Spaces:
Sleeping
Sleeping
| #------------------------------------------------------------------------ | |
| # Import Modules | |
| #------------------------------------------------------------------------ | |
| import streamlit as st | |
| import spacy | |
| import string | |
| from annotated_text import annotated_text | |
| from PIL import Image | |
| spacy.cli.download("en_core_web_sm") # Download and install the model | |
| # Load the English NLP model | |
| nlp = spacy.load("en_core_web_sm") | |
| #------------------------------------------------------------------------ | |
| # Configurations | |
| #------------------------------------------------------------------------ | |
| # Streamlit page setup | |
| # icon = Image.open("MTSS.ai_Icon.png") | |
| icon = Image.open("MTSS.ai_Icon.png") | |
| st.set_page_config( | |
| page_title="Kaleidoscope | Text Annotation", | |
| page_icon=icon, | |
| layout="centered", | |
| initial_sidebar_state="auto", | |
| menu_items={ | |
| 'About': "### *This application was created by* \n### LeVesseur Ph.D | MTSS.ai" | |
| } | |
| ) | |
| #------------------------------------------------------------------------ | |
| # Header | |
| #------------------------------------------------------------------------ | |
| # st.image('MTSS.ai_Logo.png', width=300) | |
| st.title('MTSS:grey[.ai]') | |
| st.header('Kaleidoscope:grey[ | Parts of Speech Annotation]') | |
| #------------------------------------------------------------------------ | |
| # Sidebar | |
| #------------------------------------------------------------------------ | |
| contact = st.sidebar.toggle('Handmade by \n**LeVesseur** :grey[ PhD] \n| :grey[MTSS.ai]') | |
| if contact: | |
| st.sidebar.write('Inquiries: [[email protected]](mailto:[email protected]) \nProfile: [levesseur.com](http://levesseur.com) \nCheck out: [InkQA | Dynamic PDFs](http://www.inkqa.com)') | |
| # Color options | |
| colors = { | |
| "Green (DAF1E7)": "#DAF1E7", | |
| "Blue (BDE5FF)": "#BDE5FF", | |
| "Navy (D1DBE9)": "#D1DBE9", | |
| "Teal (D6EAED)": "#D6EAED", | |
| "Iceburg (E4EEF6)": "#E4EEF6", | |
| "Vermillion (F6DCDD)": "#F6DCDD", | |
| } | |
| with st.sidebar: | |
| st.divider() | |
| # Sidebar display (Option 1: Color blocks with hex) | |
| st.sidebar.header("Recommended Colors") | |
| for color_name, hex_code in colors.items(): | |
| st.sidebar.color_picker(color_name, hex_code) | |
| st.subheader("Example") | |
| annotated_text( | |
| ("I", "Pronoun", "#F6DCDD"), | |
| " ", | |
| "really", | |
| " ", | |
| ("appreciate", "Verb", "#DAF1E7"), | |
| " ", | |
| ("all", "Pronoun", "#F6DCDD"), | |
| " ", | |
| ("that", "Pronoun", "#F6DCDD"), | |
| " ", | |
| "the", | |
| " ", | |
| ("social", "Adj", "#BDE5FF"), | |
| " ", | |
| "committee", | |
| " ", | |
| "has", | |
| " ", | |
| ("done", "Verb", "#DAF1E7"), | |
| " ", | |
| "to", | |
| " ", | |
| ("keep", "Verb", "#DAF1E7"), | |
| " ", | |
| ("us", "Pronoun", "#F6DCDD"), | |
| " ", | |
| ("feeling", "Verb", "#DAF1E7"), | |
| " ", | |
| ("connected", "Adj", "#BDE5FF"), | |
| " ", | |
| ".", | |
| " ", | |
| "I", | |
| " ", | |
| "also", | |
| " ", | |
| "really", | |
| " ", | |
| ("value", "Verb", "#DAF1E7"), | |
| " ", | |
| ("our", "Pronoun", "#F6DCDD"), | |
| " ", | |
| "in", | |
| " ", | |
| "-person", | |
| " ", | |
| ("meetings", "Noun", "#D1DBE9"), | |
| " ", | |
| "and", | |
| " ", | |
| "the", | |
| " ", | |
| "social", | |
| " ", | |
| ("opportunities", "Noun", "#D1DBE9"), | |
| " ", | |
| ("built", "Verb", "#DAF1E7"), | |
| " ", | |
| "into", | |
| " ", | |
| "these", | |
| " ", | |
| "meetings", | |
| " ", | |
| ".", | |
| ) | |
| st.divider() | |
| st.subheader("Directions for Using the Text Annotation Tool") | |
| directions = """ | |
| 1. **Enter Your Text**: | |
| - Type the text you want to annotate in the text area provided. | |
| 2. **Select Parts of Speech**: | |
