Text-Based Chatbot Performance Analysis

This project implements an end-to-end performance analysis system for text-based chatbots using Machine Learning. It features a Bidirectional LSTM model with an Attention mechanism to evaluate chatbot responses based on provided context and facts.

Features

Exploratory Data Analysis: Visual insights into engine distribution and performance correlations.
Advanced LSTM Model: Uses Bidirectional LSTM and Attention layers for high-accuracy evaluation.
Context-Aware Prediction: Evaluates responses not just on linguistics but also on factual consistency.
Modern Web Interface: Glassmorphic UI with real-time performance analytics.
Flask Backend: Robust API for model inference.

Project Structure

train_model.py: Training pipeline for the advanced model.
app.py: Flask server for real-time predictions.
explore_data.py: EDA script for dataset visualization.
BP_MHS_V1.csv: The core dataset.
templates/ & static/: Frontend assets.

How to Run

Install dependencies:
```
pip install -r requirements.txt
```
Train the model:
```
python train_model.py
```
Start the application:
```
python app.py
```
Access the UI at http://127.0.0.1:5000.

Model Insights

The system uses an Attention mechanism to focus on critical parts of the facts and responses, ensuring the expert verdict is both accurate and contextually relevant.