Updated readme.md

README.md

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 app_file: app/app.py
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+
+# SQL Chat Assistant
+
+## Overview
+
+This project is a Flask-based chat assistant that converts natural language queries into SQL statements using state-of-the-art NLP models. The system leverages Hugging Face transformer models, sentence embedding techniques, and fine-tuning approaches to generate accurate SQL queries for an SQLite database.
+
+The primary goal of this project is to enable users to interact with structured data using conversational language, making database queries accessible to non-technical users.
+
+## Approach
+
+### 1. Pretrained Transformer Models (Hugging Face)
+
+Initially, multiple Hugging Face models were tested to generate SQL queries from natural language inputs. However, most of them produced inconsistent results due to their general training data.
+
+### 2. Sentence Transformers + Cosine Similarity + Parameter Extraction
+
+To improve query generation, I experimented with an approach that captures the semantic meaning of user queries and maps them to predefined SQL templates using:
+
+- **Sentence embeddings**: Extracting vector representations of queries.
+- **Cosine similarity**: Matching user queries with predefined SQL structures.
+- **Regular expression templates**: Extracting SQL parameters dynamically to refine query formation.
+
+### 3. Fine-Tuning T5-Small with ONNX Quantization
+
+To enhance accuracy, I fine-tuned the t5-small model using a custom dataset based on the structure of my SQLite database.
+
+ONNX quantization was applied to reduce the model size and improve deployment efficiency while staying within hosting constraints.
+
+## Installation Guide
+
+### 1. Clone the Repository
+
+```sh
+git clone https://github.com/DevashishXO/SQLite-Chat-Assistant.git
+cd SQLite-Chat-Assistant
+
+### 2. Install Dependencies
+
+```sh
+pip install -r requirements.txt
+
+### 3. Set up the SQLite Database
+
+```sh
+python data/initialize_db.py
+
+### 4. Run the Flask App
+
+```sh
+$env:FLASK_APP="app.main:app"
+flask run
+
+