| - Choose which parts of speech you want to include in the annotation by checking the corresponding boxes (e.g., Verbs, Adjectives, Nouns, Pronouns). | |
| 3. **Submit Your Text**: | |
| - Click the "Submit Text" button to process your input. The app will automatically label and color the words based on the selected parts of speech. | |
| 4. **Review the Annotations**: | |
| - The annotated text will be displayed, showing the parts of speech labels and colors applied to the words. | |
| 5. **Adjust Annotations (Optional)**: | |
| - You can manually adjust the labels and colors for each word if needed. | |
| 6. **Generate Annotated Text**: | |
| - After reviewing and adjusting the annotations, click the "Generate Annotated Text" button. | |
| - The final annotated text will be displayed. | |
| 7. **Take a Screenshot**: | |
| - To use the annotated text, take a screenshot of the displayed text. | |
| 8. **Adjust Text Width** (Optional): | |
| - If you want to adjust the width of the sentences for a better screenshot, minimize or resize your browser window accordingly before taking the screenshot. | |
| """ | |
| st.markdown(directions) | |
| #------------------------------------------------------------------------ | |
| # Functions: Parts of Speech | |
| #------------------------------------------------------------------------ | |
| # # Function to split text into words | |
| # def split_text(text): | |
| # # Add a space before punctuation marks | |
| # for char in string.punctuation: | |
| # text = text.replace(char, f" {char}") | |
| # return text.split() | |
| # # Function to automatically label and color words based on parts of speech | |
| # def auto_label_and_color_words(doc, words): | |
| # labels = [""] * len(words) | |
| # colors = ["#FFFFFF"] * len(words) | |
| # word_positions = {i: word for i, word in enumerate(words)} | |
| # for token in doc: | |
| # # Match token with the words from the original text | |
| # for index, word in word_positions.items(): | |
| # if token.text == word: | |
| # if token.pos_ == "VERB": | |
| # labels[index] = "Verb" | |
| # colors[index] = "#DAF1E7" | |
| # elif token.pos_ == "ADJ": | |
| # labels[index] = "Adj" | |
| # colors[index] = "#BDE5FF" | |
| # elif token.pos_ == "NOUN": | |
| # labels[index] = "Noun" | |
| # colors[index] = "#D1DBE9" | |
| # elif token.pos_ == "PRON": | |
| # labels[index] = "Pronoun" | |
| # colors[index] = "#F6DCDD" | |
| # break # Exit loop once the word is found and processed | |
| # return labels, colors | |
| # # Main Streamlit application | |
| # st.title("Text Annotation Tool") | |
| # # Initialize session state to store text and annotations | |
| # if 'user_text' not in st.session_state: | |
| # st.session_state.user_text = "" | |
| # if 'words' not in st.session_state: | |
| # st.session_state.words = [] | |
| # if 'labels' not in st.session_state: | |
| # st.session_state.labels = [] | |
| # if 'colors' not in st.session_state: | |
| # st.session_state.colors = [] | |
| # if 'extracted_pos' not in st.session_state: | |
| # st.session_state.extracted_pos = {} | |
| # # User input for the text | |
| # user_text = st.text_area("Enter the text you want to annotate:", value=st.session_state.user_text, height=100) | |
| # # Button to process the text | |
| # if st.button("Submit Text"): | |
| # st.session_state.user_text = user_text | |
| # st.session_state.words = split_text(user_text) | |
| # # Process the text with spaCy | |
| # doc = nlp(user_text) | |
| # # Automatically label and color words based on parts of speech | |
| # st.session_state.labels, st.session_state.colors = auto_label_and_color_words(doc, st.session_state.words) | |
| # # Extract parts of speech | |
| # st.session_state.extracted_pos = { | |
| # "verbs": [token.text for token in doc if token.pos_ == "VERB"], | |
| # "adjectives": [token.text for token in doc if token.pos_ == "ADJ"], | |
