{"id":25926877,"url":"https://github.com/samarth4023/shell-internship-2","last_synced_at":"2026-05-04T00:32:55.781Z","repository":{"id":280088812,"uuid":"940950764","full_name":"Samarth4023/Shell-Internship-2","owner":"Samarth4023","description":"🤖 AICTE Shell Internship - NLP Chatbot This repository contains the implementation of a Chatbot using NLP, developed as part of the AICTE Shell Internship. 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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.  \n\n🔗 **Deployed Application:** [🧠AI Chatbot🤖](https://huggingface.co/spaces/SamarthPujari/AI-Chatbot)\n\n## **🎯 Project Goals**  \n✅ Implement **AI \u0026 NLP techniques** for intelligent conversation.  \n✅ Explore **BERT-based Deep Learning** for chatbot development.  \n✅ Develop a **context-aware chatbot** with high accuracy.  \n✅ Enhance **text preprocessing, model training, and deployment skills**.  \n✅ Deploy an **interactive chatbot web app** using **Streamlit**.  \n\n## **📂 Dataset Used**  \nThe chatbot is trained on a **custom Intent JSON dataset**, which includes:  \n\n- **User Queries \u0026 Responses**: Predefined conversations.  \n- **Intent Classification Data**: Labeled conversations for accurate intent detection.  \n- **Pretrained BERT Model**: Fine-tuned for improved understanding.  \n\n## **📊 Methodology**  \n\n### **Step 1: Data Collection \u0026 Preprocessing**  \n🔹 Loaded and cleaned **Intent JSON dataset**.  \n🔹 **Tokenized text data** using BERT tokenizer.  \n🔹 **Converted labels to categorical format** for training.  \n\n### **Step 2: Model Selection \u0026 Training**  \n🔹 Used **BERT (Bidirectional Encoder Representations from Transformers)**.  \n🔹 Implemented **deep learning-based intent classification**.  \n🔹 Trained on multiple epochs \u0026 tuned hyperparameters for **optimal accuracy**.  \n🔹 Evaluated **training \u0026 validation accuracy** to ensure model performance.  \n\n### **Step 3: Chatbot Development \u0026 Integration**  \n🔹 Built an **Intent Recognition Model** using **BERT for Sequence Classification**.  \n🔹 Designed a **Response Generation Mechanism** for accurate replies.  \n🔹 Integrated trained model into a **Streamlit \u0026 HuggingFace web app** for user interaction.  \n\n### **Step 4: Deployment \u0026 User Interaction**  \n🔹 **Saved and exported the trained BERT model** for real-time inference.  \n🔹 Deployed chatbot as a **Streamlit as well as HuggingFace web app**.  \n🔹 **Implemented real-time conversations** with NLP-powered responses.  \n\n## **🔍 Key Features**  \n✅ **Real-time Chatbot using BERT-based Intent Recognition**.  \n✅ **Deep Learning Model trained on an Intent JSON dataset**.  \n✅ **Optimized Text Processing \u0026 Tokenization**.  \n✅ **Accurate Intent Classification for diverse queries**.  \n✅ **Deployable on Web using Streamlit**.  \n\n## **🚀 Technologies Used**  \n| Category            | Tools \u0026 Libraries |  \n|---------------------|-------------------|  \n| **Development**    | Python, Jupyter Notebook, Anaconda, VS Code|  \n| **NLP Frameworks** | Hugging Face Transformers, BERT |  \n| **Machine Learning** | TensorFlow, PyTorch |  \n| **Data Processing** | Pandas, NumPy |  \n| **Deployment**     | Streamlit, Streamlit Cloud, HuggingFace |  \n\n## **📷 Screenshots**  \n\n| **Streamlit App - Chatbot Interface** |  \n|---------------------------------------|  \n|![Screenshot 2025-03-15 122923](https://github.com/user-attachments/assets/8c1efceb-3f62-4b5e-b77b-74d64e6600cb)|\n\n| **Streamlit App - Chatbot Interface** |  \n|---------------------------------------|  \n|![Screenshot 2025-03-15 123114](https://github.com/user-attachments/assets/d7be631b-e5de-46cc-aba1-6dda6f85e04a)|\n\n| **Streamlit App - Chatbot Interface** |  \n|---------------------------------------|  \n|![Screenshot 2025-03-15 123048](https://github.com/user-attachments/assets/d881f663-8335-4228-8c1a-564ba8652370)|\n\n| **Streamlit App - Chatbot Interface** |  \n|---------------------------------------|  \n|![Screenshot 2025-03-15 123003](https://github.com/user-attachments/assets/4a78aba1-77a9-4a34-9740-9217e88518da)|\n\n## **🎯 Future Improvements**  \n🔹 Expand dataset with **more real-world conversations**.  \n🔹 Integrate **voice-based interaction** using Speech Recognition.  \n🔹 Enhance **context retention** for long conversations.  \n🔹 Optimize model efficiency for **faster response times**.  \n🔹 Expanding chatbot capabilities with **multilingual support**.\n\n## **📥 Installation \u0026 Setup**  \n\n### **🔹 Clone the Repository**  \n```bash  \ngit clone https://github.com/Samarth4023/Shell-Internship-2.git\ncd Shell-Internship-2\n```  \n\n### **🔹 Install Required Dependencies**  \n```bash  \npip install -r requirements.txt  \n```  \n\n### **🔹 Run the Streamlit App**  \n```bash  \nstreamlit run app.py  \n```  \n\n## **📜 License**  \nThis project is **open-source** and free to use. Feel free to contribute!  \n\n## **📧 Contact**  \n📌 **Author:** Samarth Pujari  \n📌 **GitHub:** [Samarth4023](https://github.com/Samarth4023)  \n📌 **LinkedIn:** [Samarth Pujari](https://www.linkedin.com/in/samarth-pujari-328a1326a)  ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamarth4023%2Fshell-internship-2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamarth4023%2Fshell-internship-2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamarth4023%2Fshell-internship-2/lists"}