import streamlit as st
from datetime import datetime
import time
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import os

# -- SETUP --
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"

@st.cache_resource
def load_model():
    model_id = "sshleifer/tiny-gpt2"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)
    return pipeline("text-generation", model=model, tokenizer=tokenizer)

generator = load_model()

if "history" not in st.session_state:
    st.session_state.history = []
    st.session_state.summary = ""

TRIGGER_PHRASES = ["kill myself", "end it all", "suicide", "not worth living", "can't go on"]

def is_high_risk(text):
    return any(phrase in text.lower() for phrase in TRIGGER_PHRASES)

def get_response(prompt, max_new_tokens=100, temperature=0.7):
    output = generator(prompt, max_new_tokens=max_new_tokens, temperature=temperature)[0]["generated_text"]
    return output.strip()

st.title("🧠 TARS.help")
st.markdown("### A quiet AI that reflects and replies.")
st.markdown(f"🗓️ {datetime.now().strftime('%B %d, %Y')} | {len(st.session_state.history)//2} exchanges")

user_input = st.text_input("How are you feeling today?", placeholder="Start typing...")

if user_input:
    context = "\n".join([f"{s}: {m}" for s, m, _ in st.session_state.history[-4:]])
    with st.spinner("TARS is reflecting..."):
        time.sleep(1)
        if is_high_risk(user_input):
            response = "I'm really sorry you're feeling this way. You're not alone — please talk to someone you trust or a mental health professional. 💙"
        else:
            prompt = f"You are a calm, empathetic AI assistant.\n{context}\nUser: {user_input}\nAI:"
            response = get_response(prompt)
        timestamp = datetime.now().strftime("%H:%M")
        st.session_state.history.append(("🧍 You", user_input, timestamp))
        st.session_state.history.append(("🤖 TARS", response, timestamp))

st.markdown("## 🗨️ Session")
for speaker, msg, time in st.session_state.history:
    st.markdown(f"**{speaker} [{time}]:** {msg}")

if st.button("🧾 Generate Session Summary"):
    convo = "\n".join([f"{s}: {m}" for s, m, _ in st.session_state.history])
    prompt = f"Summarize the emotional tone and themes in this conversation:\n{convo}\nSummary:"
    try:
        summary = get_response(prompt, max_new_tokens=100, temperature=0.5)
        st.session_state.summary = summary
    except Exception as e:
        st.error("Summary generation failed.")
        st.exception(e)

if st.session_state.summary:
    st.markdown("### 🧠 Session Note")
    st.markdown(st.session_state.summary)
    st.download_button("📥 Download Summary", st.session_state.summary, file_name="tars_session.txt")

st.markdown("---")
st.caption("TARS is not a therapist. If you're in crisis, please seek help from a professional.")