---
license: apache-2.0
title: 🧠AI Chatbot🤖
sdk: streamlit
emoji: 💻
colorFrom: pink
colorTo: purple
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/6686260107019f3fe482ce08/xfpa6MidZ5aE9OEP96pi5.jpeg
short_description: The System on Real-Time Intent Recognition and Conversations
sdk_version: 1.44.1
---
# **🤖 AI-Powered Chatbot using NLP**  
 
## **📌 Introduction**  
This project is an **AI-driven chatbot**, developed as part of my **AICTE-Shell Internship**. The chatbot leverages **Natural Language Processing (NLP) and Deep Learning** techniques using **BERT** to provide intelligent responses based on user queries. The chatbot is trained on an **Intent JSON dataset** and fine-tuned to enhance accuracy.  

🔗 **Deployed Application:** [🧠AI Chatbot🤖](https://huggingface.co/spaces/SamarthPujari/AI-Chatbot)

## **🎯 Project Goals**  
✅ Implement **AI & NLP techniques** for intelligent conversation.  
✅ Explore **BERT-based Deep Learning** for chatbot development.  
✅ Develop a **context-aware chatbot** with high accuracy.  
✅ Enhance **text preprocessing, model training, and deployment skills**.  
✅ Deploy an **interactive chatbot web app** using **Streamlit**.  

## **📂 Dataset Used**  
The chatbot is trained on a **custom Intent JSON dataset**, which includes:  

- **User Queries & Responses**: Predefined conversations.  
- **Intent Classification Data**: Labeled conversations for accurate intent detection.  
- **Pretrained BERT Model**: Fine-tuned for improved understanding.  

## **📊 Methodology**  

### **Step 1: Data Collection & Preprocessing**  
🔹 Loaded and cleaned **Intent JSON dataset**.  
🔹 **Tokenized text data** using BERT tokenizer.  
🔹 **Converted labels to categorical format** for training.  

### **Step 2: Model Selection & Training**  
🔹 Used **BERT (Bidirectional Encoder Representations from Transformers)**.  
🔹 Implemented **deep learning-based intent classification**.  
🔹 Trained on multiple epochs & tuned hyperparameters for **optimal accuracy**.  
🔹 Evaluated **training & validation accuracy** to ensure model performance.  

### **Step 3: Chatbot Development & Integration**  
🔹 Built an **Intent Recognition Model** using **BERT for Sequence Classification**.  
🔹 Designed a **Response Generation Mechanism** for accurate replies.  
🔹 Integrated trained model into a **Streamlit & HuggingFace web app** for user interaction.  

### **Step 4: Deployment & User Interaction**  
🔹 **Saved and exported the trained BERT model** for real-time inference.  
🔹 Deployed chatbot as a **Streamlit as well as HuggingFace web app**.  
🔹 **Implemented real-time conversations** with NLP-powered responses.  

## **🔍 Key Features**  
✅ **Real-time Chatbot using BERT-based Intent Recognition**.  
✅ **Deep Learning Model trained on an Intent JSON dataset**.  
✅ **Optimized Text Processing & Tokenization**.  
✅ **Accurate Intent Classification for diverse queries**.  
✅ **Deployable on Web using Streamlit**.  

## **🚀 Technologies Used**  
| Category            | Tools & Libraries |  
|---------------------|-------------------|  
| **Development**    | Python, Jupyter Notebook, Anaconda, VS Code|  
| **NLP Frameworks** | Hugging Face Transformers, BERT |  
| **Machine Learning** | TensorFlow, PyTorch |  
| **Data Processing** | Pandas, NumPy |  
| **Deployment**     | Streamlit, Streamlit Cloud, HuggingFace |  

## **📷 Screenshots**  

| **Streamlit App - Chatbot Interface** |  
|---------------------------------------|  
|![Screenshot 2025-03-15 122923](https://github.com/user-attachments/assets/8c1efceb-3f62-4b5e-b77b-74d64e6600cb)|

| **Streamlit App - Chatbot Interface** |  
|---------------------------------------|  
|![Screenshot 2025-03-15 123114](https://github.com/user-attachments/assets/d7be631b-e5de-46cc-aba1-6dda6f85e04a)|

| **Streamlit App - Chatbot Interface** |  
|---------------------------------------|  
|![Screenshot 2025-03-15 123048](https://github.com/user-attachments/assets/d881f663-8335-4228-8c1a-564ba8652370)|

| **Streamlit App - Chatbot Interface** |  
|---------------------------------------|  
|![Screenshot 2025-03-15 123003](https://github.com/user-attachments/assets/4a78aba1-77a9-4a34-9740-9217e88518da)|

## **🎯 Future Improvements**  
🔹 Expand dataset with **more real-world conversations**.  
🔹 Integrate **voice-based interaction** using Speech Recognition.  
🔹 Enhance **context retention** for long conversations.  
🔹 Optimize model efficiency for **faster response times**.  
🔹 Expanding chatbot capabilities with **multilingual support**.

## **📥 Installation & Setup**  

### **🔹 Clone the Repository**  
```bash  
git clone https://github.com/Samarth4023/Shell-Internship-2.git
cd Shell-Internship-2
```  

### **🔹 Install Required Dependencies**  
```bash  
pip install -r requirements.txt  
```  

### **🔹 Run the Streamlit App**  
```bash  
streamlit run app.py  
```  

## **📜 License**  
This project is **open-source** and free to use. Feel free to contribute!  

## **📧 Contact**  
📌 **Author:** Samarth Pujari  
📌 **GitHub:** [Samarth4023](https://github.com/Samarth4023)  
📌 **LinkedIn:** [Samarth Pujari](https://www.linkedin.com/in/samarth-pujari-328a1326a)