AI Answer Evaluator API

An API to evaluate student answers using Google's Generative AI. This project uses a FastAPI backend to process evaluation requests, interacts with a database to store questions and answers, and leverages the Google Gemini API to check if the provided answers are correct.

Features

• Bulk Evaluation: Trigger an evaluation for all answers in the database.
• Subject-Specific Evaluation: Trigger an evaluation for all answers related to a specific subject.
• Background Processing: Evaluations run as background tasks to prevent blocking the API.
• AI-Powered: Uses Google's Gemini API to determine the correctness of answers.
• Database Logging: Logs request metadata, including token usage and duration, for each evaluation.

Project Structure

.
├── app
│   ├── __init__.py
│   ├── crud.py          # Database Create, Read, Update, Delete operations
│   ├── database.py      # Database engine and session setup
│   ├── main.py          # FastAPI application and endpoints
│   ├── models.py        # SQLAlchemy database models
│   ├── schemas.py       # Pydantic data validation schemas
│   └── services.py      # Logic for interacting with the Google Gemini AI
├── .gitignore
├── readme.md
└── requirements.txt

Prerequisites

• Python 3.8+
• pip
• A running PostgreSQL database instance

Setup

1. Clone the repository

   git clone <your-repository-url>
   cd <your-repository-name>

2. Create and activate a virtual environment

   • macOS/Linux:

     python3 -m venv venv
     source venv/bin/activate

   • Windows:

     python -m venv venv
     .\venv\Scripts\activate

3. Install dependencies

   Install all the required Python packages from the requirements.txt file.

   pip install -r requirements.txt

4. Set up environment variables

   Create a file named .env in the root directory of the project. This file will store your secret keys and database connection string. The .gitignore file is already configured to ignore this file.

   Your .env file should contain the following:

   # Your connection string for the PostgreSQL database
   DATABASE_URL="postgresql://user:password@host:port/dbname"
   
   # Your API key for the Google Gemini API
   GEMINI_API_KEY="your-gemini-api-key"

   • DATABASE_URL: The application uses this to connect to your database.
   • GEMINI_API_KEY: The application needs this to send requests to the Gemini API for answer evaluation.

Running the Application

Once the setup is complete, you can run the FastAPI application using uvicorn. The --reload flag will automatically restart the server whenever you make changes to the code.

uvicorn app.main:app --reload

The API will be available at http://127.0.0.1:8000.

API Endpoints

The interactive API documentation (powered by Swagger UI) will be available at http://127.0.0.1:8000/docs.

Trigger Evaluations

• POST /evaluate/all-answers

  Triggers a background task to evaluate all answers for all questions currently in the database.

  • Response 202 Accepted:

    {
      "message": "Evaluation process started in the background. The `status` field in the `task_answers` table will be updated upon completion."
    }

• POST /evaluate/subject/{subject_id}

  Triggers a background task to evaluate all answers for questions belonging to a specific subject.

  • Path Parameter:
    • subject_id (UUID): The unique identifier for the subject.
  • Response 202 Accepted:

    {
      "message": "Evaluation process for subject {subject_id} started in the background. The `status` field in the `task_answers` table will be updated upon completion."
    }

View Evaluation Results

• GET /answers/all

  Retrieves the current evaluation status of all answers that have been processed by the AI.

  • Response 200 OK:

    [
      {
        "task_answer_id": "uuid-of-the-answer",
        "correct": true
      },
      {
        "task_answer_id": "another-uuid-of-an-answer",
        "correct": false
      }
    ]