Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- tatsu-lab/alpaca
+language:
+- en
+metrics:
+- perplexity
+base_model:
+- distilbert/distilgpt2
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- chatbot
+- instruction-tuning
+- distilgpt2
+- alpaca
+- transformers
+- fine-tuned
+- offline
+- flask
+---
+# 🧠 My Fine-Tuned Local Chatbot
+
+A locally hosted AI chatbot powered by a fine-tuned **DistilGPT2** model using Hugging Face Transformers. This chatbot is trained on the **Stanford Alpaca Instruction Dataset**, enabling it to follow instructions and provide helpful responses — all without relying on internet access.
+
+---
+
+## 🚀 Features
+
+- 🔒 **Fully local** – no internet required after setup  
+- 🧠 **Fine-tuned on Stanford Alpaca-style instructions**  
+- ⚡ **Fast inference** with CUDA or CPU fallback  
+- 🌐 **Flask API** with simple HTML/CSS/JavaScript frontend  
+- 🎨 **Customizable prompts** and response formatting  
+- 🧾 **Chat history** saved using SQLite  
+
+---
+
+## 📂 Project Structure
+
+```
+├── app.py # Flask API backend
+├── train.py # Script for fine-tuning the model
+├── chatbot_model/
+│ └── trained_model_* # Your fine-tuned model directory
+├── static/
+│ ├── styles.css # Frontend styles
+│ └── script.js
+├── templates/
+│ └── index.html # Web UI
+├── requirements.txt
+├── README.md # You are here!
+├── download.py
+├── preprocess.py
+├── int_db.py
+├── chat_history.db # Saves history of chats
+└── processed_dataset.csv
+```
+
+---
+
+## 💡 Sample Prompt
+
+> **Human**: What is the capital of France?  
+> **Assistant**: The capital of France is Paris.
+
+---
+
+## 🏋️‍♀️ Training
+```
+python train.py
+```
+
+This will:
+
+Download the Stanford Alpaca dataset
+
+Fine-tune distilgpt2
+
+Save it inside:
+chatbot_model/trained_model_YYYYMMDD_HHMMSS/
+
+---
+
+## 🖥️ Run the App
+```
+python app.py
+```
+Then visit: http://localhost:5005
+
+---
+
+## ❓ FAQ
+Q: Does this work offline?
+✅ Yes! Once the model is fine-tuned, no internet is needed.
+
+Q: Can I run it on CPU?
+✅ Yes, but it will be slower. A CUDA GPU is recommended for faster responses.
+
+Q: Can I replace the model?
+✅ Yes! You can fine-tune any Hugging Face-compatible model by modifying train.py.
+
+---
+
+## 🛠️ Tech Stack
+- Flask – Web server backend
+- Transformers – Hugging Face inference
+- PyTorch – Deep learning engine
+- HTML/CSS/JavaScript – Frontend
+- Stanford Alpaca Dataset
+- SQLite – For saving chat history
+- Python
+
+---
+
+## 📜 License
+MIT License – Free to use, modify, and share.
+
+---
+
+## 👩‍💻 Author
+Jasleen Kaur Matharoo
+📧 [email protected]
+🌐 GitHub @Jasleen-05