Chapter 9 – NLP Systems for Real Applications (Django AI) | Practical AI/ML Course

Chapter 9: NLP Systems for Real Applications (Django AI)

This chapter focuses on building real-world NLP (Natural Language Processing) systems.
Here, AI learns to understand, process, analyze, and generate human language.
NLP systems power search engines, chatbots, assistants, recommendation engines, document analysis, and automation platforms.

This chapter teaches how to build production-grade NLP systems using Django and AI pipelines — not just text models, but complete language intelligence systems.

⭐ What is a Real-World NLP System?

• Live text input
• Language processing
• Semantic understanding
• AI inference
• Decision making
• System integration

⭐ NLP System Architecture

Text Input → Preprocessing → NLP Model → Analysis → Decision → Django API → System Action

⭐ NLP Pipeline Flow

Input → Cleaning → Tokenization → Vectorization → Model → Output

⭐ Text Preprocessing Example

import re

def preprocess(text):
    text = text.lower()
    text = re.sub(r'[^a-zA-Z0-9 ]', '', text)
    return text

⭐ Tokenization Example

from nltk.tokenize import word_tokenize

tokens = word_tokenize("AI is transforming the world")
print(tokens)

⭐ Simple NLP Logic Example

def sentiment_logic(score):
    if score > 0.6:
        return "Positive"
    else:
        return "Negative"

⭐ Django NLP API Example

Real-time NLP API using Django:

# views.py
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import re

@csrf_exempt
def nlp_api(request):
    if request.method == "POST":
        data = json.loads(request.body)
        text = data.get("text")

        text = text.lower()
        text = re.sub(r'[^a-z0-9 ]', '', text)

        if "good" in text or "great" in text:
            sentiment = "Positive"
        else:
            sentiment = "Negative"

        return JsonResponse({
            "text": text,
            "sentiment": sentiment
        })

⭐ URL Configuration

# urls.py
from django.urls import path
from .views import nlp_api

urlpatterns = [
    path("nlp/", nlp_api),
]

⭐ Model-Based NLP Service (Django)

# views.py
import tensorflow as tf
import numpy as np

model = tf.keras.models.load_model("nlp_model.h5")
tokenizer = tf.keras.preprocessing.text.Tokenizer()

@csrf_exempt
def ai_nlp(request):
    if request.method == "POST":
        data = json.loads(request.body)
        text = data.get("text")

        seq = tokenizer.texts_to_sequences([text])
        pad = tf.keras.preprocessing.sequence.pad_sequences(seq, maxlen=100)

        pred = model.predict(pad)[0][0]
        sentiment = "Positive" if pred > 0.5 else "Negative"

        return JsonResponse({"sentiment": sentiment})

⭐ Real-World NLP Use Cases

• Search engines
• Chatbots
• Customer support AI
• Document processing
• Resume scanners
• Legal document AI
• Quran/Hadith search systems

⭐ NLP System Design Principles

• Language normalization
• Semantic understanding
• Scalable pipelines
• Low-latency inference
• Secure APIs

⭐ Mini Practical Task

Build a Django NLP system that:

• Accepts text input
• Processes language
• Applies AI logic
• Returns NLP output

# views.py
@csrf_exempt
def text_analyzer(request):
    if request.method == "POST":
        data = json.loads(request.body)
        text = data.get("text").lower()

        words = text.split()

        if "danger" in words or "alert" in words:
            result = "Risk Text"
        else:
            result = "Normal Text"

        return JsonResponse({"analysis": result})

📌 Chapter Outcome

• Build real NLP systems
• Create NLP APIs
• Deploy text AI services
• Design language pipelines
• Integrate NLP into real applications

📌 Core Principle

Text is data.
AI turns language into intelligence.
NLP builds smart systems.