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import os, json, requests, streamlit as st
from backend.rag_engine import get_embedder,get_chroma,retrieve,seed_index
from backend.soap_generator import compose_soap
from utils.constants import DOCS_DIR,CHAT_ENDPOINT
st.set_page_config(page_title='MediAssist v13',page_icon='🩺',layout='wide')
@st.cache_resource
def emb():return get_embedder()
@st.cache_resource
def col():return get_chroma()[1]
def chat(prompt):
token=os.getenv('HF_API_TOKEN')
if not token:return 'Missing HF_API_TOKEN'
r=requests.post(CHAT_ENDPOINT,headers={"Authorization":f"Bearer {token}"},json={"inputs":prompt},timeout=200)
d=r.json()
if isinstance(d,list) and "generated_text" in d[0]:
return d[0]["generated_text"]
return str(d)
st.title("🩺 MediAssist v13 — AI Gynae Assistant")
with st.sidebar:
if st.button("Seed Index"):
n=seed_index(col(),emb(),DOCS_DIR);st.success(f"Indexed {n} chunks")
txt=st.text_area("Patient narrative")
if st.button("Generate Report"):
items=retrieve(col(),emb(),txt,5)
soap=compose_soap(txt,items)
ctx="\n".join([i["text"] for i in items])
prompt=f"Use this context to create a refined clinical report:\n{ctx}\nPatient: {txt}"
reply=chat(prompt)
st.subheader("AI Draft Report");st.write(reply)
st.subheader("SOAP");st.json(soap)
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