app.py

import os, re, time, datetime, traceback, torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from transformers.utils import logging as hf_logging

# ---------- Logging ---------------------------------------------------------
os.environ["HF_HOME"] = "/data/.huggingface"
LOG_FILE = "/data/requests.log"


def log(msg: str):
    ts = datetime.datetime.utcnow().strftime("%H:%M:%S.%f")[:-3]
    line = f"[{ts}] {msg}"
    print(line, flush=True)
    try:
        with open(LOG_FILE, "a") as f:
            f.write(line + "\n")
    except FileNotFoundError:
        pass


# ---------- Config ----------------------------------------------------------
MODEL_ID = "ibm-granite/granite-3.3-2b-instruct"
MAX_TURNS, MAX_TOKENS, MAX_INPUT_CH = 6, 128, 400

SYSTEM_MSG = (
    "You are **SchoolSpirit AI**, the digital mascot for SchoolSpirit AI LLC, "
    "founded by Charles Norton in 2025. The company installs on‑prem AI chat "
    "mascots, offers custom fine‑tuning, and ships turnkey GPU hardware to schools.\n\n"
    "Guidelines:\n"
    "• Warm, concise answers (max 4 sentences).\n"
    "• No personal‑data collection or sensitive advice.\n"
    "• If unsure, say so and suggest a human follow‑up.\n"
    "• Avoid profanity, politics, or mature themes."
)
WELCOME_MSG = "Welcome to SchoolSpirit AI! Do you have any questions?"

strip = lambda s: re.sub(r"\s+", " ", s.strip())


# ---------- Load model (GPU FP‑16 → CPU fallback) ---------------------------
hf_logging.set_verbosity_error()
try:
    log("Loading tokenizer …")
    tok = AutoTokenizer.from_pretrained(MODEL_ID)

    if torch.cuda.is_available():
        log("GPU detected → FP‑16")
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_ID, device_map="auto", torch_dtype=torch.float16
        )
    else:
        log("CPU fallback")
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_ID, device_map="cpu", torch_dtype="auto", low_cpu_mem_usage=True
        )

    gen = pipeline(
        "text-generation",
        model=model,
        tokenizer=tok,
        max_new_tokens=MAX_TOKENS,
        do_sample=True,
        temperature=0.6,
        pad_token_id=tok.eos_token_id,
    )
    MODEL_ERR = None
    log("Model loaded ✔")
except Exception as exc:  # noqa: BLE001
    MODEL_ERR, gen = f"Model load error: {exc}", None
    log(MODEL_ERR)


# ---------- Chat callback ---------------------------------------------------
def chat_fn(user_msg: str, history: list[dict]):
    """
    history comes in/out as list[{'role':'user'|'assistant','content':str}, …]
    """
    if MODEL_ERR:
        return history + [{"role": "assistant", "content": MODEL_ERR}]

    user_msg = strip(user_msg or "")
    if not user_msg:
        return history + [{"role": "assistant", "content": "Please type something."}]
    if len(user_msg) > MAX_INPUT_CH:
        warn = f"Message too long (>{MAX_INPUT_CH} chars)."
        return history + [{"role": "assistant", "content": warn}]

    # Append user to history
    history.append({"role": "user", "content": user_msg})

    # Keep system + last N messages
    convo = [m for m in history if m["role"] != "system"][-MAX_TURNS * 2 :]
    prompt_parts = [SYSTEM_MSG] + [
        f"{'User' if m['role']=='user' else 'AI'}: {m['content']}" for m in convo
    ] + ["AI:"]
    prompt = "\n".join(prompt_parts)

    try:
        raw = gen(prompt)[0]["generated_text"]
        reply = strip(raw.split("AI:", 1)[-1])
        reply = re.split(r"\b(?:User:|AI:)", reply, 1)[0].strip()
    except Exception:
        log("❌ Inference error:\n" + traceback.format_exc())
        reply = "Sorry—backend crashed. Please try again later."

    history.append({"role": "assistant", "content": reply})
    return history


# ---------- Launch ----------------------------------------------------------
gr.ChatInterface(
    fn=chat_fn,
    chatbot=gr.Chatbot(
        height=480,
        type="messages",
        value=[
            {"role": "assistant", "content": WELCOME_MSG}
        ],  # ONE welcome bubble
    ),
    additional_inputs=None,
    title="SchoolSpirit AI Chat",
    theme=gr.themes.Soft(primary_hue="blue"),
    examples=None,
).launch()