README.md

metadata

title: CureMind
emoji: 🩺
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false

CureMind — AI Medical Information Assistant

An end-to-end Retrieval-Augmented Generation (RAG) medical chatbot built as part of a German MSc AI portfolio. It combines curated open biomedical datasets with user-uploaded documents to answer clinical questions with source citations.

---

Architecture

User Question
     │
     ▼
Streamlit UI  ──►  FastAPI Backend
                        │
              ┌─────────┴──────────┐
              ▼                    ▼
    Google GenAI             Pinecone Vector DB
    Embeddings (768d)   ◄──  (medicalindex)
              │
              ▼
     Top-5 Relevant Chunks
              │
              ▼
     ┌────────────────────┐
     │  LLM Router        │
     │  ├─ Qwen-3.8-27B   │  (fast queries)
     │  ├─ GPT-OSS-120B   │  (reasoning / "why/how")
     │  └─ Groq-Compound  │  (fallback)
     └────────────────────┘
              │  (via Groq API)
              ▼
     Answer + Source Citations

---

Tech Stack

Layer	Technology
Frontend	Streamlit
Backend	FastAPI + Uvicorn
Vector Store	Pinecone (768-dim, dotproduct)
Embeddings	Google Generative AI (embedding-001)
LLM Inference	Groq API (Qwen-3.8-27B · GPT-OSS-120B · Groq-Compound)
RAG Framework	LangChain LCEL
Datasets	HuggingFace Hub
Config	Pydantic Settings
Deployment	HuggingFace Spaces (Docker)

---

Knowledge Base — Open Datasets

Dataset	Source	Size	License
PubMedQA	qiaojin/PubMedQA	1 k (labeled)	MIT
Mental Health Counseling	Amod/mental_health_counseling_conversations	3.5 k	RAIL-D
Medical Meadow MediQA	medalpaca/medical_meadow_mediqa	2.2 k	Academic
MedQA-USMLE	GBaker/MedQA-USMLE-4-options-hf	12.7 k	CC-BY-SA-4.0

Users can also upload their own PDF documents via the sidebar to extend the knowledge base.

---

API Endpoints

Method	Path	Description
GET	/health	Service health check
POST	/ask/	Submit a question, returns answer + sources
POST	/upload_pdfs/	Upload PDF documents for indexing
GET	/hf_datasets/	List available HuggingFace datasets
POST	/hf_datasets/load/	Load a dataset into the vector store

Interactive docs available at /docs when running.

---

Local Development

1. Clone and install

git clone https://github.com/YOUR_USERNAME/curemind.git
cd curemind

2. Configure environment

cp server/.env.example server/.env
# Fill in your API keys in server/.env

Required keys:

• GOOGLE_API_KEY — console.cloud.google.com
• GROQ_API_KEY — console.groq.com
• PINECONE_API_KEY — app.pinecone.io
• HUGGINGFACE_HUB_TOKEN — huggingface.co/settings/tokens

3. Run backend

cd server
pip install -r requirements.txt
uvicorn main:app --reload

4. Run frontend

cd client
pip install -r requirements.txt
streamlit run app.py

---

HuggingFace Spaces Deployment

Environment Variables (set in Space Settings → Variables and Secrets)

Key	Type
GOOGLE_API_KEY	Secret
GROQ_API_KEY	Secret
PINECONE_API_KEY	Secret
HUGGINGFACE_HUB_TOKEN	Secret
PINECONE_INDEX_NAME	Variable — medicalindex
PINECONE_REGION	Variable — us-east-1

The Docker container starts both FastAPI (port 8000, internal) and Streamlit (port 7860, public) automatically.

---

Project Structure

curemind/
├── Dockerfile                  # HF Spaces Docker build
├── docker-entrypoint.sh        # Starts both services
├── render.yaml                 # Render.com alternative deployment
│
├── server/                     # FastAPI backend
│   ├── core/
│   │   ├── settings.py         # Pydantic BaseSettings
│   │   └── schemas.py          # Request / response models
│   ├── modules/
│   │   ├── llm.py              # LangChain LCEL chain + model router
│   │   ├── load_vectorstore.py # PDF ingestion pipeline
│   │   └── hf_dataset_loader.py# HuggingFace dataset ingestion
│   ├── routes/
│   │   ├── ask_question.py     # POST /ask/
│   │   ├── upload_pdfs.py      # POST /upload_pdfs/
│   │   └── load_hf_datasets.py # GET|POST /hf_datasets/
│   └── main.py
│
└── client/                     # Streamlit frontend
    ├── app.py                  # Entry point
    ├── components/
    │   ├── chatUI.py
    │   ├── upload.py
    │   ├── hf_loader.py
    │   └── history_download.py
    └── utils/
        └── api.py