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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AIDC-AI/Marco-o1")
model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Marco-o1")
# Streamlit UI
st.title("ChatGPT Clone")
st.write("A simple chatbot interface using SmolLM2-1.7B-Instruct")
# Conversation history
if "history" not in st.session_state:
st.session_state.history = []
# User input
user_input = st.text_input("You:", key="input")
if st.button("Send") and user_input:
# Add user input to the history
st.session_state.history.append({"role": "user", "content": user_input})
# Tokenize input and generate a response
inputs = tokenizer(user_input, return_tensors="pt", padding=True, truncation=True)
outputs = model.generate(**inputs, max_length=150, do_sample=True, top_p=0.95, top_k=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Add response to the history
st.session_state.history.append({"role": "bot", "content": response})
# Display conversation history
for message in st.session_state.history:
role = "User" if message["role"] == "user" else "Bot"
st.markdown(f"**{role}:** {message['content']}")
# Reset chat button
if st.button("Reset Chat"):
st.session_state.history = []
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