app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import pandas as pd

# ✅ Load IBM Granite model with cache to speed up
@st.cache_resource
def load_model():
    model_id = "ibm-granite/granite-3.3-2b-instruct"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)
    return pipeline("text-generation", model=model, tokenizer=tokenizer)

generator = load_model()

# ✅ Set Streamlit page configuration
st.title("🩺 HealthAI – Intelligent Healthcare Assistant")

# ✅ Define tabs
tab1, tab2, tab3, tab4 = st.tabs([
    "🧠 Patient Chat", "🧾 Disease Prediction", 
    "💊 Treatment Plans", "📊 Health Analytics"
])

# ------------------------------
# 🧠 TAB 1: Patient Chat
# ------------------------------
with tab1:
    st.subheader("Ask any health-related question")
    query = st.text_area("Enter your question here")
    
    if st.button("Get Advice", key="chat"):
        if query.strip() == "":
            st.warning("Please enter a question.")
        else:
            with st.spinner("Thinking..."):
                response = generator(query, max_new_tokens=200)[0]["generated_text"]
                st.success("AI Response:")
                st.markdown(f"markdown\n{response}\n")

# ------------------------------
# 🧾 TAB 2: Disease Prediction
# ------------------------------
with tab2:
    st.subheader("Enter your symptoms (comma-separated)")
    symptoms = st.text_input("E.g. persistent fever, fatigue, dry cough")
    
    if st.button("AI Diagnose", key="predict"):
        if symptoms.strip() == "":
            st.warning("Please enter your symptoms.")
        else:
            prompt = (
                f"I am feeling unwell. My symptoms are: {symptoms}.\n"
                "Can you please suggest what possible conditions I might have based on this?\n"
                "List top 3 possible diseases with a short reason for each, and give a seriousness score out of 10."
            )
            with st.spinner("Analyzing symptoms..."):
                result = generator(prompt, max_new_tokens=300, do_sample=True)[0]['generated_text']
                st.success("AI Prediction:")
                st.markdown(f"markdown\n{result}\n")

# ------------------------------
# 💊 TAB 3: Treatment Plans
# ------------------------------
with tab3:
    st.header("💊 Treatment Plan Generator")
condition = st.text_input("Enter the known condition (e.g., Asthma, Diabetes)")

if st.button("Get Full Treatment Plan"):
    if not condition.strip():
        st.warning("Please enter a condition.")
    else:
        with st.spinner("Generating treatment plan..."):

            def get_response(prompt):
                return generator(prompt, max_new_tokens=1000, temperature=0.7, do_sample=True)[0]['generated_text'].strip()

            prompts = {
                "1️⃣ Medications": f"What medications are usually prescribed for {condition}?",
                "2️⃣ Diet": f"What diet is recommended for someone with {condition}?",
                "3️⃣ Exercise": f"What type of physical activities should a person with {condition} follow?",
                "4️⃣ Follow-Up & Monitoring": f"What follow-up steps and monitoring should be done for {condition}?",
                "5️⃣ Precautions": f"What precautions should be taken by someone with {condition}?",
                "6️⃣ Mental Health & Stress": f"How can someone with {condition} manage stress and mental health?"
            }

            for section, prompt in prompts.items():
                st.subheader(section)
                st.markdown(f"markdown\n{get_response(prompt)}\n")

# ------------------------------
# 📊 TAB 4: Health Analytics
# ------------------------------
with tab4:
    st.subheader("Track your health data over time")
    uploaded = st.file_uploader("Upload your CSV file (with columns like 'blood_pressure', 'heart_rate')", type=["csv"])
    
    if uploaded:
        df = pd.read_csv(uploaded)
        st.dataframe(df)

        for col in df.select_dtypes(include=['float', 'int']).columns:
            st.line_chart(df[col])
            if df[col].mean() > df[col].iloc[-1]:
                st.info(f"📉 {col} is improving.")
            else:
                st.warning(f"📈 {col} is rising — consider medical advice.")