# 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") ################ 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 = "gpt-3.5-turbo" # "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("

Chat with GPT

", 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 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.title("Chat with OpenAssistant/LLaMA") # 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")