QUICKSTART.md

Quick Start Guide

Get up and running with the NLP Analysis API in minutes!

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation Steps

1. Clone or Navigate to Project

cd sentimant

2. Create Virtual Environment (Recommended)

Windows:

python -m venv venv
venv\Scripts\activate

Linux/Mac:

python -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

This will install:

• FastAPI (web framework)
• Uvicorn (ASGI server)
• Transformers (Hugging Face models)
• PyTorch (ML backend)
• Pydantic (data validation)

4. Start the Server

python run_server.py

Or:

python main.py

5. Verify Installation

Open your browser and visit:

• API Status: http://localhost:8000
• Interactive Docs: http://localhost:8000/docs
• Alternative Docs: http://localhost:8000/redoc
• Health Check: http://localhost:8000/health

First API Call

Using cURL

Sentiment Analysis:

curl -X POST "http://localhost:8000/analyze" \
     -H "Content-Type: application/json" \
     -d "{\"text\": \"I love this API!\"}"

Named Entity Recognition:

curl -X POST "http://localhost:8000/ner" \
     -H "Content-Type: application/json" \
     -d "{\"text\": \"Apple Inc. is located in Cupertino, California.\"}"

Translation:

curl -X POST "http://localhost:8000/translate" \
     -H "Content-Type: application/json" \
     -d "{\"text\": \"Hello world\", \"source_lang\": \"en\", \"target_lang\": \"ar\"}"

Using Python

import requests

# Sentiment Analysis
response = requests.post(
    "http://localhost:8000/analyze",
    json={"text": "I love this API!"}
)
print(response.json())

# NER
response = requests.post(
    "http://localhost:8000/ner",
    json={"text": "Apple Inc. is in Cupertino, California."}
)
print(response.json())

# Translation
response = requests.post(
    "http://localhost:8000/translate",
    json={
        "text": "Hello world",
        "source_lang": "en",
        "target_lang": "ar"
    }
)
print(response.json())

Using Interactive Docs

1. Open http://localhost:8000/docs in your browser
2. Click on any endpoint (e.g., "/analyze")
3. Click "Try it out"
4. Enter your text in the JSON body
5. Click "Execute"
6. See the response below

What's Next?

• Read the README.md for detailed API documentation
• Check ARCHITECTURE.md to understand the codebase
• Explore the lib/ directory structure
• Try different text samples
• Test batch processing

Troubleshooting

Models Not Loading

Problem: Long startup time or model loading errors

Solutions:

• Ensure stable internet connection (models download on first use)
• Free up disk space (models are ~500MB each)
• Check system RAM (models require ~2-3GB)

Port Already in Use

Problem: Address already in use error

Solutions:

# Change port in main.py or run_server.py
uvicorn main:app --port 8001

Import Errors

Problem: Module not found errors

Solutions:

• Ensure you're in the correct directory
• Activate virtual environment
• Reinstall requirements: pip install -r requirements.txt

Slow Response Times

Problem: API responses are slow

Solutions:

• First request is always slower (cold start)
• Consider using GPU if available
• Check system resources
• Optimize batch size for large datasets

Common Use Cases

Analyze Product Reviews

reviews = [
    "This product is amazing!",
    "Terrible quality, disappointed.",
    "It's okay, nothing special."
]

for review in reviews:
    response = requests.post(
        "http://localhost:8000/analyze",
        json={"text": review}
    )
    sentiment = response.json()
    print(f"Review: {review}")
    print(f"Sentiment: {sentiment['sentiment']} ({sentiment['confidence']})")

Extract Business Information

text = "Apple Inc. CEO Tim Cook announced new products at WWDC in Cupertino, California."

response = requests.post(
    "http://localhost:8000/ner",
    json={"text": text}
)

entities = response.json()
for entity in entities['entities']:
    print(f"{entity['label']}: {entity['text']} ({entity['score']})")

Batch Processing

texts = [
    "I love Python!",
    "FastAPI is great!",
    "Python is the best!"
]

response = requests.post(
    "http://localhost:8000/analyze-batch",
    json={"texts": texts}
)

results = response.json()
for result in results['results']:
    print(f"{result['text']}: {result['sentiment']}")

Tips for Best Performance

1. Use Batch Endpoints: For multiple texts, use /analyze-batch
2. Cache Results: Don't re-analyze the same text
3. Keep Server Running: Model loading is expensive
4. Monitor Memory: Close unused connections
5. Use Async: For concurrent requests

Need Help?

• Check the README.md for detailed documentation
• Review ARCHITECTURE.md for code structure
• Examine error messages in the server logs
• Use the interactive docs at /docs for API exploration

Happy analyzing! 🚀