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This project is a medical chatbot leveraging AI and NLP to assist users with medical-related queries. It utilizes Pinecone for vector search, FastAPI/Flask for backend, and a clean HTML/CSS frontend for an interactive UI. The chatbot is designed to provide quick and reliable responses using machine learning models and advanced search techniques.

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Aymen016/Medical-Chatbot

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🏥 Medical Chatbot

🚀 A cutting-edge AI-powered medical chatbot designed to assist users with medical queries using advanced NLP and vector search technology.

Screenshot 2025-02-20 214250


📂 Project Structure

  • 📁 research/ # 📄 Research documents & references
  • 📁 src/ # ⚙️ Source code for chatbot logic
  • 📁 static/ # 🎨 CSS & frontend assets
  • 📁 templates/ # 🖼️ HTML templates for UI
  • 📜 .gitignore/ # 🚫 Git ignored files
  • 📜 LICENSE/ # 📜 License information
  • 📜 README.md/ # 📖 This file!
  • 📜 app.py/ # 🚀 Main Flask API
  • 📜 requirements.txt/ # 📌 Required dependencies 🔖
  • 📜 setup.py/ # 🛠️ Setup configurations
  • 📜 store_index.py/ # 📂 Pinecone index storage ├
  • 📜 template.py/ # 🎭 UI template logic

🔧 Installation & Setup

1️⃣ Clone the repository 🛠️

git clone https://github.com/your-repo.git
cd your-repo

2️⃣ Create a virtual environment 🌍

python -m venv venv
source venv/bin/activate  # On MacOS/Linux
venv\Scripts\activate     # On Windows

3️⃣ Install dependencies 📦

pip install -r requirements.txt

4️⃣ Run the application 🚀

python app.py

🛠️ Technologies Used

  • 🐍 Python
  • FastAPI / Flask
  • 🧠 Machine Learning & NLP
  • 🔍 Pinecone (Vector Search)
  • 🎨 HTML / CSS / JS
  • ☁️ Cloud Deployment (AWS/GCP/Azure)

💡 Features

Real-time medical query responses
AI-powered chatbot assistant
Intelligent search with Pinecone
User-friendly interface


🤝 Contributing

🚀 Want to contribute? Feel free to submit a PR! 🛠️


📜 License

🔖 This project is licensed under the MIT License.


📩 Contact

📧 Email: ayemenbaig26@gmail.com

About

This project is a medical chatbot leveraging AI and NLP to assist users with medical-related queries. It utilizes Pinecone for vector search, FastAPI/Flask for backend, and a clean HTML/CSS frontend for an interactive UI. The chatbot is designed to provide quick and reliable responses using machine learning models and advanced search techniques.

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