| # Natural Language Tools | |
| # Richard Orama - September 2024 | |
| #x = st.slider('Select a value') | |
| #st.write(x, 'squared is', x * x) | |
| import streamlit as st | |
| from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel | |
| import ast | |
| st.title("Assorted Language Tools - AI Craze") | |
| ########################################################## | |
| import streamlit as st | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| # Load the summarization model and tokenizer | |
| MODEL_NAME = "facebook/bart-large-cnn" # A commonly used summarization model | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) | |
| # Streamlit UI for input | |
| st.title("Text Summarization with BART") | |
| # Input text area for the article | |
| article = st.text_area("Enter the text you want to summarize", height=300) | |
| # Summarize button | |
| if st.button("Summarize"): | |
| if article: | |
| # Tokenize input article | |
| inputs = tokenizer(article, return_tensors="pt", truncation=True, padding="longest", max_length=1024) | |
| # Generate summary | |
| summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True) | |
| # Decode summary | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| # Display the summary | |
| st.write("**Summary:**") | |
| st.write(summary) | |
| else: | |
| st.warning("Please enter some text to summarize!") | |
| ################ STATEMENT SUMMARIZATION ################# | |
| # Load the summarization model | |
| #summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6") # smaller version of the model | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| # Define the summarization function | |
| def summarize_statement(txt): | |
| st.write('\n\n') | |
| #st.write(txt[:100]) # Display the first 100 characters of the article | |
| #st.write('--------------------------------------------------------------') | |
| summary = summarizer(txt, max_length=500, min_length=30, do_sample=False) | |
| st.write(summary[0]['summary_text']) | |
| DEFAULT_STATEMENT = "" | |
| # Create a text area for user input | |
| STATEMENT = st.sidebar.text_area('Enter Statement (String)', DEFAULT_STATEMENT, height=150) | |
| # Enable the button only if there is text in the SENTIMENT variable | |
| if STATEMENT: | |
| if st.sidebar.button('Summarize Statement'): | |
| # Call your Summarize function here | |
| summarize_statement(STATEMENT) # Directly pass the STATEMENT | |
| else: | |
| st.sidebar.button('Summarize Statement', disabled=True) | |
| st.warning('👈 Please enter Statement!') | |
| ################ SENTIMENT ANALYSIS ################# | |
| # Initialize the sentiment analysis pipeline | |
| # No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english | |
| sentiment_pipeline = pipeline("sentiment-analysis") | |
| def is_valid_list_string(string): | |
| try: | |
| result = ast.literal_eval(string) | |
| return isinstance(result, list) | |
| except (ValueError, SyntaxError): | |
| return False | |
| # Define the summarization function | |
| def analyze_sentiment(txt): | |
| st.write('\n\n') | |
| #st.write(txt[:100]) # Display the first 100 characters of the article | |
| #st.write('--------------------------------------------------------------') | |
| # Display the results | |
| if is_valid_list_string(txt): | |
| txt_converted = ast.literal_eval(txt) #convert string to actual content, e.g. list | |
| # Perform Hugging sentiment analysis on multiple texts | |
| results = sentiment_pipeline(txt_converted) | |
| for i, text in enumerate(txt_converted): | |
| st.write(f"Text: {text}") | |
| st.write(f"Sentiment: {results[i]['label']}, Score: {results[i]['score']:.2f}\n") | |
| else: | |
| # Perform Hugging sentiment analysis on multiple texts | |
| results = sentiment_pipeline(txt) | |
| st.write(f"Text: {txt}") | |
| st.write(f"Sentiment: {results[0]['label']}, Score: {results[0]['score']:.2f}\n") | |
| DEFAULT_SENTIMENT = "" | |
| # Create a text area for user input | |
| SENTIMENT = st.sidebar.text_area('Enter Sentiment (String or List of Strings)', DEFAULT_SENTIMENT, height=150) | |
| # Enable the button only if there is text in the SENTIMENT variable | |
| if SENTIMENT: | |
| if st.sidebar.button('Analyze Sentiment'): | |
| analyze_sentiment(SENTIMENT) # Directly pass the SENTIMENT | |
| else: | |
| st.sidebar.button('Analyze Sentiment', disabled=True) | |
| st.warning('👈 Please enter Sentiment!') | |
| # ################ CHAT BOT ################# | |
