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4e977d5 944724f 4e977d5 944724f 4e977d5 dc01079 4e977d5 cab2179 dc01079 cab2179 dc01079 cab2179 dc01079 4e977d5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import matplotlib.pyplot as plt
import nltk
import numpy as np
import pandas as pd
import seaborn as sns
import streamlit as st
from nltk.tokenize import word_tokenize
import os
# Use a writable temporary directory
nltk_data_dir = "/tmp/nltk_data"
# Create the directory if it doesn't exist
os.makedirs(nltk_data_dir, exist_ok=True)
# Add it to NLTK's data path
nltk.data.path.append(nltk_data_dir)
# Download 'punkt' only if not already downloaded
try:
nltk.data.find("tokenizers/punkt")
except LookupError:
nltk.download("punkt", download_dir=nltk_data_dir)
nltk.download("punkt_tab", download_dir=nltk_data_dir)
st.title("๐ Bayesian Token Co-occurrence Simulator")
# User input
user_input = st.text_area(
"โ๏ธ Enter your training sentences (one per line):",
"""
fido loves the red ball
timmy and fido go to the park
fido and timmy love to play
the red ball is timmy's favorite toy
""",
)
sentences = user_input.strip().split("\n")
tokenized = [word_tokenize(s.lower()) for s in sentences if s.strip()]
vocab = sorted(set(word for sentence in tokenized for word in sentence))
token2idx = {word: i for i, word in enumerate(vocab)}
idx2token = {i: word for word, i in token2idx.items()}
# Co-occurrence matrix
window_size = 2
matrix = np.zeros((len(vocab), len(vocab)))
for sentence in tokenized:
for i, word in enumerate(sentence):
for j in range(
max(0, i - window_size), min(len(sentence), i + window_size + 1)
):
if i != j:
matrix[token2idx[word]][token2idx[sentence[j]]] += 1
alpha = st.slider("๐ง Set Bayesian Prior (ฮฑ smoothing)", 0.0, 2.0, 0.1)
posterior = matrix + alpha
df = pd.DataFrame(posterior, index=vocab, columns=vocab)
st.subheader("๐ Co-occurrence Heatmap")
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(df, annot=True, cmap="Blues", fmt=".1f", ax=ax)
st.pyplot(fig)
# Next-token prediction
selected_word = st.selectbox("๐ฎ Predict next token after:", vocab)
row = posterior[token2idx[selected_word]]
probs = row / row.sum()
prediction = np.random.choice(vocab, p=probs)
st.markdown(f"**Predicted next token:** `{prediction}`")
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