Added another learning source for Nora. Also added the requirements.

persona_manager.py

@@ -0,0 +1,88 @@
+# persona_manager.py
+
+import os
+import torch
+import asyncio
+import re
+
+from tokenizer import CharTokenizer
+from model import NoraTransformerLM
+
+PERSONA_PATH = "nora_persona.txt"
+
+# 1) A meta-prompt that explicitly tells Nora to invent a persona and avoid quoting:
+PERSONA_META_PROMPT = (
+    "Below, Nora will create a brand‐new identity for herself. "
+    "She must NOT quote from any books or passages she has read. "
+    "Instead, she should invent her own style, voice, quirks, and personality traits as if she were a completely new person. "
+    "Her persona should be flirty, playful, curious, and speak in full sentences. "
+    "Write at least three paragraphs in your own words.\n\n"
+    "Nora, please invent and write your complete persona now:\n\nNora:"
+)
+
+
+async def generate_persona(model: NoraTransformerLM, tokenizer: CharTokenizer, device: str) -> str:
+    """
+    Ask Nora to write out her own, original persona, avoiding any verbatim quotes.
+    Returns the raw generated text.
+    """
+    # We’ll ask for up to 512 tokens, with higher temperature and top_p sampling.
+    # That combination tends to produce more creative, less‐memorizable text.
+    raw = await asyncio.to_thread(
+        model.generate,
+        tokenizer,
+        device,
+        PERSONA_META_PROMPT,
+        512,      # allow several paragraphs
+        1.2,      # higher temperature for more creativity
+        0         # top_k=0 means no fixed-k; we’ll apply top_p filtering instead
+    )
+
+    # At this point, “raw” may include the word “Nora:” etc. Strip everything before “Nora:”
+    if "Nora:" in raw:
+        persona_text = raw.split("Nora:")[-1].strip()
+    else:
+        persona_text = raw.strip()
+
+    # Now apply a simple post‐filter: remove any long spans that match exact sequences in the book corpus.
+    # This is optional but helps ensure she didn’t copy large chunks verbatim. We check for 6+ character substrings
+    # appearing more than once in her output.
+    def remove_long_quotes(text: str) -> str:
+        filtered = text
+        # find any substring of length ≥6 that appears twice; we’ll just guess she’s quoting if it’s repeated.
+        for match in re.finditer(r"\b[\w',]{6,}\b", text):
+            substr = match.group(0)
+            if filtered.count(substr) > 1:
+                filtered = filtered.replace(substr, "[…]")
+        return filtered
+
+    persona_text = remove_long_quotes(persona_text)
+    return persona_text
+
+
+def ensure_persona_file(model: NoraTransformerLM, tokenizer: CharTokenizer, device: str):
+    """
+    If nora_persona.txt does not exist, generate one (ensuring originality).
+    """
+    if os.path.isfile(PERSONA_PATH):
+        return
+
+    print("[persona] No persona found. Generating a new, original persona…")
+    persona_text = asyncio.run(generate_persona(model, tokenizer, device))
+
+    # Save to disk
+    with open(PERSONA_PATH, "w", encoding="utf-8") as f:
+        f.write(persona_text)
+    print(f"[persona] Wrote new persona to {PERSONA_PATH}.")
+
+
+async def maybe_update_persona(model: NoraTransformerLM, tokenizer: CharTokenizer, device: str):
+    """
+    Regenerate Nora’s persona if she requests it, overwriting the file.
+    """
+    print("[persona] Updating persona at Nora's request…")
+    persona_text = await generate_persona(model, tokenizer, device)
+    with open(PERSONA_PATH, "w", encoding="utf-8") as f:
+        f.write(persona_text)
+    print(f"[persona] Updated persona in {PERSONA_PATH}.")
+    return persona_text