Add bayes_chat.py so assist.bayes_chat can be imported

assist/bayes_chat.py

@@ -0,0 +1,27 @@
+import json
+import torch
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load the Bayesian embeddings & vocab at import time
+WEIGHTS = torch.load("tensor_bayes.pt").detach().numpy()   # shape: (V, V)
+with open("vocab_bayes.json", "r") as f:
+    TOKEN2IDX = json.load(f)
+IDX2TOKEN = {int(idx): tok for tok, idx in TOKEN2IDX.items()}
+
+def bayes_chat(question: str) -> str:
+    """
+    Given a user question, tokenize → average Bayesian embeddings →
+    find the nearest token in the vocab → return that as the "answer."
+    """
+    tokens = question.lower().split()
+    idxs = [TOKEN2IDX[t] for t in tokens if t in TOKEN2IDX]
+    if not idxs:
+        return "🤔 I don’t recognize any of those words."
+    # average the rows corresponding to each token
+    qv = np.mean(WEIGHTS[idxs], axis=0, keepdims=True)
+    # compute similarities against every token’s vector
+    sims = cosine_similarity(qv, WEIGHTS)[0]
+    best_idx = int(np.argmax(sims))
+    best_tok = IDX2TOKEN.get(best_idx, "<unknown>")
+    return f"🔬 Bayesian neighbor: **{best_tok}**"