimental):**

```bash
# Attempt to extract references (results currently logged)
peersight --check-references path/to/your/paper.txt

# Combine with other flags
peersight -v --check-references --json path/to/paper.pdf -o review_with_refs.json**Reference Handling (Experimental):**

```bash
# Attempt to extract references (results currently logged)
peersight --check-references path/to/your/paper.txt

# Combine with other flags
peersight -v --check-references --json path/to/paper.pdf -o review_with_refs.json
```

### Output Control:

* Save Text review to a file
```bash
peersight path/to/paper.txt -o output/review.md
```

* Output JSON to console
```bash
peersight --json path/to/paper.txt
```

* Save JSON review to a file
```bash
peersight --json path/to/paper.txt -o output/review.json
```

### LLM Control:

* Use a different model for this run
```bash
peersight --model "mistral:latest" path/to/paper.txt
```

* Adjust temperature for less randomness
```bash
peersight -t 0.3 path/to/paper.txt
```

* Use nucleus sampling
```bash
peersight --top-p 0.95 path/to/paper.txt
```

### Combine options
* Use a different model, save JSON output to a file, and set temperature
```bash
peersight --model "llama3:8b" -t 0.5 --json path/to/paper.txt -o reviews/paper1_llama3.json
```

### Verbose Logging:

* See DEBUG level logs (useful for troubleshooting)
```bash
peersight -v path/to/paper.txt
```

## 📝 Output Formats

* Text (Default): Human-readable Markdown-like text with standard headers (## Summary, etc.). Suitable for quick reading or pasting into documents.

* JSON (--json): Structured JSON object with keys summary, strengths, weaknesses, and recommendation. Useful for programmatic access or integration with other tools.
```json
{
    "summary": "The paper discusses...",
    "strengths": "- Strength 1...\n- Strength 2...",
    "weaknesses": "- Weakness 1...\n- Weakness 2...",
    "recommendation": "Minor Revision"
}
```

## ⚠️ Limitations

* **Input Length:** Very long papers may exceed the LLM's context window or Ollama's processing limits, potentially leading to poor results or errors (a warning is issued). Chunking is not yet implemented.

* **LLM Consistency:** The quality and adherence to instructions depend heavily on the specific LLM used (model, size) and its inherent variability. Results may differ between runs, especially with higher temperatures.

* **Factuality/Citation Check:** PeerSight does not verify the factual accuracy of the paper's claims or check citations. It simulates the review based on the provided text only.

* **Deep Understanding:** As an AI simulation, it lacks the deep domain expertise and nuanced understanding of a human expert in a specific field.

* **No Feedback Loop:** Does not currently support incorporating reviewer comments back into revisions.

*   **Reference Extraction:** The current reference extraction (`--check-references`) is highly experimental and uses basic heuristics. It will struggle with diverse or poorly formatted reference lists in plain text or PDFs. Accuracy is not guaranteed. Full reference validation and analysis are future work.

## 🔧 Development

* Install dev dependencies:
```bash
pip install -e .[dev]
```

* Install pre-commit hooks:
```bash
python -m pre_commit install
```

* Run tests:
```bash
pytest
```

* Run linters/formatters:
```bash
ruff check --fix . and black .
```
