https://github.com/vidhi1290/chatbot-with-rasa-nlu-model-and-python

This project builds an intelligent chatbot using Rasa NLU for an E-Commerce business 🛍️. The chatbot can handle user queries like product information, pricing, and order management 💬. With spacy and TensorFlow pipelines 🧠 for training, and MongoDB for storing data 📦, it offers seamless, context-aware conversations
https://github.com/vidhi1290/chatbot-with-rasa-nlu-model-and-python

aichatbot artificial-intelligence chatbot jupyter-notebook matplotlib nlu nlu-chatbot pandas pymongo python rasa-chatbot rasa-nlu spacy spacy-nlp tensorflow

Last synced: 4 days ago
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This project builds an intelligent chatbot using Rasa NLU for an E-Commerce business 🛍️. The chatbot can handle user queries like product information, pricing, and order management 💬. With spacy and TensorFlow pipelines 🧠 for training, and MongoDB for storing data 📦, it offers seamless, context-aware conversations

• Host: GitHub
• URL: https://github.com/vidhi1290/chatbot-with-rasa-nlu-model-and-python
• Owner: Vidhi1290
• Created: 2024-12-22T13:08:26.000Z (4 days ago)
• Default Branch: main
• Last Pushed: 2024-12-22T13:26:14.000Z (4 days ago)
• Last Synced: 2024-12-22T14:24:05.901Z (4 days ago)
• Topics: aichatbot, artificial-intelligence, chatbot, jupyter-notebook, matplotlib, nlu, nlu-chatbot, pandas, pymongo, python, rasa-chatbot, rasa-nlu, spacy, spacy-nlp, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.72 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 🌟 Chatbot with RASA and NLU Model 🌟  

**An AI-powered conversational assistant designed to revolutionize user interaction for E-commerce.**

---

## 🎯 **Business Objective**

A chatbot serves as a digital assistant, engaging users in natural conversations—whether to fetch product details, provide support, or process requests seamlessly. This project focuses on building an **AI-based chatbot** using the **Rasa NLU framework**, enabling dynamic and intelligent communication.  

**Use Case:** An E-commerce assistant capable of:  

- 📦 Providing product information (`product_info`)  

- 💰 Answering price inquiries (`ask_price`)  

- ❌ Handling order cancellations (`cancel_order`)  

---

## 🚀 **Project Overview**

### **What’s Inside?**  

- **Intents:** Understanding user goals (e.g., `ask_price`, `cancel_order`)  

- **Entities:** Extracting contextual data (e.g., `product`, `order_id`, `location`)  

- **Pipeline:** Built using **Spacy** and **TensorFlow** for robust natural language understanding.  

### **Tech Stack** 🛠️  

- **Language:** Python 🐍  

- **Libraries:** `pandas`, `matplotlib`, `rasa`, `pymongo`, `tensorflow`, `spacy`  

- **Database:** MongoDB  

---

## 📁 **Folder Structure**

### 🔹 **Input:**  

Contains training data, configurations, and sample intents/entities:  

- `data.json`  

- `spacy_config.yml`  

- `tensorflow_config.yml`  

### 🔹 **Src (Source Code):**  

The backbone of the project, containing modularized code:  

- **`Engine.py`**: The main script orchestrating all functions.  

- **`ML_Pipeline` folder**: Contains modular Python functions for:  

  - Data preparation 📊  

  - Model training 🤖  

  - Evaluation metrics 📈  

### 🔹 **Output:**  

Pre-trained models for instant deployment. No need to retrain from scratch—just load and go! 🚀  

### 🔹 **Lib (Reference Materials):**  

Includes Jupyter notebooks, reference slides, and notes for deeper understanding.  

---

## 🛠️ **Key Features**

1. **Intent and Entity Recognition:**  

   - Captures user intent (`product_info`, `ask_price`) and extracts relevant entities (`product`, `order_id`).  

2. **Model Training Pipelines:**  

   - Supports **Spacy** and **TensorFlow** pipelines for intent classification and entity recognition.  

3. **Data Visualization & Insights:**  

   - Exploratory Data Analysis (EDA) for a deeper understanding of dataset patterns.  

4. **MongoDB Integration:**  

   - Efficiently manages session-based interactions.  

---

## 📚 **How It Works**

1. 🧹 **Data Preparation**: Curate datasets from tools like [Rasa NLU Trainer](https://rasahq.github.io/rasa-nlu-trainer/) or [Chatito](https://rodrigopivi.github.io/Chatito/).  

2. 🧩 **Modular Code**: Functions are neatly organized for clarity and scalability.  

3. 🎛️ **Model Configuration**: YAML files for **Spacy** and **TensorFlow** pipelines.  

4. 🏋️ **Training**: Models trained on annotated datasets for intent and entity recognition.  

5. 📊 **Evaluation**: Confusion matrix plots to compare models and select the best one.  

6. 🤝 **Chatbot Testing**: Seamless real-time testing for robust performance validation.  

---

## 💡 **Project Takeaways**

By the end of this project, you’ll learn:  

- The fundamentals of **AI-based chatbots**.  

- How to configure pipelines with **Rasa NLU**, **Spacy**, and **TensorFlow**.  

- MongoDB integration for chatbot sessions.  

- Building modularized, scalable Python codebases.  

---

## 🎉 **Want to Explore?**

### **Try it Out!**

1. Clone this repo:  

   ```bash

   git clone https://github.com/Vidhi1290/Chatbot-with-RASA-NLU-Model-and-Python.git

   ```

2. Install dependencies:  

   ```bash

   pip install -r requirements.txt

   ```

3. Train the model:  

   ```bash

   python src/engine.py

   ```

4. Test the chatbot:  

   ```bash

   python src/test_chatbot.py

   ```

---

## 📞 **Connect with Me!**  

**Vidhi Waghela**  

- ✉️ Email: [[email protected]](mailto:[email protected])  

- 📞 Contact: +91 9152257810  

- 🌐 [GitHub](https://github.com/Vidhi1290) | [Kaggle](https://www.kaggle.com/vidhikishorwaghela)  

- 💼 [LinkedIn](https://www.linkedin.com/in/vidhi-waghela-434663198/)  

- 📸 [Instagram](https://www.instagram.com/vidhi_waghela__/)  

- 🐦 [X (Twitter)](https://x.com/VidhiWaghela)  

- ✍️ [Medium](https://medium.com/@datasciencemeetscybersecurity)  

---