app.py

import streamlit as st
import pandas as pd
import numpy as np

st.title("My First Streamlit App")

# Create a simple slider
number = st.slider("Pick a number", 0, 100)

# Display a message based on the slider value
st.write(f"You picked: {number}")

# Create a dataframe and display it
data = pd.DataFrame({
    'Column 1': np.random.rand(10),
    'Column 2': np.random.rand(10)
})

st.write(data)

# Create a line chart
st.line_chart(data)


import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from nltk.tokenize import word_tokenize
import nltk

nltk.download('punkt')


st.title("📊 Bayesian Token Co-occurrence Simulator")

# User input
user_input = st.text_area("✍️ 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("🔧 Set Bayesian Prior (α smoothing)", 0.0, 2.0, 0.1)
posterior = matrix + alpha

df = pd.DataFrame(posterior, index=vocab, columns=vocab)
st.subheader("📈 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("🔮 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}`")