app.py

import streamlit as st
from llama_cpp import Llama

st.set_page_config(page_title="🩺 Medical Chatbot", layout="centered")
st.title("🩺 Medical Chatbot (Doctor Mistral)")

@st.cache_resource
def load_model():
    return Llama(
        model_path="./models/BioMistral-7B.Q4_K_M.gguf",  # <- Update to your actual path
        n_ctx=2048,
        n_threads=8,
        n_gpu_layers=0  # Use GPU if available
    )

llm = load_model()

if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

prompt = st.text_input("Ask me a medical question:")

if st.button("Send") and prompt:
    st.session_state.chat_history.append(("You", prompt))
    full_prompt = f"You are a helpful medical assistant.\n\nUser: {prompt}\nDoctor:"
    output = llm(full_prompt, max_tokens=512, stop=["User:", "Doctor:"])
    reply = output["choices"][0]["text"].strip()
    st.session_state.chat_history.append(("Doctor", reply))

# Show chat history
for role, message in st.session_state.chat_history:
    st.markdown(f"**{role}:** {message}")