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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) | |