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---
title: Python Dependency Compatibility Board
emoji: 🐍
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: mit
---
# 🐍 Python Dependency Compatibility Board
A powerful tool to analyze and resolve Python package dependencies. Check for version conflicts, compatibility issues, and generate clean `requirements.txt` files.
## ✨ Features
- **Multiple Input Methods**: Library list, requirements.txt paste, or file upload
- **Conflict Detection**: Automatically detects version conflicts and compatibility issues
- **🤖 AI-Powered Explanations**: Uses LLM to generate intelligent, natural language explanations for conflicts (with fallback to rule-based)
- **Dependency Resolution**: Uses pip's resolver to find compatible versions
- **Environment Aware**: Configure Python version, device (CPU/GPU), and OS
- **Analysis Modes**: Quick (top-level) or Deep (with transitive dependencies)
- **Resolution Strategies**: Latest compatible, stable/pinned, keep existing, or minimal changes
- **Spell Checking**: Auto-corrects common spelling mistakes in package names
- **Validation Utilities**: Benchmark against the bundled synthetic dataset and generate perturbed requirements for stress testing
## 🚀 How to Use
### Input Your Dependencies
You can provide dependencies in three ways:
1. **Library List**: Enter package names one per line
```
pandas
torch
langchain
fastapi
```
2. **Requirements Text**: Paste your existing requirements.txt
```
pandas==2.0.3
torch>=2.0.0
langchain==0.1.0
```
3. **File Upload**: Upload a requirements.txt file directly
### Configure Environment
- **Python Version**: Select your target Python version (3.8-3.12)
- **Device**: CPU only, NVIDIA GPU (CUDA), Apple Silicon (MPS), or Custom
- **Operating System**: Any, Linux, Windows, or macOS
### Analysis & Resolution
1. Choose **Analysis Mode**:
- **Quick**: Fast analysis of top-level dependencies
- **Deep**: Complete dependency tree with transitive dependencies
2. Select **Resolution Strategy**:
- **latest_compatible**: Resolve to latest compatible versions
- **stable/pinned**: Prefer stable, pinned versions
- **keep_existing_pins**: Preserve your existing version pins
- **minimal_changes**: Make minimal changes to resolve conflicts
3. Click **"Analyze & Resolve Dependencies"**
4. Review the results and download your resolved `requirements.txt`
## 🔍 What It Detects
The tool automatically detects:
- **Duplicate Packages**: Same package specified multiple times with conflicting versions
- **PyTorch Compatibility**: Ensures pytorch-lightning>=2.0 works with torch>=2.0
- **FastAPI/Pydantic**: Checks version compatibility (e.g., fastapi 0.78.x requires pydantic v1)
- **TensorFlow/Keras**: Validates TensorFlow/Keras version pairs
- **Version Conflicts**: Identifies incompatible version specifications
## 🤖 AI Explanations
When enabled, the tool uses LLM reasoning to provide:
- **Clear Explanations**: Natural language descriptions of what the conflict is
- **Why It Happens**: Technical reasons behind the conflict
- **How to Fix**: Actionable solutions with specific version recommendations
The LLM explanations use Hugging Face Inference API (free tier) and automatically fall back to rule-based explanations if the API is unavailable.
## 📋 Example
**Input:**
```
torch==1.8.0
pytorch-lightning==2.2.0
pandas==2.0.3
```
**Output:**
```
⚠️ Compatibility Issues Found:
- pytorch-lightning>=2.0 requires torch>=2.0, but torch<2.0 is specified
Resolved requirements.txt:
torch==2.1.0
pytorch-lightning==2.2.0
pandas==2.0.3
...
```
## 🛠️ Technical Details
- Built with [Gradio](https://gradio.app/)
- Uses `packaging` library for version parsing
- Leverages pip's dependency resolver
- Supports PEP 508 requirement specifications
## 📝 Notes
- Full dependency resolution requires pip >= 22.2
- Deep mode may take longer for large dependency sets
- The tool works best with packages available on PyPI
- Platform-specific dependencies (e.g., CUDA) are detected but resolution may vary
- Run `python validation_tools.py` to benchmark the built-in compatibility checks against synthetic cases.
- Use `python scripts/perturb_requirements.py --help` to generate noisy/invalid requirements for robustness testing.
## 🤝 Contributing
Feel free to test the tool and report any issues! This tool is designed to help developers manage Python dependencies more effectively.
## 📄 License
MIT License - feel free to use and modify as needed.
---
**Made with ❤️ for the Python community**
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