| # 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 | |
| 1. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 2. Train the model: | |
| ```bash | |
| python train_model.py | |
| ``` | |
| 3. Start the application: | |
| ```bash | |
| python app.py | |
| ``` | |
| 4. 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. | |