app.py

import streamlit as st
import pandas as pd
import datetime
import io
import nltk
import base64
import os
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

nltk.download('punkt')
nltk.download('stopwords')

def generate_file_name(text, file_type):
    try:
        # Tokenize the text into sentences
        sentences = sent_tokenize(text)
        
        # Tokenize the sentences into words and remove stopwords
        words = [word.lower() for sentence in sentences for word in word_tokenize(sentence) if word.isalnum()]
        stop_words = set(stopwords.words('english'))
        filtered_words = [word for word in words if word not in stop_words]
        
        # Count word frequencies
        word_freq = nltk.FreqDist(filtered_words)
        
        # Get the top 3 most frequent words
        top_words = [word for word, _ in word_freq.most_common(3)]
        
        # Generate the file name
        current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        file_name = f"{'-'.join(top_words)}_{current_time}.{file_type}"
        return file_name
    except:
        # Fallback to default file naming if an error occurs
        current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        file_name = f"text_file_{current_time}.{file_type}"
        return file_name

def save_text_as_file(text, file_type):
    file_name = generate_file_name(text, 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 = generate_file_name(text, "xlsx")
    df.to_excel(file_name, index=False)
    st.success(f"Character list saved as {file_name}")
    return file_name

@st.cache_resource
def get_download_link(file_path):
    try:
        with open(file_path, 'rb') as file:
            data = file.read()
            b64 = base64.b64encode(data).decode()
            file_name = os.path.basename(file_path)
            ext = os.path.splitext(file_name)[1]  # get the file extension
            if ext == '.xlsx':
                mime_type = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
            elif ext == '.csv':
                mime_type = 'text/csv'
            elif ext == '.md':
                mime_type = 'text/markdown'
            else:
                mime_type = 'application/octet-stream'  # general binary data type
            href = f'<a href="data:{mime_type};base64,{b64}" download="{file_name}">{file_name}</a>'
            return href
    except:
        return ''

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": get_download_link(file), "Size (bytes)": file_size, "Last Modified": file_modified_time})
    files_df = pd.DataFrame(files)
    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), unsafe_allow_html=True)
                    st.markdown(get_download_link(file_name.replace(".xlsx", ".csv")), unsafe_allow_html=True)
                    st.markdown(get_download_link(file_name.replace(".xlsx", ".md")), unsafe_allow_html=True)
                except:
                    pass

    show_files_in_directory()

if __name__ == "__main__":
    main()