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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
st.set_page_config(page_title="Qwen Chatbot", layout="centered")
st.title("🧠 Qwen3-0.6B Chatbot")
@st.cache_resource
def load_model():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", torch_dtype=torch.float32)
return tokenizer, model
tokenizer, model = load_model()
# Chat state
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
user_input = st.text_input("You:", key="input")
if user_input:
# Add to chat history
st.session_state.chat_history.append(("You", user_input))
# Prepare prompt with full context
context = ""
for speaker, msg in st.session_state.chat_history:
context += f"{speaker}: {msg}\n"
context += "Bot:"
inputs = tokenizer(context, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, top_p=0.9, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only bot response (after "Bot:")
bot_msg = response.split("Bot:")[-1].strip()
st.session_state.chat_history.append(("Bot", bot_msg))
# Display conversation
for speaker, msg in st.session_state.chat_history:
st.markdown(f"**{speaker}:** {msg}")
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