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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)
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