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from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
import streamlit as st
import re
model_id = "google/gemma-1.1-2b-it"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
#device_map="cpu",
torch_dtype=dtype,
)
st.title("💬 Chatbot")
st.caption("🚀 A streamlit chatbot powered by Google's Gemma")
# Initialize chat history
if 'messages' not in st.session_state:
st.session_state['messages'] = [] #[{"role": "assistant", "content": "How can I help you?"}]
# Display chat messages from history on app rerun
for messasge in st.session_state.messages:
st.chat_message(messasge["role"]).write(messasge["content"])
# React to user input
if prompt := st.chat_input():
# Display user message in chat message container
st.chat_message("user").write(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
messages=st.session_state.messages
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
##Get response to the message using client
inputs = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=150)
msg = tokenizer.decode(outputs[0]) #output[0]['generated_text']
msg = re.sub(r'<.*?>', '', msg)
# Display assistant response in chat message container
st.chat_message("assistant").write(msg)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": msg}) |