Terraform BDD Scenario Generator API

This Flask API generates Behavior-Driven Development (BDD) scenarios in Gherkin syntax for cloud infrastructure security. The API leverages Gemini 1.5 Flash for natural language generation and response.

Features

• Generates Gherkin-based BDD scenarios for Terraform configurations.
• Accepts cloud provider and service as inputs.
• Uses a pre-persisted vector index and Gemini 1.5 Flash LLM for responses.

Requirements

• Python 3.8+
• A GOOGLE_API_KEY environment variable with access to Gemini 1.5 Flash.
• Pre-built index stored in the ./storage directory.

Setup

Prerequisites

Ensure you have the following installed:

• Python
• Pip
• Flask
• Other Python dependencies listed in the requirements.txt.

Installation

1. Clone the Repository:

   git clone https://github.com/EnricoEng/hackaton.git
   cd hackaton

2. Install Dependencies:

   pip install -r requirements.txt

3. Set the Environment Variable: Create a .env file in the root directory:

   echo "GOOGLE_API_KEY=your_google_api_key" > .env

   Replace your_google_api_key with your valid API key.

4. Ensure the Persisted Index: Place the pre-built index files in the ./storage directory.

Pre-Built Index

This API relies on a vector index created using llama-index. Make sure your ./storage directory contains the persisted index files (docstore.json, index_store.json, etc.). Use StorageContainer for managing these files.

Usage

Running the API

Start the Flask app:

python app.py



The API will run locally at http://127.0.0.1:5000.

Endpoint
POST /generate_scenario
Generate BDD scenarios for a specified cloud provider and service.

  Request:
  JSON  
    {
          "provider": "GCP",
          "service": "Storage Bucket"
    }


  Response:
  Returns a string containing the Gherkin-based BDD scenario.

  Example:
  JSON
    Scenario: Mitigating public access vulnerability in Storage Bucket
            Given a configuration for Storage Bucket on GCP
            When permissions are set to allow public access
            Then restrict bucket access to authorized users only to prevent unauthorized access


Testing with curl
curl -X POST http://127.0.0.1:5000/generate_scenario -H "Content-Type: application/json" -d '{"provider": "AWS", "service": "S3 Bucket"}'


Project Structure
├── app.py                 # Flask API implementation
├── storage/               # Persisted index files (pre-built)
├── requirements.txt       # Python dependencies
├── README.md              # Project documentation
└── .env                   # Environment variables

Dependencies
  Flask
  llama-index
  huggingface/transformers
  dotenv

Environment Variables
  GOOGLE_API_KEY: Required for Gemini 1.5 Flash API access.


Troubleshooting
  Missing API Key:
    Ensure the .env file contains the correct GOOGLE_API_KEY.

  Index Not Found:
    Verify the ./storage directory contains the persisted index files.

  Flask Errors:
    Ensure all dependencies are installed.
    Check for typos in the request payload.

License
  This project is licensed under the MIT License.
  You can further customize this README file to fit additional needs or specific details about your project.