import streamlit as st from transformers import pipeline from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForCausalLM from transformers import T5Tokenizer, T5ForConditionalGeneration st.subheader('Pipe5: Text-To-Text Generation -> Que. Generation',divider='orange') if st.toggle(label='Show Pipe5'): models = [ 'google/flan-t5-base', 'meta-llama/Meta-Llama-3-8B', 'meta-llama/Meta-Llama-3-8B-Instruct' ] model_name = st.selectbox( label='Select Model', options=models, placeholder='google/vit-base-patch16-224', ) if model_name == models[0]: tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) else: tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) input_text = st.text_area(label='Enter the text from which question is to be generated:',value='Bruce Wayne is the Batman.') input_text = 'Generate a question from this: ' + input_text input_ids = tokenizer(input_text, return_tensors='pt').input_ids outputs = model.generate(input_ids) output_text = tokenizer.decode(outputs[0][1:len(outputs[0])-1]) if st.checkbox(label='Show Tokenized output'): st.write(outputs) st.write("Output is:") st.write(f"{output_text}") if st.toggle(label='Access model unrestricted'): input_text = st.text_area('Enter text') input_ids = tokenizer(input_text, return_tensors='pt').input_ids outputs = model.generate(input_ids) st.write(tokenizer.decode(outputs[0])) st.write(outputs)