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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** |
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| **Streamlit App - Chatbot Interface** |
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| **Streamlit App - Chatbot Interface** |
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| **Streamlit App - Chatbot Interface** |
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## **🎯 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) |