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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
st.set_page_config(page_title="Chat with Qwen2.5-Omni-7B", layout="centered")
st.title("Chat with Qwen2.5-Omni-7B")
# Model name
model_name = "Qwen/Qwen2.5-Omni-7B"
# Prompt input
system_prompt = st.text_area("System Prompt", "You are a helpful assistant.", height=100)
user_input = st.text_input("Your Message", "")
# Temp & token sliders
temperature = st.slider("Temperature", 0.0, 1.0, 0.7)
max_tokens = st.slider("Max Tokens", 16, 1024, 256)
# Optional: Hugging Face token field (left empty for user)
hf_token = st.text_input("Hugging Face Token (optional)", type="password")
# Load model pipeline
@st.cache_resource
def load_pipeline():
return pipeline(
"text-generation",
model=model_name,
tokenizer=model_name,
use_auth_token=hf_token if hf_token else None,
device_map="auto"
)
if user_input:
pipe = load_pipeline()
prompt = f"{system_prompt}\nUser: {user_input}\nAssistant:"
response = pipe(prompt, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
st.markdown("**Response:**")
st.write(response.replace(prompt, ""))
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