🔍 Search Autocomplete & Query Prediction Engine

This project implements a real-time search autocomplete system that predicts and ranks multi-word query suggestions as a user types.
The primary focus is on backend system design, NLP-based sequence modeling, and production-style integration, rather than isolated model experimentation.

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🚀 Features

• Real-time search query autocomplete
• Top-K ranked suggestions with confidence scores
• NLP-based sequence modeling (LSTM / Transformer concepts)
• REST API–driven backend for low-latency inference
• Explainability & visualization (token highlighting, prediction flow)
• Clean separation of training, inference, and frontend layers

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🔄 System Workflow

User Query
   ↓
Text Tokenization
   ↓
Sequence Modeling (LSTM / Transformer-based Language Model)
   ↓
Top-K Next-Token Prediction
   ↓
Confidence Scoring & Ranking
   ↓
Autocomplete Suggestions (API Response)

The model predicts the next most probable tokens, which are combined to form meaningful query completions similar to modern search engine autocomplete systems.

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🛠️ Tech Stack

Backend & Machine Learning

• Python
• Hugging Face Transformers
• PyTorch
• NLP Tokenization (Byte Pair Encoding - BPE)
• Sequence Modeling (LSTM / Transformer concepts)

Backend Services

• Flask (REST APIs)
• JSON-based inference responses

Frontend

• HTML, CSS, JavaScript
• Live autocomplete UI
• Visualization of prediction flow and confidence scores

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📂 Project Structure

nlp_project/
│
├── model/
│   ├── train.py          # Model training pipeline
│   ├── inference.py      # Autocomplete inference logic
│   ├── final_model/      # Saved trained model & tokenizer
│   └── data/
│       └── queries.txt   # Search-style training corpus
│
├── static/
│   ├── style.css
│   └── script.js
│
├── templates/
│   └── index.html
│
├── app.py                # Flask application
├── metrics.json          # Training metrics (loss curve)
└── README.md

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🧪 Training Details

• Dataset: Search-style query corpus (queries.txt)
• Model Type: Causal Language Model
• Training Objective: Next-token prediction
• Evaluation: Training loss tracking
• Output: Trained model and tokenizer saved locally

The training pipeline is modular and can be extended to larger datasets or alternative language models.

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📊 Explainability & Visualization

To improve transparency and interpretability, the system includes:

• Token highlighting to show influential query tokens
• Prediction flow visualization explaining how suggestions are generated
• Confidence-based ranking for each autocomplete suggestion

These features help bridge the gap between machine learning predictions and real-world system behavior.

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▶️ How to Run

1️⃣ Train the Model

python model/train.py

2️⃣ Start the Backend Server

python app.py

3️⃣ Open in Browser

http://127.0.0.1:5000

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🎯 Project Goals

• Demonstrate how NLP models integrate into backend systems
• Build a search-style product, not just a standalone ML model
• Emphasize system design, APIs, and scalability
• Showcase explainable AI in a user-facing application

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