| # "nouns": [token.text for token in doc if token.pos_ == "NOUN"], | |
| # "pronouns": [token.text for token in doc if token.pos_ == "PRON"] | |
| # } | |
| # # Display extracted parts of speech | |
| # if st.session_state.extracted_pos: | |
| # st.subheader("Extracted Parts of Speech") | |
| # st.write("**Verbs:**", st.session_state.extracted_pos.get("verbs", [])) | |
| # st.write("**Adjectives:**", st.session_state.extracted_pos.get("adjectives", [])) | |
| # st.write("**Nouns:**", st.session_state.extracted_pos.get("nouns", [])) | |
| # st.write("**Pronouns:**", st.session_state.extracted_pos.get("pronouns", [])) | |
| # # Collect annotation inputs for each word | |
| # if st.session_state.words: | |
| # for i, word in enumerate(st.session_state.words): | |
| # st.write(f"Annotate the word: {word}") | |
| # st.session_state.labels[i] = st.selectbox( | |
| # f"Label for '{word}'", ["", "Verb", "Adj", "Noun", "Pronoun"], | |
| # key=f"label_{i}", index=["", "Verb", "Adj", "Noun", "Pronoun"].index(st.session_state.labels[i]) | |
| # ) | |
| # st.session_state.colors[i] = st.color_picker( | |
| # f"Color for '{word}'", | |
| # value=st.session_state.colors[i], | |
| # key=f"color_{i}" | |
| # ) | |
| # # Generate button to process the annotations | |
| # if st.button("Generate Annotated Text"): | |
| # annotated_elements = [] | |
| # for i, word in enumerate(st.session_state.words): | |
| # if st.session_state.labels[i] and st.session_state.colors[i] != "#FFFFFF": | |
| # annotated_elements.append((word, st.session_state.labels[i], st.session_state.colors[i])) | |
| # else: | |
| # annotated_elements.append(word) | |
| # annotated_elements.append(" ") # Add space between words | |
| # # Remove the last extra space added | |
| # if annotated_elements and annotated_elements[-1] == " ": | |
| # annotated_elements.pop() | |
| # # Display the annotated text using the `annotated_text` function | |
| # st.subheader("Annotated Text:") | |
| # annotated_text(*annotated_elements) | |
| # # Print the code for the annotated text | |
| # st.subheader("Generated Code:") | |
| # code_str = 'annotated_text(\n' | |
| # for elem in annotated_elements: | |
| # if isinstance(elem, tuple): | |
| # code_str += f' ("{elem[0]}", "{elem[1]}", "{elem[2]}"),\n' | |
| # else: | |
| # code_str += f' "{elem}",\n' | |
| # code_str += ')' | |
| # st.code(code_str, language='python') | |
| #------------------------------------------------------------------------ | |
| # Functions: Parts of Speech + Buttons | |
| #------------------------------------------------------------------------ | |
| # Function to split text into words | |
| def split_text(text): | |
| # Add a space before punctuation marks | |
| for char in string.punctuation: | |
| text = text.replace(char, f" {char}") | |
| return text.split() | |
| # Function to automatically label and color words based on parts of speech | |
| def auto_label_and_color_words(doc, words, include_verbs, include_adjectives, include_nouns, include_pronouns): | |
| labels = [""] * len(words) | |
| colors = ["#FFFFFF"] * len(words) | |
| word_positions = {i: word for i, word in enumerate(words)} | |
| for token in doc: | |
| # Match token with the words from the original text | |
| for index, word in word_positions.items(): | |
| if token.text == word: | |
| if token.pos_ == "VERB" and include_verbs: | |
| labels[index] = "Verb" | |
| colors[index] = "#DAF1E7" | |
| elif token.pos_ == "ADJ" and include_adjectives: | |
| labels[index] = "Adj" | |
| colors[index] = "#BDE5FF" | |
| elif token.pos_ == "NOUN" and include_nouns: | |
| labels[index] = "Noun" | |
| colors[index] = "#D1DBE9" | |
| elif token.pos_ == "PRON" and include_pronouns: | |
| labels[index] = "Pronoun" | |
| colors[index] = "#F6DCDD" | |
| break # Exit loop once the word is found and processed | |
| return labels, colors | |
| # Initialize session state to store text and annotations | |
