Added Streamlit autocomplete UI for bigram name generation

diagram_autocomplete_streamlit.py

@@ -0,0 +1,45 @@
+import streamlit as st
+import torch
+
+st.title("Bigram Name Autocomplete")
+st.write("Type a name and see the most likely next characters based on the bigram model.")
+
+# Load dataset
+words = open('names.txt').read().splitlines()
+
+# Build vocabulary
+chars = sorted(list(set(''.join(words))))
+stoi = {s: i + 1 for i, s in enumerate(chars)}
+stoi['.'] = 0
+itos = {i: s for s, i in stoi.items()}
+
+# Build bigram count matrix
+N = torch.zeros((27, 27), dtype=torch.int32)
+for w in words:
+    chs = ['.'] + list(w) + ['.']
+    for ch1, ch2 in zip(chs, chs[1:]):
+        N[stoi[ch1], stoi[ch2]] += 1
+
+# Convert counts to probabilities
+P = N.float()
+P = P / P.sum(dim=1, keepdim=True)
+
+# User input
+user_input = st.text_input("Start typing a name:", value="a")
+
+if user_input:
+    # Use last character as context
+    last_char = user_input[-1].lower()
+    if last_char not in stoi:
+        st.warning(f"Character '{last_char}' is not in the training vocabulary.")
+    else:
+        ix = stoi[last_char]
+        probs = P[ix]
+        top_k = torch.topk(probs, 5)
+
+        st.subheader(f"Top 5 likely next characters after '{last_char}':")
+        for i, (prob, idx) in enumerate(zip(top_k.values, top_k.indices)):
+            char = itos[idx.item()]
+            if char == '.':
+                char = "(end of name)"
+            st.write(f"{i + 1}. **{char}** — probability {prob.item():.4f}")