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4ba369d a058e67 4ba369d 064e698 4ba369d a058e67 6ef422c a058e67 4ba369d a058e67 4ba369d a058e67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import os
import requests
import streamlit as st
BACKEND_URL = os.environ["BACKEND_URL"].rstrip("/")
HF_TOKEN = os.environ.get("HF_TOKEN")
st.set_page_config(page_title="Medical Assistant", page_icon="🩺")
st.title("Medical Assistant")
st.caption(
"Ask a medical question and get an answer grounded in the MedQuAD dataset, "
"with sources cited below each response. Not a substitute for professional "
"medical advice."
)
with st.expander("ℹ️ How this works"):
st.markdown(
"""
This chat UI is the **frontend** half of a two-Space RAG system — it carries
no model logic of its own, it just calls a backend API.
**Pipeline:** your question → embedded with
[`sentence-transformers/all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
→ top-k passages retrieved from a FAISS index built over the
[MedQuAD](https://www.kaggle.com/datasets/pythonafroz/medquad-medical-question-answer-for-ai-research)
medical Q&A dataset → assembled into a prompt that restricts the model to
answering **only** from that retrieved context (no diagnosis, no
prescriptions) → answer generated by a hosted LLM and returned with its
sources.
**Models in use:** a stock, unmodified sentence-embedding model for
retrieval, plus a remote hosted LLM for generation. Nothing here is
fine-tuned or trained — there's no custom model artifact behind this
project, so there's no separate model card.
**Limitations:** answers are only as good as MedQuAD's coverage; the
backend has no control over the hosted generation model beyond the
grounding prompt; no published latency/accuracy numbers yet.
Full details: see this Space's
[README](https://huggingface.co/spaces/whosouravsharma/Medical-Assistant-ChatBot/blob/main/README.md)
and the [backend Space](https://huggingface.co/spaces/whosouravsharma/medical-assistant-ai-backend).
"""
)
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
def ask_backend(question: str) -> str:
headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
response = requests.post(
f"{BACKEND_URL}/ask",
json={"question": question},
headers=headers,
timeout=60,
)
response.raise_for_status()
result = response.json()
sources = "\n".join(f"- {s['source']} — {s['focus_area']}" for s in result["sources"])
return f"{result['answer']}\n\n**Sources**\n{sources}"
if question := st.chat_input("What are the symptoms of glaucoma?"):
st.session_state.messages.append({"role": "user", "content": question})
with st.chat_message("user"):
st.markdown(question)
with st.chat_message("assistant"):
try:
answer = ask_backend(question)
except requests.RequestException as exc:
answer = f"Sorry, the backend request failed: {exc}"
st.markdown(answer)
st.session_state.messages.append({"role": "assistant", "content": answer})
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