# Simply Assorted Language Tools (SALT) # 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") st.markdown("

O - S A L T

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Orama's Selectively Assorted Language Tools

", unsafe_allow_html=True) #st.markdown("---") # Horizontal line to separate content from the rest #st.markdown("

Orama's AI Craze

", unsafe_allow_html=True) ################ SENTIMENT ANALYSIS - side bar - pippeline ################# # 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.sidebar.write(f"Text: {text}") st.sidebar.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.sidebar.write(f"Text: {txt}") st.sidebar.write(f"Sentiment: {results[0]['label']}, Score: {results[0]['score']:.2f}\n") st.sidebar.markdown("

Sentiment Analysis - Pipeline

", unsafe_allow_html=True) 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!') ################ STATEMENT SUMMARIZATION - side bar - tokenizer ################# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # 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) st.sidebar.markdown("

Text Summarization - BART Tokenizer

", unsafe_allow_html=True) DEFAULT_STATEMENT = "" # Create a text area for user input STATEMENT = st.sidebar.text_area('Enter Statement (String1)', DEFAULT_STATEMENT, height=150) # Enable the button only if there is text in the SENTIMENT variable if STATEMENT: if st.sidebar.button('Summarize Statement1'): # Call your Summarize function here # summarize_statement(STATEMENT) # Directly pass the STATEMENT # Tokenize input article inputs = tokenizer(STATEMENT, 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.sidebar.write("**Summary:**") st.sidebar.write(summary) else: st.sidebar.button('Summarize Statement1', disabled=True) #st.warning('👈 Please enter Statement!') ################ STATEMENT SUMMARIZATION - side bar - pipeline ################# # 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") st.sidebar.markdown("

Text Summarization - BART Pipeline

", unsafe_allow_html=True) DEFAULT_STATEMENT = "" # Create a text area for user input STATEMENT = st.sidebar.text_area('Enter Statement (String2)', DEFAULT_STATEMENT, height=150) # Enable the button only if there is text in the SENTIMENT variable if STATEMENT: if st.sidebar.button('Summarize Statement2'): # Call your Summarize function here #st.write('\n\n') summary = summarizer(STATEMENT, max_length=500, min_length=30, do_sample=False) st.sidebar.write(summary[0]['summary_text']) #summarize_statement(STATEMENT) # Directly pass the STATEMENT else: st.sidebar.button('Summarize Statement2', disabled=True) #st.warning('👈 Please enter Statement!') ################ TEXT TO SPEECH (TTS) - side bar - pipeline ################# from transformers import pipeline import sounddevice as sd # Import for audio playback (optional) # Load the pipeline tts = pipeline("text-to-speech") st.sidebar.markdown("

TTS - Pipeline

", unsafe_allow_html=True) DEFAULT_STATEMENT = "This is a sample text to be converted to speech." # Create a text area for user input STATEMENT = st.sidebar.text_area('Enter Text', DEFAULT_STATEMENT, height=150) # Enable the button only if there is text in the TTS variable if STATEMENT: if st.sidebar.button('TTS'): # Text to generate speech from text = STATEMENT # Use the user input from STATEMENT # Generate speech speech = tts(text) # Access the audio waveform from the dictionary (assuming key name is 'waveform') audio_data = speech['waveform'] # Optional: Save the audio to a file (uncomment if needed) # sd.write(audio_data, samplerate=speech['sampling_rate']) # Adjust samplerate if necessary # with open("sample_tts.wav", "wb") as f: # f.write(audio_data) # Optional: Play the audio directly in Streamlit (uncomment if needed) sd.play(audio_data, samplerate=speech['sampling_rate']) # Adjust samplerate if necessary st.sidebar.write('Text converted to speech') else: st.sidebar.button('TTS', disabled=True) # st.warning(' Please enter Statement!') # ################ CHAT BOT - main area ################# # # 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("

Chat - gpt2-medium

", 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("

Chat with OpenAssistant/LLaMA

", 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

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