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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer
@st.cache_resource
def load_model():
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-chat-hf",
device_map="auto",
torch_dtype="auto"
)
return model, tokenizer
# Initialize
st.title("LLaMA-2 Chatbot")
st.sidebar.title("Configuration")
# Input parameters
max_tokens = st.sidebar.slider("Max Tokens", 50, 1000, 256)
temperature = st.sidebar.slider("Temperature", 0.1, 1.5, 0.7)
model, tokenizer = load_model()
# Chat interface
if "messages" not in st.session_state:
st.session_state.messages = []
user_input = st.text_input("Your message:", key="input")
if st.button("Send"):
if user_input:
st.session_state.messages.append({"role": "user", "content": user_input})
inputs = tokenizer(
user_input, return_tensors="pt", truncation=True
).to(model.device)
outputs = model.generate(
inputs.input_ids,
max_length=max_tokens,
temperature=temperature,
do_sample=True,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.session_state.messages.append({"role": "assistant", "content": response})
# Display the conversation
for message in st.session_state.messages:
role = "User" if message["role"] == "user" else "Assistant"
st.write(f"**{role}:** {message['content']}")
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