https://github.com/aniket2021448/medibotiq
An AI-driven chatbot designed to assist with health queries using a custom LLM hosted on Hugging Face. It features a Streamlit interface, LangChain for prompt management, and Pinecone for efficient vector retrieval, ensuring accurate and context-aware responses. While helpful, it is not a replacement for professional medical advice.
https://github.com/aniket2021448/medibotiq
generative-ai-projects huggingface llama2-7b llm pinecone python sentence-transformer-model streamlit
Last synced: 4 months ago
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An AI-driven chatbot designed to assist with health queries using a custom LLM hosted on Hugging Face. It features a Streamlit interface, LangChain for prompt management, and Pinecone for efficient vector retrieval, ensuring accurate and context-aware responses. While helpful, it is not a replacement for professional medical advice.
- Host: GitHub
- URL: https://github.com/aniket2021448/medibotiq
- Owner: Aniket2021448
- Created: 2024-06-29T07:05:11.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-09T10:41:34.000Z (over 1 year ago)
- Last Synced: 2025-04-15T05:51:28.075Z (about 1 year ago)
- Topics: generative-ai-projects, huggingface, llama2-7b, llm, pinecone, python, sentence-transformer-model, streamlit
- Language: Jupyter Notebook
- Homepage: https://huggingface.co/spaces/GoodML/MediBotAI
- Size: 10.4 MB
- Stars: 0
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🤖 MediBot AI – AI-Powered Medical Chatbot
## 🚀 Overview
MediBot AI is an **AI-powered medical chatbot** that provides users with information about medical conditions, symptoms, and treatments. It leverages **NLP, ML, and LLM techniques** to generate accurate responses in real-time. Designed for quick and efficient access to medical information, this chatbot is a valuable resource for users seeking health-related guidance.
> ⚠️ **Disclaimer:**
> MediBot AI provides **general medical information** and is **not a substitute for professional medical advice**. Always consult a healthcare provider for medical concerns.
## ✨ Key Features
✅ **Custom LLM Model** – Utilizes a **custom Large Language Model** hosted on Hugging Face for precise medical responses.
✅ **Streamlit Interface** – Simple and interactive **web-based UI** for smooth user interactions.
✅ **Hugging Face Integration** – Secure model authentication via Hugging Face credentials.
✅ **LangChain for Context Management** – Handles chat history and improves **context-aware responses**.
✅ **Pinecone for Vector Storage** – Stores embeddings for efficient **retrieval of relevant medical information**.
## 📊 Demo
🔗 **Live Demo:** [Hugging Face Space](https://huggingface.co/spaces/GoodML/MediBotAI)
## 🛠️ Technologies Used
- **Python** 🐍
- **Streamlit** 🌐
- **Hugging Face API** 🤗
- **LangChain** 🔗
- **Pinecone (Vector Database)** 📚
- **LLaMA (Large Language Model)** 🧠
- **Pandas & NumPy** 📊
## 📂 Project Structure
MediBotAI/ │── app.py # Main Streamlit application
│── model.py # LLM model integration & response generation
│── data_handler.py # Pinecone vector storage & retrieval
│── prompt_manager.py # LangChain prompt management
│── requirements.txt # Python dependencies
│── README.md # Project documentation
│── assets/ # Images & resources
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## ⚡ Installation & Setup
Follow these steps to set up **MediBot AI** locally:
### 🔹 1. Clone the Repository
```bash
git clone https://github.com/YourUsername/MediBotAI.git
cd MediBotAI
🔹 2. Install Dependencies
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pip install -r requirements.txt
🔹 3. Set Up Hugging Face Authentication
Create an account on Hugging Face.
Generate an API token from your Hugging Face settings.
Add the token in your environment variables:
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export HUGGINGFACE_API_KEY="your_api_token_here"
🔹 4. Run the Streamlit App
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streamlit run app.py
🏗️ How It Works
1️⃣ User Input: Users enter medical queries via the Streamlit interface.
2️⃣ Query Processing: LangChain processes prompts, managing chat history for contextual accuracy.
3️⃣ Medical LLM Response: The chatbot generates responses using a custom Hugging Face model.
4️⃣ Vector Retrieval: Pinecone retrieves relevant stored embeddings to enhance accuracy.
5️⃣ Output: The chatbot provides structured, medically relevant responses in real-time.
📸 Screenshots
💬 Chatbot Interface
🏥 Medical Query Response
🚀 Future Enhancements
🔹 Real-time Model Loading: Load models on-demand with progress indicators.
🔹 Enhanced Medical Knowledge Base: Continuously update the database with the latest medical insights.
🔹 Speech-to-Text Input: Allow users to ask queries via voice input.
🔹 Multilingual Support: Expand to support multiple languages for global accessibility.
🤝 Contributing
We welcome contributions! 🚀
Fork the repository
Create a new branch: git checkout -b feature/new-feature
Make your changes and commit: git commit -m "Add new feature"
Push to your branch: git push origin feature/new-feature
Open a Pull Request 🎉
📜 License
This project is licensed under the MIT License – feel free to modify and use it!
👨💻 Author
Developed by Aniket Panchal ✨
📧 Email: AniketPanchal1257@gmail.com
🔗 LinkedIn: Your LinkedIn Profile
🔗 GitHub: Your GitHub Profile
🌟 If you like this project, give it a star! ⭐
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---
### **Why this README is effective?**
✅ **Professional Formatting** – Organized for readability 🎨
✅ **Badges & Icons** – Adds a polished look 🏆
✅ **Installation Steps** – Clear setup guide 🏗️
✅ **Future Enhancements** – Shows project roadmap 🚀
✅ **Contribution Section** – Encourages collaboration 🤝