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NLP Insight Engine — REST API
FastAPI backend exposing the NLP pipeline as a JSON API.
Run: uvicorn api.main:app --reload
"""
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from utils.pipeline import NLPPipeline
app = FastAPI(
title="NLP Insight Engine API",
description=(
"A free, open-source NLP analysis API. "
"Supports sentiment analysis, named entity recognition, "
"keyword extraction, and extractive summarisation."
),
version="1.0.0",
docs_url="/api/docs",
)
# Lazy-load pipeline on first request
_pipeline = None
def get_pipeline() -> NLPPipeline:
global _pipeline
if _pipeline is None:
_pipeline = NLPPipeline()
return _pipeline
# ── Request / Response schemas ───────────────────────────────────────────────
class AnalyseRequest(BaseModel):
text: str = Field(..., min_length=20, max_length=5000, description="Text to analyse")
sentiment: bool = Field(True, description="Run sentiment analysis")
ner: bool = Field(True, description="Run named entity recognition")
keywords: bool = Field(True, description="Run keyword extraction")
summary: bool = Field(True, description="Run extractive summarisation")
class AnalyseResponse(BaseModel):
word_count: int
sentiment: dict | None = None
entities: list | None = None
keywords: list | None = None
summary: str | None = None
# ── Endpoints ────────────────────────────────────────────────────────────────
@app.get("/")
def root():
return {
"service": "NLP Insight Engine",
"version": "1.0.0",
"docs": "/api/docs",
}
@app.get("/health")
def health():
return {"status": "ok"}
@app.post("/api/analyse", response_model=AnalyseResponse)
def analyse(req: AnalyseRequest):
"""Run NLP analysis on the provided text."""
pipe = get_pipeline()
text = req.text.strip()
result = AnalyseResponse(word_count=len(text.split()))
try:
if req.sentiment:
result.sentiment = pipe.analyse_sentiment(text)
if req.ner:
result.entities = pipe.extract_entities(text)
if req.keywords:
result.keywords = pipe.extract_keywords(text)
if req.summary:
result.summary = pipe.summarise(text)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
return result
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