Update app.py

app.py

@@ -3,80 +3,133 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from transformers.utils import logging as hf_logging
 
+# ---------------------------------------------------------------------------
 # Persistent cache + request log
+# ---------------------------------------------------------------------------
 os.environ["HF_HOME"] = "/data/.huggingface"
 LOG_FILE = "/data/requests.log"
-def log(msg): 
+
+
+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
+    try:
+        with open(LOG_FILE, "a") as f:
+            f.write(line + "\n")
+    except FileNotFoundError:
+        pass
+
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+MODEL_ID = "ibm-granite/granite-3.3-2b-instruct"  # 2‑B fits HF CPU Space
+MAX_TURNS = 4               # keep last N user/AI pairs
+MAX_TOKENS = 64
+MAX_INPUT_CH = 300
 
-# Config
-MODEL_ID, MAX_TURNS, MAX_TOKENS, MAX_INPUT_CH = (
-    "ibm-granite/granite-3.3-2b-instruct", 4, 64, 300
+SYSTEM_MSG = (
+    "You are **SchoolSpirit AI**, the official digital mascot for "
+    "SchoolSpirit AI LLC, founded by Charles Norton in 2025. The company "
+    "specializes in on‑prem AI chat mascots, custom fine‑tuning of language "
+    "models, and turnkey GPU servers for K‑12 schools and education vendors.\n\n"
+    "GUIDELINES:\n"
+    "• Respond in a warm, encouraging tone suitable for students, parents, "
+    "and staff.\n"
+    "• Keep answers concise (≤ 4 sentences) unless asked for detail.\n"
+    "• If unsure or out of scope, say you’re not sure and offer human follow‑up.\n"
+    "• No personal data collection, no medical/legal/financial advice.\n"
+    "• Maintain professionalism—no profanity, politics, or mature themes."
 )
-SYSTEM_MSG = ("You are SchoolSpirit AI, the upbeat mascot for a company that "
-              "installs on‑prem AI chatbots in schools. Keep answers short, "
-              "friendly, and safe.")
 
+WELCOME_MSG = "Welcome to SchoolSpirit AI! Do you have any questions?"
+
+# ---------------------------------------------------------------------------
 # Load model
+# ---------------------------------------------------------------------------
 hf_logging.set_verbosity_error()
 try:
     log("Loading model …")
     tok = AutoTokenizer.from_pretrained(MODEL_ID)
     model = AutoModelForCausalLM.from_pretrained(
-        MODEL_ID, device_map="auto", torch_dtype="auto")
-    gen = pipeline("text-generation", model=model, tokenizer=tok,
-                   max_new_tokens=MAX_TOKENS, do_sample=True, temperature=0.6)
+        MODEL_ID, device_map="auto", torch_dtype="auto"
+    )
+    gen = pipeline(
+        "text-generation",
+        model=model,
+        tokenizer=tok,
+        max_new_tokens=MAX_TOKENS,
+        do_sample=True,
+        temperature=0.6,
+    )
     MODEL_ERR = None
     log("Model loaded ✔")
-except Exception as exc:
+except Exception as exc:  # noqa: BLE001
     MODEL_ERR, gen = f"Model load error: {exc}", None
     log(MODEL_ERR)
 
 clean = lambda t: re.sub(r"\s+", " ", t.strip()) or "…"
-trim  = lambda m: m if len(m)<=1+MAX_TURNS*2 else [m[0]]+m[-MAX_TURNS*2:]
+trim = lambda m: m if len(m) <= 1 + MAX_TURNS * 2 else [m[0]] + m[-MAX_TURNS * 2 :]
 
+# ---------------------------------------------------------------------------
 # Chat logic
-def chat_fn(user_msg, history):
+# ---------------------------------------------------------------------------
+
+
+def chat_fn(user_msg: str, history: list):
     log(f"User sent {len(user_msg)} chars")
-    if not history or history[0]["role"]!="system":
-        history=[{"role":"system","content":SYSTEM_MSG}]
-    if MODEL_ERR: return MODEL_ERR
+    # Seed system + welcome messages on first call
+    if not history or history[0]["role"] != "system":
+        history = [
+            {"role": "system", "content": SYSTEM_MSG},
+            {"role": "assistant", "content": WELCOME_MSG},
+        ]
+
+    if MODEL_ERR:
+        return MODEL_ERR
 
     user_msg = clean(user_msg or "")
-    if not user_msg: return "Please type something."
-    if len(user_msg)>MAX_INPUT_CH: 
+    if not user_msg:
+        return "Please type something."
+    if len(user_msg) > MAX_INPUT_CH:
         return f"Message too long (>{MAX_INPUT_CH} chars)."
 
-    history.append({"role":"user","content":user_msg})
+    history.append({"role": "user", "content": user_msg})
     history = trim(history)
 
-    prompt_lines=[m["content"] if m["role"]=="system"
-                  else f'{"User" if m["role"]=="user" else "AI"}: {m["content"]}'
-                  for m in history]+["AI:"]
+    prompt_lines = [
+        m["content"]
+        if m["role"] == "system"
+        else f'{"User" if m["role"]=="user" else "AI"}: {m["content"]}'
+        for m in history
+    ] + ["AI:"]
     prompt = "\n".join(prompt_lines)
     log(f"Prompt {len(prompt)} chars → generating")
 
-    t0=time.time()
+    t0 = time.time()
     try:
-        raw   = gen(prompt)[0]["generated_text"]
-        reply = clean(raw.split("AI:",1)[-1])
-        reply = re.split(r"\b(?:User:|AI:)", reply, 1)[0].strip()   # ← cut here
+        raw = gen(prompt)[0]["generated_text"]
+        reply = clean(raw.split("AI:", 1)[-1])
+        reply = re.split(r"\b(?:User:|AI:)", reply, 1)[0].strip()
         log(f"generate() {time.time()-t0:.2f}s, reply {len(reply)} chars")
     except Exception:
-        log("❌ Inference exception:\n"+traceback.format_exc())
-        reply="Sorry—backend crashed. Please try again later."
+        log("❌ Inference exception:\n" + traceback.format_exc())
+        reply = "Sorry—backend crashed. Please try again later."
 
     return reply
 
+
+# ---------------------------------------------------------------------------
 # UI
+# ---------------------------------------------------------------------------
 gr.ChatInterface(
     fn=chat_fn,
-    chatbot=gr.Chatbot(height=480, type="messages"),
+    chatbot=gr.Chatbot(
+        height=480,
+        type="messages",
+        value=[("", WELCOME_MSG)],  # pre-populate AI welcome bubble
+    ),
     title="SchoolSpirit AI Chat",
     theme=gr.themes.Soft(primary_hue="blue"),
     type="messages",