TV Data Show FastAPI Project

This project is a FastAPI application for managing and analyzing TV show data. The application supports uploading data, analyzing it, clustering, classification, and more.

Project Structure

tv_data_show/
├── controllers/
│   ├── v1/
│   │   ├── init.py
│   │   ├── analyze.py
│   │   ├── query.py
│   │   ├── upload.py
│   ├── init.py
├── core/
│   ├── init.py
│   ├── config.py
│   ├── security.py
├── db/
│   ├── init.py
│   ├── base.py
│   ├── models.py
│   ├── schemas.py
│   ├── session.py
├── services/
│   ├── init.py
│   ├── analyze.py
│   ├── upload.py
│   ├── query.py
├── globals.py
├── main.py
├── generate_api_key.py
├── interview_data.db
├── test.db
├── requirements.txt
├── Dockerfile
├── .env
└── tests/
    ├── init.py
    ├── conftest.py
    ├── test_main.py
    ├── test_analyze.py
    ├── test_query.py
    ├── test_upload.py

Setup and Installation

Prerequisites

• Docker
• Python 3.11
• pip

Environment Variables

Create a .env file in the root of your project with the following content:

API_KEY=your_api_key_here

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/tv_data_show.git
   cd tv_data_show

2. Install dependencies:

   pip install -r requirements.txt

3. Generate an API key:

   You can generate an API key manually and place it in the .env file or use the provided script:

   python generate_api_key.py

   This will create an api_key.txt file. Move the API key from api_key.txt to your .env file.

Running the Application

Using Docker

1. Build the Docker image:

   docker build -t tv_data_show:latest .

2. Run the Docker container:

   docker run -p 8000:8000 --env-file .env tv_data_show:latest

Using Python

1. Run the application:

   uvicorn main:app --reload

Project Functionality

Uploading Data

Endpoint: /api/v1/uploadfile/

• Method: POST

• Description: Upload a CSV file containing TV show data.

• Example:

  curl -X POST "http://127.0.0.1:8000/api/v1/uploadfile/" -F "file=@path_to_your_file.csv" -H "Authorization: your_api_key"

Analyzing Data

Endpoint: /api/v1/analyze/

• Method: POST

• Description: Analyze the uploaded data.

• Example:

  curl -X POST "http://127.0.0.1:8000/api/v1/analyze/" -H "Authorization: your_api_key"

Fetching Results

Endpoint: /api/v1/results/

• Method: GET

• Description: Fetch the analysis results.

• Example:

  curl -X GET "http://127.0.0.1:8000/api/v1/results/" -H "Authorization: your_api_key"

Computing Similarity

Endpoint: /api/v1/similarity/

• Method: GET

• Description: Compute the similarity of a given video ID with other videos.

• Example:

  curl -X GET "http://127.0.0.1:8000/api/v1/similarity/?video_id=YourVideoID" -H "Authorization: your_api_key"

Clustering Videos

Endpoint: /api/v1/cluster/

• Method: GET

• Description: Cluster the videos into a specified number of clusters.

• Example:

  curl -X GET "http://127.0.0.1:8000/api/v1/cluster/?n_clusters=5" -H "Authorization: your_api_key"

Classifying Videos

Endpoint: /api/v1/classify/

• Method: GET

• Description: Classify the videos based on their features.

• Example:

  curl -X GET "http://127.0.0.1:8000/api/v1/classify/" -H "Authorization: your_api_key"

Testing

Running Tests

1. Install test dependencies:

   pip install pytest httpx python-dotenv

2. Run the tests:

   pytest

Test Files

• tests/conftest.py: Sets up environment variables for tests.
• tests/test_main.py: Tests the main endpoints.
• tests/test_analyze.py: Tests the analyze functionality.
• tests/test_query.py: Tests the query functionality.
• tests/test_upload.py: Tests the upload functionality.