AI Model Inference with FastAPI

This project demonstrates how to set up a FastAPI endpoint for AI model inference, specifically using a pre-trained sentiment analysis model from the Hugging Face Transformers library.

Features

• FastAPI Integration: Easily create API endpoints using FastAPI.
• Local Model Loading: Load and use pre-trained models stored locally on your machine.
• Sentiment Analysis: Analyze text sentiment using state-of-the-art transformer models.
• Extensible and Modular: Designed to be easily extendable for other types of models and tasks.

Project Structure

• app/: Contains the FastAPI application code.
  • main.py: Entry point for the FastAPI application.
  • models/: Directory for model-related code.
    • sentiment_model.py: Contains the sentiment analysis model class.
  • routers/: Directory for route definitions.
    • inference.py: Contains the route for sentiment analysis inference.
  • schemas/: Directory for request/response schemas.
    • text_data.py: Contains the schema for text data.
• Dockerfile: Docker configuration file.
• requirements.txt: List of Python dependencies.
• .dockerignore: List of files and directories to ignore in the Docker image.
• README.md: Project documentation.

Setup

Prerequisites

• Docker
• Docker Compose (optional, for more complex setups)

Running the Application

1. Build the Docker image:

   docker build -t ai-model-inference .

2. Run the Docker container:

   docker run -d -p 8000:8000 ai-model-inference

3. Access the API documentation at http://localhost:8000/docs.

Endpoints

• POST /predict/: Predict the sentiment of a given text.

Download Models

--visit `https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english/tree/main

Example Request

curl -X POST "http://localhost:8000/predict/" -H "Content-Type: application/json" -d '{"text": "I love FastAPI!"}'