import os os.system('pip install transformers','pip install torch torchvision torchaudio') import torch print(torch.__version__) import streamlit as st from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Choose your desired free model from the Hugging Face Hub model_name = "t5-small" # Replace with your choice (e.g., facebook/bart-base or EleutherAI/gpt-neo-125M) # Load the model and tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) # From here down is all the StreamLit UI. st.set_page_config(page_title="LangChain Demo", page_icon=":robot:") st.header("Hey, I'm your Chat GPT") if "sessionMessages" not in st.session_state: st.session_state.sessionMessages = [ SystemMessage(content="You are a helpful assistant.") ] def load_answer(question): st.session_state.sessionMessages.append(HumanMessage(content=question)) inputs = tokenizer(question, return_tensors="pt") outputs = model.generate(**inputs) response_text = tokenizer.decode(outputs[0], skip_special_tokens=True) st.session_state.sessionMessages.append(AIMessage(content=assistant_answer.content)) return assistant_answer.content def get_text(): input_text = st.text_input("You: ", key= input) return input_text user_input=get_text() submit = st.button('Generate') if submit: response = load_answer(user_input) st.subheader("Answer:") st.write(response,key= 1)