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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Set Streamlit page configuration
st.set_page_config(
page_title="Qwen2.5-Coder Chat",
page_icon="💬",
layout="wide",
)
# Title of the app
st.title("💬 Qwen2.5-Coder Chat Interface")
# Initialize session state for messages (store conversation history)
if 'messages' not in st.session_state:
st.session_state['messages'] = []
# Load the model and tokenizer
@st.cache_resource
def load_model():
model_name = "Qwen/Qwen2.5-Coder-32B-Instruct" # Replace with the correct model path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
return tokenizer, model
# Load tokenizer and model
with st.spinner("Loading model... This may take a while..."):
tokenizer, model = load_model()
# Function to generate model response
def generate_response(user_input, max_tokens=150, temperature=0.7, top_p=0.9):
# Tokenize the user input
inputs = tokenizer.encode(user_input, return_tensors="pt").to(model.device)
# Generate a response
with torch.no_grad():
outputs = model.generate(
inputs,
max_length=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
num_return_sequences=1
)
# Decode the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Return the response without the input prompt
return response[len(user_input):].strip()
# Layout: Two columns for the main chat and sidebar
chat_col, sidebar_col = st.columns([4, 1])
with chat_col:
# Display chat messages
for message in st.session_state['messages']:
if message['role'] == 'user':
st.markdown(f"**You:** {message['content']}")
else:
st.markdown(f"**Qwen2.5-Coder:** {message['content']}")
# Input area for user message
with st.form(key='chat_form', clear_on_submit=True):
user_input = st.text_area("You:", height=100)
submit_button = st.form_submit_button(label='Send')
if submit_button and user_input:
# Append the user's message to the chat history
st.session_state['messages'].append({'role': 'user', 'content': user_input})
# Generate and append the model's response
with st.spinner("Qwen2.5-Coder is typing..."):
response = generate_response(user_input)
# Append the model's response to the chat history
st.session_state['messages'].append({'role': 'assistant', 'content': response})
# Rerun the app to display new messages
st.experimental_rerun()
with sidebar_col:
st.sidebar.header("Settings")
max_tokens = st.sidebar.slider(
"Maximum Tokens",
min_value=512,
max_value=4096,
value=150,
step=256,
help="Set the maximum number of tokens for the model's response."
)
temperature = st.sidebar.slider(
"Temperature",
min_value=0.1,
max_value=1.0,
value=0.7,
step=0.1,
help="Controls the randomness of the model's output."
)
top_p = st.sidebar.slider(
"Top-p (Nucleus Sampling)",
min_value=0.1,
max_value=1.0,
value=0.9,
step=0.1,
help="Controls the diversity of the model's output."
)
if st.sidebar.button("Clear Chat"):
st.session_state['messages'] = []
st.experimental_rerun()
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