| --- |
| title: Medical Assistant UI |
| emoji: 🩺 |
| colorFrom: blue |
| colorTo: purple |
| sdk: docker |
| app_port: 8501 |
| short_description: RAG-grounded medical Q&A chat over the MedQuAD dataset |
| tags: |
| - streamlit |
| - rag |
| - medical |
| - healthcare |
| - question-answering |
| - faiss |
| - sentence-transformers |
| models: |
| - sentence-transformers/all-MiniLM-L6-v2 |
| pinned: false |
| --- |
| |
| # Medical Assistant |
|
|
| Ask a medical question and get an answer grounded in retrieved source |
| passages from the MedQuAD dataset, with the sources cited under every |
| response. The answer comes from retrieved text, not from the model's own |
| memory. |
|
|
| > **Not a substitute for professional medical advice, diagnosis, or treatment.** |
|
|
| This Space is the **frontend** of a two-Space system. Retrieval and |
| generation run in the |
| [`medical-assistant-ai-backend`](https://huggingface.co/spaces/whosouravsharma/medical-assistant-ai-backend) |
| Space. |
|
|
| ## How it works |
|
|
| 1. You type a question into the chat. |
| 2. This Space sends it to the backend's `POST /ask` endpoint. |
| 3. The backend embeds the question and retrieves the top 5 most similar |
| passages from a FAISS index built over MedQuAD. |
| 4. Those passages go into a prompt that tells the model to answer **only** |
| from the retrieved context, to avoid diagnosis and prescriptions, and to |
| say so when the context isn't enough to answer. |
| 5. A hosted LLM generates the answer, and the backend returns it along with |
| its sources. |
| 6. This Space shows the answer with the cited sources underneath. |
|
|
| ``` |
| User ──▶ Streamlit UI (this Space) ──POST /ask──▶ FastAPI backend |
| │ |
| embed (MiniLM) ──┤ |
| FAISS top-k ─────┤ (MedQuAD index) |
| grounded prompt ─┤ |
| hosted LLM ──────┘ |
| ◀──────────── answer + sources ──────────── |
| ``` |
|
|
| ## Components |
|
|
| | Component | Role | |
| |---|---| |
| | **This Space** (`Medical-Assistant-ChatBot`) | Streamlit chat UI. Its only dependencies are `streamlit` and `requests`, and it holds no model logic | |
| | [`medical-assistant-ai-backend`](https://huggingface.co/spaces/whosouravsharma/medical-assistant-ai-backend) | FastAPI service exposing `GET /health` and `POST /ask` | |
| | `whosouravsharma/medical-assistant-data` (private) | Prebuilt FAISS index and chunk metadata, pulled by the backend at startup | |
| | [MedQuAD](https://www.kaggle.com/datasets/pythonafroz/medquad-medical-question-answer-for-ai-research) | Source corpus of medical question–answer pairs | |
|
|
| ## Models |
|
|
| No model in this project is trained or fine-tuned. The system is built from |
| off-the-shelf components: |
|
|
| | Stage | Model / method | Notes | |
| |---|---|---| |
| | Embedding | [`sentence-transformers/all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | Small (~90 MB), CPU-friendly, used unmodified. Loaded once per backend process | |
| | Retrieval | FAISS `IndexFlatIP` on L2-normalized vectors | Exact cosine-similarity search, top-k = 5 | |
| | Generation | `gpt-5.6-luna` via the OpenAI API | Hosted remotely. No generation model runs inside either Space | |
|
|
| ## Data |
|
|
| The index is built offline from MedQuAD: |
|
|
| - Remove exact duplicates and records with no answer |
| - Join each question and answer into one text field |
| - Split the text into 500-word chunks with a 50-word overlap |
| - Store each chunk with its source, focus area, and original question so |
| every answer can cite where it came from |
|
|
| ## Intended use |
|
|
| **Use it for** general, informational questions about conditions, symptoms, |
| and treatments that MedQuAD covers. |
|
|
| **Don't use it for** diagnosis, treatment or medication decisions, |
| emergencies, or questions about a specific patient. |
|
|
| ## API |
|
|
| The backend can also be called directly: |
|
|
| ```bash |
| curl -X POST https://whosouravsharma-medical-assistant-ai-backend.hf.space/ask \ |
| -H "Authorization: Bearer $HF_TOKEN" \ |
| -H "Content-Type: application/json" \ |
| -d '{"question": "What are the symptoms of glaucoma?"}' |
| ``` |
|
|
| The response contains `query`, `answer`, and `sources`. Each source has its |
| rank, similarity score, source name, focus area, original question, and the |
| chunk text. |
|
|
| ## Setup (for forks / redeploys) |
|
|
| Set these as Space secrets or variables: |
|
|
| | Name | Purpose | |
| |---|---| |
| | `BACKEND_URL` | Backend Space URL: `https://whosouravsharma-medical-assistant-ai-backend.hf.space` | |
| | `HF_TOKEN` | Sent as a bearer token to the backend. Authentication is token-based and there is no user login | |
|
|
| ## Limitations |
|
|
| - Answers are limited to what MedQuAD covers. For questions outside it, the |
| model is told to say the retrieved information is insufficient rather |
| than guess. |
| - The grounding prompt reduces hallucination risk but doesn't remove it, |
| and it hasn't been formally evaluated. |
| - Generation depends on a third-party hosted model. Its behaviour can only |
| be shaped through the prompt. |
| - No latency, cost, or accuracy benchmarks have been published yet. |
| - The embedding model is general-purpose and hasn't been tuned for medical |
| text. |
|
|