| if 'user_text' not in st.session_state: | |
| st.session_state.user_text = "" | |
| if 'words' not in st.session_state: | |
| st.session_state.words = [] | |
| if 'labels' not in st.session_state: | |
| st.session_state.labels = [] | |
| if 'colors' not in st.session_state: | |
| st.session_state.colors = [] | |
| if 'extracted_pos' not in st.session_state: | |
| st.session_state.extracted_pos = {} | |
| # User input for the text | |
| user_text = st.text_area("Enter the text you want to annotate:", value=st.session_state.user_text, height=100) | |
| # Checkboxes for parts of speech to include | |
| include_verbs = st.checkbox("Include Verbs", value=True) | |
| include_adjectives = st.checkbox("Include Adjectives", value=True) | |
| include_nouns = st.checkbox("Include Nouns", value=True) | |
| include_pronouns = st.checkbox("Include Pronouns", value=True) | |
| # Button to process the text | |
| if st.button("Submit Text"): | |
| st.session_state.user_text = user_text | |
| st.session_state.words = split_text(user_text) | |
| # Process the text with spaCy | |
| doc = nlp(user_text) | |
| # Automatically label and color words based on parts of speech | |
| st.session_state.labels, st.session_state.colors = auto_label_and_color_words( | |
| doc, st.session_state.words, include_verbs, include_adjectives, include_nouns, include_pronouns) | |
| # Extract parts of speech | |
| st.session_state.extracted_pos = { | |
| "verbs": [token.text for token in doc if token.pos_ == "VERB"], | |
| "adjectives": [token.text for token in doc if token.pos_ == "ADJ"], | |
| "nouns": [token.text for token in doc if token.pos_ == "NOUN"], | |
| "pronouns": [token.text for token in doc if token.pos_ == "PRON"] | |
| } | |
| # Display extracted parts of speech | |
| if st.session_state.extracted_pos: | |
| st.subheader("Extracted Parts of Speech") | |
| st.write("**Verbs:**", st.session_state.extracted_pos.get("verbs", [])) | |
| st.write("**Adjectives:**", st.session_state.extracted_pos.get("adjectives", [])) | |
| st.write("**Nouns:**", st.session_state.extracted_pos.get("nouns", [])) | |
| st.write("**Pronouns:**", st.session_state.extracted_pos.get("pronouns", [])) | |
| # Collect annotation inputs for each word | |
| if st.session_state.words: | |
| for i, word in enumerate(st.session_state.words): | |
| st.write(f"Annotate the word: {word}") | |
| st.session_state.labels[i] = st.selectbox( | |
| f"Label for '{word}'", ["", "Verb", "Adj", "Noun", "Pronoun"], | |
| key=f"label_{i}", index=["", "Verb", "Adj", "Noun", "Pronoun"].index(st.session_state.labels[i]) | |
| ) | |
| st.session_state.colors[i] = st.color_picker( | |
| f"Color for '{word}'", | |
| value=st.session_state.colors[i], | |
| key=f"color_{i}" | |
| ) | |
| # Generate button to process the annotations | |
| if st.button("Generate Annotated Text", type="primary"): | |
| annotated_elements = [] | |
| for i, word in enumerate(st.session_state.words): | |
| if st.session_state.labels[i] and st.session_state.colors[i] != "#FFFFFF": | |
| annotated_elements.append((word, st.session_state.labels[i], st.session_state.colors[i])) | |
| else: | |
| annotated_elements.append(word) | |
| annotated_elements.append(" ") # Add space between words | |
| # Remove the last extra space added | |
| if annotated_elements and annotated_elements[-1] == " ": | |
| annotated_elements.pop() | |
| # Display the annotated text using the `annotated_text` function | |
| st.subheader("Annotated Text:") | |
| annotated_text(*annotated_elements) | |
| # Print the code for the annotated text | |
| st.subheader("Generated Code:") | |
| code_str = 'annotated_text(\n' | |
| for elem in annotated_elements: | |
| if isinstance(elem, tuple): | |
| code_str += f' ("{elem[0]}", "{elem[1]}", "{elem[2]}"),\n' | |
| else: | |
| code_str += f' "{elem}",\n' | |
| code_str += ')' | |
| st.code(code_str, language='python') |