Create app.py

app.py

@@ -0,0 +1,153 @@
+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()