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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
model_name = "Qwen/Qwen2.5-1.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = [
{"role": "system", "content": "You are a helpful assistant."}
]
# 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("Ask me anything about data structures in LeetCode"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Prepare the chat message for the model
messages = st.session_state.messages[-10:] # limit messages to last 10 for performance
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate response from the model
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
# Add bot response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
# Display bot response in chat message container
with st.chat_message("assistant"):
st.markdown(response)
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