| # # Load the GPT model | |
| # generator = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B") | |
| # # Streamlit chat UI | |
| # #st.title("GPT-3 Chatbox") | |
| # # user_input = st.text_input("You: ", "Hello, how are you?") | |
| # # if user_input: | |
| # # response = generator(user_input, max_length=100, num_return_sequences=1)[0]['generated_text'] | |
| # # st.write(f"GPT-3: {response}") | |
| # # Define the summarization function | |
| # def chat(txt): | |
| # st.write('\n\n') | |
| # #st.write(txt[:100]) # Display the first 100 characters of the article | |
| # #st.write('--------------------------------------------------------------') | |
| # #summary = summarizer(txt, max_length=500, min_length=30, do_sample=False) | |
| # #st.write(summary[0]['summary_text']) | |
| # response = generator(txt, max_length=500, num_return_sequences=1)[0]['generated_text'] | |
| # st.write(f"GPT-3: {response}") | |
| # DEFAULT_CHAT = "" | |
| # # Create a text area for user input | |
| # CHAT = st.sidebar.text_area('Enter Chat (String)', DEFAULT_CHAT, height=150) | |
| # # Enable the button only if there is text in the CHAT variable | |
| # if CHAT: | |
| # if st.sidebar.button('Chat Statement'): | |
| # # Call your Summarize function here | |
| # chat(CHAT) # Directly pass the your | |
| # else: | |
| # st.sidebar.button('Chat Statement', disabled=True) | |
| # st.warning('👈 Please enter Chat!') | |
| # Load pre-trained GPT-2 model and tokenizer | |
| model_name = "gpt2-medium" # "gpt2" # Use "gpt-3.5-turbo" or another model from Hugging Face if needed | |
| model = GPT2LMHeadModel.from_pretrained(model_name) | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_name) | |
| # Initialize the text generation pipeline | |
| gpt_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| # Streamlit UI | |
| st.markdown("<h3 style='text-align: center; font-size: 20px;'>Chat with GPT</h3>", unsafe_allow_html=True) | |
| if 'conversation' not in st.session_state: | |
| st.session_state.conversation = "" | |
| def chat_with_gpt(user_input): | |
| # Append user input to the conversation | |
| st.session_state.conversation += f"User: {user_input}\n" | |
| # Generate response | |
| response = gpt_pipeline(user_input, max_length=100, num_return_sequences=1)[0]['generated_text'] | |
| response_text = response.replace(user_input, '') # Strip the user input part from response | |
| # Append GPT's response to the conversation | |
| st.session_state.conversation += f"GPT: {response_text}\n" | |
| return response_text | |
| # Text input for user query | |
| user_input = st.text_input("You:", "") | |
| if st.button("Send"): | |
| if user_input: | |
| chat_with_gpt(user_input) | |
| # Display conversation history | |
| st.text_area("Conversation", value=st.session_state.conversation, height=400) | |
| ############# | |
| # # LLaMA 7B model from Hugging Face | |
| # # MODEL_NAME = "huggyllama/llama-7b" # Example of a LLaMA model | |
| # # Try this OpenAssistant model available on Hugging Face | |
| # MODEL_NAME = "OpenAssistant/oasst-sft-1-pythia-12b" # Example of an OpenAssistant model | |
| # import streamlit as st | |
| # from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # import torch | |
| # # Load the model and tokenizer (OpenAssistant or LLaMA) | |
| # MODEL_NAME = "OpenAssistant/oasst-sft-1-pythia-12b" # Replace with "huggyllama/llama-7b" for LLaMA | |
| # tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| # model = AutoModelForCausalLM.from_pretrained(MODEL_NAME) | |
| # # Streamlit UI for input | |
| # st.markdown("<h3 style='text-align: center; font-size: 20px;'>Chat with OpenAssistant/LLaMA</h3>", unsafe_allow_html=True) | |
| # # Input text area | |
| # user_input = st.text_area("You:", "", height=150) | |
| # if st.button('Generate Response'): | |
| # if user_input: | |
| # # Tokenize the input and generate response | |
| # inputs = tokenizer(user_input, return_tensors="pt") | |
| # outputs = model.generate(**inputs, max_length=150) | |
| # # Decode the generated response | |
| # response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # # Display the model's response | |
| # st.write("Assistant: ", response) | |
| # else: | |
| # st.warning('Please enter some text to get a response!') | |
| # ################ END ################# | |
| # Add a footnote at the bottom | |
| st.markdown("---") # Horizontal line to separate content from footnote | |
| st.markdown("Orama's AI Craze") |