app.py

import os
import re
import gradio as gr

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

from transformers import pipeline

# vaderSentiment (graceful optional import)
try:
    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
except Exception:
    SentimentIntensityAnalyzer = None

# -------- Model / bot configuration --------
GEN_MODEL_NAME = "MBZUAI/LaMini-Flan-T5-248M"
WELCOME_IMAGE_PATH = "assets/cat.png"

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. "
    "Do not include greetings or apologies and do not say phrases like 'as an AI language model'. "
    "Start directly with the answer."
)

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

WELCOME_TEXT = "Hi! I share facts about cats in ancient Egypt.\n\n" + HELP_TEXT

# -------- Output cleanup --------
DISCLAIMER_PATTERNS = [
    r"^\s*(hi|hello|hey)[,!.?\s-]*",
    r"^\s*i'?m\s+sorr(y|ied)[^.\n]*[.\n]*",
    r"^\s*as an ai language model[^.\n]*[.\n]*"
]
def strip_preamble(text: str) -> str:
    t = text or ""
    for pat in DISCLAIMER_PATTERNS:
        t = re.sub(pat, "", t, flags=re.IGNORECASE)
    return t.strip()

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

def get_t2t():
    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():
    global _vader
    if _vader is None:
        if SentimentIntensityAnalyzer is None:
            print("[startup] VADER not installed; using neutral sentiment fallback")
            class _NeutralVader:
                def polarity_scores(self, _): return {"compound": 0.0}
            _vader = _NeutralVader()
        else:
            _vader = SentimentIntensityAnalyzer()
            print("[startup] Loaded VADER sentiment analyzer")
    return _vader

# -------- Sentiment helpers --------
def detect_sentiment_bucket(text: str):
    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 is_question(text: str) -> bool:
    t = (text or "").strip()
    if "?" in t: return True
    return bool(re.match(r"^(who|what|when|where|why|how|do|does|did|can|could|is|are|was|were|should|would|will)\b", t.lower()))

def is_thanks_or_praise(text: str) -> bool:
    t = (text or "").lower()
    return any(k in t for k in [
        "thanks", "thank you", "appreciate", "appreciated",
        "great answer", "nice", "awesome", "love", "helpful",
        "that helped", "that was good", "i like your response"
    ])

POS_QUESTION_PREFIXES = ["Good question! ", "Nice one—here’s the gist: ", "Let’s dig in. "]
POS_PRAISE_PREFIXES   = ["You’re welcome—glad that helped. ", "Appreciate the kind words! ", "Happy it was useful. "]
POS_STATEMENT_PREFIXES= ["Sounds good. ", "Got it. ", "All right—here’s the short version. "]
NEG_PREFIX = "Calm down. You're being a little too negative! "

def choose_positive_prefix(message: str) -> str:
    if is_thanks_or_praise(message): return POS_PRAISE_PREFIXES[0]
    if is_question(message):         return POS_QUESTION_PREFIXES[0]
    return POS_STATEMENT_PREFIXES[0]

def apply_tone_prefix(reply_text: str, bucket: str, message: str = "") -> str:
    if bucket == "pos":   prefix = choose_positive_prefix(message)
    elif bucket == "neg": prefix = NEG_PREFIX
    else:                 prefix = ""
    return (prefix + (reply_text or "")).strip()

# ---- LLM fallback ----
def ai_fallback(prompt: str) -> str:
    try:
        gen = get_t2t()
        prefixed = f"{DOMAIN_INSTRUCTIONS}\n\nUser: {prompt}\nAssistant:"
        out = gen(prefixed, max_new_tokens=48, do_sample=False, no_repeat_ngram_size=3)[0]["generated_text"]
        return strip_preamble(out)
    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):
    bucket, _score = detect_sentiment_bucket(message or "")
    msg = (message or "").strip().lower()

    if re.search(r"\b(hi|hello|hey|hiya|yo|greetings)\b", msg) or any(k in msg for k in ["help","menu","topics","instructions"]):
        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)

    return apply_tone_prefix(base, bucket, message)

# -------- UI --------
initial_messages = [
    {"role": "assistant", "content": WELCOME_TEXT}  # text only in the chat history (stable)
]

chatbot_component = gr.Chatbot(
    type="messages",
    value=initial_messages,
    show_label=False,
)

# UI (banner is collapsible so it won't push the input off-screen)
with gr.Blocks(fill_height=True) as demo:
    if os.path.exists(WELCOME_IMAGE_PATH):
        with gr.Accordion("Show banner", open=False):
            gr.Image(
                value=WELCOME_IMAGE_PATH,
                show_label=False,
                interactive=False,
                container=False,
                height=200
            )
    gr.ChatInterface(
        fn=reply,
        title="😺 Cats of Ancient Egypt Chatbot 😺",
        chatbot=chatbot_component,  # already defined above
        type="messages",
    )


# -------- Launch (SSR off + Spaces-friendly) --------
if __name__ == "__main__":
    demo.launch(
        ssr_mode=False,
        server_name="0.0.0.0",
        server_port=int(os.getenv("PORT", "7860")),
        share=False,
        allowed_paths=["assets"],
    )