src/streamlit_app.py

import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# -------------------------------
# Page config
# -------------------------------
st.set_page_config(
    page_title="DocMed Demo",
    page_icon="🩺",
    layout="centered"
)

# -------------------------------
# Header
# -------------------------------
st.title("🩺 DocMed")
st.subheader("Medical Study Assistant (Educational Use Only)")

st.markdown(
    """
⚠️ **Disclaimer**  
DocMed is an **educational AI model only**.  
It must **NOT** be used for diagnosis, treatment, or clinical decision-making.
"""
)

# -------------------------------
# Load model (cached)
# -------------------------------
@st.cache_resource
def load_model():
    model_id = "jip7e/DocMed"  # <-- your HF model repo

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map="auto"
    )
    model.eval()
    return tokenizer, model


with st.spinner("Loading DocMed model..."):
    tokenizer, model = load_model()

# -------------------------------
# User input
# -------------------------------
question = st.text_area(
    "Ask a medical question (student level):",
    placeholder="e.g. What is hydronephrosis?",
    height=120
)

# -------------------------------
# Inference
# -------------------------------
if st.button("Ask DocMed"):
    if question.strip() == "":
        st.warning("Please enter a question.")
    else:
        prompt = f"""You are DocMed, a medical study assistant.
Explain the following clearly and concisely for a medical student.
Use short sentences and simple language.
You may use emojis if helpful.

Question:
{question}

Answer:
"""

        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

        with torch.no_grad():
            output = model.generate(
                **inputs,
                max_new_tokens=120,
                temperature=0.6,
                top_p=0.9,
                repetition_penalty=1.2,
                do_sample=True
            )

        decoded = tokenizer.decode(output[0], skip_special_tokens=True)

        # Remove prompt echo if present
        answer = decoded.split("Answer:")[-1].strip()

        st.markdown("### 🧠 DocMed says:")
        st.write(answer)