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import torch
from transformers import pipeline
import streamlit as st
import os
token = os.environ.get('HF_TOKEN')
model_id = "google/gemma-2-2b-it"
welcome_message = f"Hello there 👋! Is there anything I can help you with?"
@st.cache_resource
def model_setup(model_id):
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="cpu",
token=token,
)
return pipe
def runModel(prompt):
messages = [{"role": "user", "content": "You are a helpful assistant who politely answers user's questions."+prompt}]
outputs = pipe(
messages,
max_new_tokens=4096,
)
return outputs[0]["generated_text"][-1]["content"]
### load model
pipe = model_setup(model_id)
### initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
st.session_state.messages.append({"role": "assistant", "content": welcome_message})
### display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
### accept user input
if prompt := st.chat_input("Type here!",key="question"):
# display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# run model
response = runModel(prompt)
# display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
# add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
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