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import streamlit as st
import torch
import torch.nn as nn
import nltk
from nltk.corpus import stopwords
import pandas as pd
import base64
import random
# Ensure NLTK resources are downloaded
nltk.download('punkt')
nltk.download('stopwords')
# Function to perform convolution on text data
def text_convolution(input_text, kernel_size=3):
words = nltk.word_tokenize(input_text)
words = [word for word in words if word not in stopwords.words('english')]
tensor_input = torch.tensor([hash(word) for word in words], dtype=torch.float)
conv_layer = nn.Conv1d(1, 1, kernel_size, stride=1)
tensor_input = tensor_input.view(1, 1, -1)
output = conv_layer(tensor_input)
return output, words
# Function to color the bars based on whether they appear together or not
def color_bars(words):
color_map = {}
color_index = 0
for word in words:
if word not in color_map:
color_map[word] = color_index
color_index += 1
colors = [f"#{random.randint(0, 0xFFFFFF):06x}" for _ in range(len(color_map))]
return [colors[color_map[word]] for word in words]
# Streamlit UI
def main():
st.title("Text Convolution Demonstration")
st.write("This app demonstrates how text convolution works. Upload a text file and see the convolution result along with a distribution plot of word tokens.")
uploaded_file = st.file_uploader("Choose a text file (TXT only)", type=["txt"])
user_email = st.text_input("Enter your email to save your prompts:")
if uploaded_file is not None and user_email:
text_data = uploaded_file.read().decode("utf-8")
conv_result, words = text_convolution(text_data)
st.write("Convolution result:", conv_result)
# Visualization
word_counts = pd.Series(words).value_counts()
word_counts = word_counts.sort_values(ascending=False)
colors = color_bars(word_counts.index)
st.bar_chart(word_counts.head(20), color=colors)
# Saving user prompts
user_file_name = f"{user_email}_prompts.txt"
with open(user_file_name, "a") as file:
file.write(text_data + "\n")
st.success(f"Your prompts have been added to {user_file_name}")
# Download link for the file
with open(user_file_name, "rb") as f:
b64 = base64.b64encode(f.read()).decode()
href = f'<a href="data:file/txt;base64,{b64}" download="{user_file_name}">Download {user_file_name}</a>'
st.markdown(href, unsafe_allow_html=True)
if __name__ == "__main__":
main()
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