app.py

import os
import gradio as gr

# --------- CPU hygiene (nice-to-have) ----------
os.environ["TOKENIZERS_PARALLELISM"] = "false"
try:
    import torch
    try:
        torch.set_num_threads(2)  # smoother on 2 vCPUs
    except Exception:
        pass
except Exception:
    pass

from transformers import pipeline
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

# -------- Model / bot configuration --------
GEN_MODEL_NAME = "MBZUAI/LaMini-Flan-T5-248M"  # CPU-friendly text2text model

DOMAIN_INSTRUCTIONS = (
    "You are a concise assistant about cats in ancient Egypt. "
    "Keep focus on Bastet, cat mummies, daily life, worship, and other ancient Egypt facts. "
    "If the user asks something unrelated, say briefly that you only cover those topics and suggest one."
)

HELP_TEXT = (
    "Ask me about: Bastet • cat mummies • daily life • worship\n"
    "Type anything else to try the AI fallback."
)

# -------- Lazy singletons --------
_t2t = None
_vader = None

def get_t2t():
    """Lazy-load the text2text pipeline (LaMini-Flan-T5)."""
    global _t2t
    if _t2t is None:
        _t2t = pipeline(
            "text2text-generation",
            model=GEN_MODEL_NAME,
            tokenizer=GEN_MODEL_NAME
        )
        print(f"[startup] Loaded model: {GEN_MODEL_NAME}")
    return _t2t

def get_vader():
    """Lazy-load the VADER sentiment analyzer."""
    global _vader
    if _vader is None:
        _vader = SentimentIntensityAnalyzer()
        print("[startup] Loaded VADER sentiment analyzer")
    return _vader

# -------- Helpers --------
def detect_sentiment_bucket(text: str):
    """
    Return ('neg'|'neu'|'pos', compound_score).
    Thresholds chosen for clear buckets in chat settings.
    """
    scores = get_vader().polarity_scores(text or "")
    c = scores.get("compound", 0.0)
    if c <= -0.4:
        return "neg", c
    if c >= 0.4:
        return "pos", c
    return "neu", c

def apply_tone_prefix(reply_text: str, bucket: str) -> str:
    """Prepend a tiny tone wrapper without changing factual content."""
    if bucket == "pos":
        prefix = "Great question! "
    elif bucket == "neg":
        prefix = "Calm down. You're being a little too negative! "
    else:
        prefix = ""
    return (prefix + (reply_text or "")).strip()

# ---- Use the LLM if the generator doesn't understand user prompt ----
def ai_fallback(prompt: str) -> str:
    try:
        gen = get_t2t()
        prefixed = (
            f"{DOMAIN_INSTRUCTIONS}\n\n"
            f"User: {prompt}\n"
            f"Assistant:"
        )
        out = gen(
            prefixed,
            max_new_tokens=48,
            do_sample=False
        )[0]["generated_text"]
        return (out or "").strip()
    except Exception as e:
        print("AI fallback error:", repr(e))
        return "AI fallback had an issue. Please try a simpler question or use the topics in 'help'."

# -------- Chat logic --------
def reply(message, history):
    # 1) sentiment first (on the raw user text)
    bucket, _score = detect_sentiment_bucket(message or "")

    # 2) rules-first responses
    msg = (message or "").strip().lower()
    if msg in {"hi", "hello", "hey"} or "help" in msg:
        base = "Hi! I share facts about cats in ancient Egypt.\n\n" + HELP_TEXT
    elif "bastet" in msg or "bast" in msg:
        base = "Bastet (later cat-headed) … major cult center at Bubastis in the Nile Delta."
    elif any(w in msg for w in ["mummy", "mummies", "mummified", "offering"]):
        base = "Millions of animal mummies (cats common), esp. Late Period (664–332 BCE)."
    elif any(w in msg for w in ["daily", "life", "pest", "mouse", "rat", "snake"]):
        base = "Cats protected grain stores; art shows them under chairs/on leashes with owners."
    elif any(w in msg for w in ["worship", "god", "goddess", "taboo"]):
        base = "People didn’t worship pet cats as gods; they revered cats via Bastet and votive offerings."
    else:
        base = ai_fallback(message)

    # 3) tone wrapper (content unchanged)
    return apply_tone_prefix(base, bucket)

# -------- UI --------
demo = gr.ChatInterface(
    fn=reply,
    title="Cats of Ancient Egypt Chatbot"
)

if __name__ == "__main__":
    demo.launch()