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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) | |