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)  # 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
WELCOME_IMAGE_PATH = "assets/cat.png"          # your local image

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 (remove model-added fluff) --------
DISCLAIMER_PATTERNS = [
    r"^\s*(hi|hello|hey)[,!.?\s-]*",            # stray greeting
    r"^\s*i'?m\s+sorr(y|ied)[^.\n]*[.\n]*",     # leading apology
    r"^\s*as an ai language model[^.\n]*[.\n]*" # “as an AI language model…”
]
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():
    """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

# -------- Sentiment helpers --------
def detect_sentiment_bucket(text: str):
    """
    Return ('neg'|'neu'|'pos', compound_score) using VADER.
    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 is_question(text: str) -> bool:
    t = (text or "").strip()
    if "?" in t:
        return True
    # Heuristic for question-like openings
    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. ",
]

# Your custom negative message:
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 (used when rules don't match) ----
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,
            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):
    # 1) sentiment first (on the raw user text)
    bucket, _score = detect_sentiment_bucket(message or "")

    # 2) rules-first responses
    msg = (message or "").strip().lower()

    # Robust greeting/help rule: catches "hi there", "hello!", etc.
    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)

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

# -------- UI --------

# Preload a welcome message that includes your local image + short intro text.
initial_messages = [
    {
        "role": "assistant",
        "content": [
            {"type": "image", "image": WELCOME_IMAGE_PATH, "alt_text": "Stylized Bastet cat image"},
            {"type": "text", "text": WELCOME_TEXT},
        ],
    }
]

chatbot_component = gr.Chatbot(
    type="messages",          # avoids deprecated tuples format
    value=initial_messages,   # show welcome image + text on load
    show_label=False,
)

demo = gr.ChatInterface(
    fn=reply,
    title="😺 Cats of Ancient Egypt Chatbot 😺",
    chatbot=chatbot_component
)

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