app.py

import streamlit as st
from ctransformers import AutoModelForCausalLM
from datetime import datetime

# Configuration
MODEL_PATH = "llama-3.1-medpalm2-imitate-8b-instruct.Q4_K_M.gguf"
THEME_CONFIG = {
    "primary": "#2563EB",
    "background": "#0F172A",
    "text": "#F8FAFC"
}

# Initialize session state
if "history" not in st.session_state:
    st.session_state.history = []

@st.cache_resource(show_spinner=False)
def load_model():
    return AutoModelForCausalLM.from_pretrained(
        MODEL_PATH,
        model_type="llama",
        gpu_layers=40,
        context_length=2048
    )

def medical_prompt(query):
    return f"""<|im_start|>system
    You are a medical expert AI. Provide accurate, compassionate responses.
    Follow this structure:
    1. Analyze symptoms
    2. List possible conditions (with likelihood)
    3. Recommend immediate actions
    4. Suggest diagnostic tests
    5. Prevention tips
    6. Ask follow-up questions
    
    Keep responses under 300 words.<|im_end|>
    <|im_start|>user
    {query}<|im_end|>
    <|im_start|>assistant
    """

def generate_response(query):
    try:
        response = model(
            medical_prompt(query),
            temperature=0.7,
            top_p=0.85,
            max_new_tokens=400
        )
        return response.strip().replace("<|im_end|>", "")
    except Exception as e:
        return f"⚠️ Error: {str(e)}"

# UI Setup
st.set_page_config(
    page_title="MedPalm AI",
    page_icon="🧠",
    layout="wide"
)

st.markdown(f"""
<style>
    body {{background: {THEME_CONFIG['background']}; color: {THEME_CONFIG['text']}}
    .stChatMessage {{border-radius: 15px; padding: 1.5rem; margin: 1rem 0}}
    .stChatInput {{position: fixed; bottom: 20px; width: 65%;}}
    @keyframes pulse {{0% {{opacity:1}} 50% {{opacity:0.5}} 100% {{opacity:1}}}}
</style>
""", unsafe_allow_html=True)

# Main Chat
st.title("⚕️ MedPalm AI Doctor")
st.caption("Instant Medical Analysis & Symptom Checker")

model = load_model()

for entry in st.session_state.history:
    with st.chat_message("user"):
        st.markdown(entry["query"])
    with st.chat_message("bot"):
        st.markdown(entry["response"])

if prompt := st.chat_input("Describe your symptoms..."):
    with st.spinner("🔍 Analyzing..."):
        response = generate_response(prompt)
        st.session_state.history.append({
            "query": prompt, 
            "response": response,
            "time": datetime.now().strftime("%H:%M:%S")
        })
        st.rerun()