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import streamlit as st
# From here down is all the StreamLit UI.
st.set_page_config(page_title="LangChain Demo", page_icon=":robot:") # Call it first
st.header("Hey, I'm your Chat GPT")
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Define SystemMessage class if needed
class SystemMessage:
def __init__(self, content):
self.content = content
# Choose your desired free model from the Hugging Face Hub
model_name = "t5-small" # Replace with your choice (e.g.,t5-small or 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)
system_message = SystemMessage("Welcome to my Chatbot! Ask me anything.")
st.write(system_message)
if "sessionMessages" not in st.session_state:
st.session_state.sessionMessages = [
SystemMessage(content="You are a helpful assistant.")
]
class HumanMessage:
def __init__(self, content):
self.content = content
class AIMessage:
def __init__(self, content):
self.content = content
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)
# Create AIMessage object after defining response_text
assistant_answer = AIMessage(content=response_text)
st.session_state.sessionMessages.append(assistant_answer)
return response_text
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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