Sentiment-Based Customer Review Response System

Overview

This project is a Flask-based web application that processes customer reviews, determines their sentiment, and generates appropriate responses using an AI model powered by LangChain and Groq's Llama3.

Features

• Accepts customer reviews as input.
• Analyzes the sentiment of the review (positive, negative, or neutral).
• Generates an appropriate response based on sentiment.
• Uses LangChain's structured output capabilities.
• Implements a state graph with LangGraph for workflow automation.
• Flask web interface to submit reviews and display AI-generated responses.

Technologies Used

• Python: Core programming language
• Flask: Web framework for handling requests and rendering templates
• LangChain: AI framework for LLM-based applications
• LangGraph: Workflow state management for AI processing
• Groq: Model provider for Llama3
• HTML: Frontend for displaying responses

Installation and Setup

Prerequisites

Ensure you have Python installed (recommended version: 3.8+). Install required dependencies using:

pip install flask langchain-core langchain chat_models langgraph

Set Environment Variables

Set up the Groq API key before running the application:

export GROQ_API_KEY='your_groq_api_key'

(Replace your_groq_api_key with your actual API key.)

Running the Application

1. Clone this repository:

git clone <repository_url>
cd <project_directory>

2. Run the Flask application:

python app.py

3. Open your browser and navigate to:

http://127.0.0.1:5000/

Project Structure

|-- app.py               # Main Flask application and AI workflow
|-- templates/
    |-- home.html       # Review submission page
    |-- response.html   # Display AI-generated response

How It Works

1. User submits a review via the home page.
2. Sentiment analysis determines if the review is positive, negative, or neutral.
3. Response generation selects an appropriate AI response template.
4. Response is displayed on the response page.

Future Enhancements

• Improve sentiment classification with fine-tuned models.
• Store reviews and responses in a database.
• Add a frontend using ReactJS for a better user experience.