Add sonnet-4 vs openai o4

Author: nikhil-1e9

sonnet4-vs-o4/.env.example

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+ANTHROPIC_API_KEY=your_anthropic_api_key
+OPENAI_API_KEY=your_openai_api_key

sonnet4-vs-o4/.gitignore

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+# Python-generated files
+__pycache__/
+*.py[cod]
+build/
+dist/
+wheels/
+*.egg-info/
+*.egg
+.eggs/
+.Python
+develop-eggs/
+downloads/
+lib/
+lib64/
+parts/
+sdist/
+var/
+.installed.cfg
+
+# Virtual environments
+.venv
+venv/
+ENV/
+env/
+.env
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+.project
+.pydevproject
+.settings/
+*.sublime-workspace
+*.sublime-project
+
+# Testing and coverage
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+htmlcov/
+
+# Documentation
+docs/_build/
+site/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Logs and databases
+*.log
+*.sqlite
+*.db
+
+# Environment variables
+.env
+.env.local
+.env.*.local

sonnet4-vs-o4/.python-version

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+3.12

sonnet4-vs-o4/README.md

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+# Claude Sonnet 4 vs OpenAI o4-mini on code generation using DeepEval
+
+This application compares the code generation capabilities of Claude Sonnet 4 and OpenAI o4-mini using DeepEval metrics. The app allows users to ingest code from a GitHub repository as context and generate new code based on that context. Both models run parallely side by side giving a fair comparison of their capabilities. Finally DeepEval evaluates both models on custom code metrics and 
+provide a detailed performance comparison with neat and clean visuals.
+
+We use:
+- LiteLLM for orchestration
+- DeepEval for evaluation
+- Gitingest for ingesting code
+- Streamlit for the UI
+
+---
+## Setup and Installation
+
+Ensure you have Python 3.12 or later installed on your system.
+
+Install dependencies:
+```bash
+uv sync
+```
+
+Copy `.env.example` to `.env` and configure the following environment variables:
+```
+ANTHROPIC_API_KEY=your_anthropic_api_key_here
+OPENAI_API_KEY=your_openai_api_key_here
+```
+
+Run the Streamlit app:
+```bash
+streamlit run app.py
+```
+
+## Usage
+
+1. Enter a GitHub repository URL in the sidebar
+2. Click "Ingest Repository" to load the repository context
+3. Enter your code generation prompt in the chat
+4. View the generated code from both models side by side
+5. Click on "Evaluate Code" to evaluate code using DeepEval
+6. View the evaluation metrics comparing both models' performance
+
+## Evaluation Metrics
+
+The app evaluates generated code using three comprehensive metrics powered by DeepEval:
+
+- **Code Correctness**: Evaluates the functional correctness of the generated code
+
+- **Code Readability**: Measures how easy the code is to understand and maintain
+
+- **Best Practices**: Assesses adherence to coding standards and coding best practices
+
+Each metric is scored on a scale of 0-10, with the following general interpretation:
+- 0-2: Major issues or non-functional code
+- 3-5: Basic implementation with significant gaps
+- 6-8: Good implementation with minor issues
+- 9-10: Excellent implementation meeting all criteria
+
+The overall score is calculated as an average of these three metrics.
+
+---
+
+## 📬 Stay Updated with Our Newsletter!
+**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
+
+[![Daily Dose of Data Science Newsletter](https://github.com/patchy631/ai-engineering/blob/main/resources/join_ddods.png)](https://join.dailydoseofds.com)
+
+---
+
+## Contribution
+
+Contributions are welcome! Please fork the repository and submit a pull request with your improvements.