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import streamlit as st
import pandas as pd
import datetime
import io
import nltk
import base64
import os
from nltk.tokenize import sent_tokenize
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
nltk.download('punkt')
def save_text_as_file(text, file_type):
current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
file_name = f"text_file_{current_time}.{file_type}"
with open(file_name, "w") as file:
file.write(text)
st.success(f"Text saved as {file_name}")
return file_name
def save_list_as_excel(text):
lines = text.split("\n")
data = []
for line in lines:
if line.strip():
parts = line.split(" - ", 1)
if len(parts) == 2:
data.append(parts)
else:
data.append([line.strip(), ""])
df = pd.DataFrame(data, columns=["Character", "Description"])
file_name = f"character_list_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
df.to_excel(file_name, index=False)
st.success(f"Character list saved as {file_name}")
return file_name
def get_download_link(file_path, file_type):
with open(file_path, 'rb') as f:
data = f.read()
b64 = base64.b64encode(data).decode()
if file_type == "xlsx":
href = f'<a href="data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}" download="{file_path}">Download {file_path}</a>'
elif file_type == "csv":
href = f'<a href="data:text/csv;base64,{b64}" download="{file_path}">Download {file_path}</a>'
elif file_type == "md":
href = f'<a href="data:text/markdown;base64,{b64}" download="{file_path}">Download {file_path}</a>'
return href
def perform_nlp(text):
sentences = sent_tokenize(text)
# Topic Modeling
vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(sentences)
lda = LatentDirichletAllocation(n_components=3, random_state=42)
lda.fit(X)
topics = lda.transform(X)
# Display topics
st.subheader("Topic Modeling")
for i, topic in enumerate(topics):
st.write(f"Topic {i+1}:")
topic_words = ", ".join([vectorizer.get_feature_names_out()[i] for i in topic.argsort()[:-6:-1]])
st.write(topic_words)
# Word Frequency
word_freq = pd.Series(" ".join(sentences).split()).value_counts()[:10]
st.subheader("Word Frequency")
st.bar_chart(word_freq)
def show_files_in_directory():
st.subheader("Files in Current Directory")
files = []
for file in os.listdir("."):
if file.endswith((".md", ".xlsx", ".csv")):
file_size = os.path.getsize(file)
file_modified_time = datetime.datetime.fromtimestamp(os.path.getmtime(file)).strftime("%Y-%m-%d %H:%M:%S")
files.append({"File Name": file, "Size (bytes)": file_size, "Last Modified": file_modified_time})
files_df = pd.DataFrame(files)
files_df["File Name"] = files_df["File Name"].apply(lambda x: f'<a href="{x}" download>{x}</a>')
st.write(files_df.to_html(escape=False, index=False), unsafe_allow_html=True)
def main():
st.title("AI UI for Text Processing")
text_input = st.text_area("Paste your text here")
if st.button("Process Text"):
if text_input.strip() == "":
st.warning("Please paste some text.")
else:
file_name = None
if text_input.strip().startswith(("1.", "1 -", "1 _")) and "\n" in text_input:
file_name = save_list_as_excel(text_input)
save_text_as_file(text_input, "csv")
save_text_as_file(text_input, "md")
elif "." in text_input or "!" in text_input or "?" in text_input:
file_name = save_text_as_file(text_input, "txt")
save_text_as_file(text_input, "csv")
save_text_as_file(text_input, "md")
perform_nlp(text_input)
else:
file_name = save_text_as_file(text_input, "txt")
save_text_as_file(text_input, "csv")
save_text_as_file(text_input, "md")
if file_name:
try:
df = pd.read_excel(file_name)
st.subheader("Saved Data")
st.dataframe(df)
st.markdown(get_download_link(file_name, "xlsx"), unsafe_allow_html=True)
st.markdown(get_download_link(file_name.replace(".xlsx", ".csv"), "csv"), unsafe_allow_html=True)
st.markdown(get_download_link(file_name.replace(".xlsx", ".md"), "md"), unsafe_allow_html=True)
except:
pass
show_files_in_directory()
if __name__ == "__main__":
main() |