Advanced NLP API

A high-performance RESTful API for Natural Language Processing tasks and psychological response generation, built with FastAPI.

🌟 Features

• Sentiment Analysis: Analyze the sentiment of input text
• Entity Recognition: Extract named entities from text
• Text Summarization: Generate concise summaries of text
• Psychological Responses: Access a database of psychologist-style responses
• Multi-language Support: Includes translation capabilities
• Filtering & Search: Filter responses by input/output text
• RESTful API: Fully documented OpenAPI/Swagger interface

🚀 Quick Start

Prerequisites

• Python 3.8+
• pip (Python package manager)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/advanced-nlp-api.git
   cd advanced-nlp-api

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Download the spaCy model:

   python -m spacy download en_core_web_sm

5. Start the server:

   uvicorn advanced_api:app --reload

The API will be available at http://localhost:8000

📚 API Documentation

Once the server is running, access the interactive API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

🔧 Endpoints

Psychological Responses

• GET / - Welcome message
• GET /responses - Get all responses with optional filtering
• GET /responses/random - Get a random response
• GET /responses/{response_id} - Get a specific response by ID
• GET /responses/happy - Get happy responses
• GET /responses/sad - Get sad responses

NLP Endpoints

• POST /analyze/sentiment - Analyze sentiment of text
• POST /analyze/entities - Extract named entities from text
• POST /summarize - Generate a summary of text
• GET /translate - Translate text to another language
• POST /qa - Get answers to questions about text

🤖 Example Usage

Using cURL

# Get a random response
curl -X 'GET' \
  'http://localhost:8000/responses/random' \
  -H 'accept: application/json'

# Analyze sentiment
curl -X 'POST' \
  'http://localhost:8000/analyze/sentiment' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{"text": "I love this API! It's amazing!"}'

Using Python

import requests

# Get a random happy response
response = requests.get("http://localhost:8000/responses/random")
print(response.json())

# Analyze text sentiment
data = {"text": "I'm feeling great today!"}
response = requests.post("http://localhost:8000/analyze/sentiment", json=data)
print(response.json())

🛠️ Development

Running Tests

# Run tests (add your test commands here)
# pytest tests/

Code Style

This project uses:

• Black for code formatting
• Flake8 for linting
• isort for import sorting

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with FastAPI
• NLP powered by spaCy
• Psychological responses dataset (custom)

📬 Contact

For questions or feedback, please open an issue on GitHub. Bhupesh Joshi https://www.linkedin.com/in/bhupesh-joshi-profile/