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import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Initialize model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3.5-mini-instruct",
device_map="cpu",
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")
# Create pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer
)
# Generation arguments
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
# Chat function
def chat(message, history, system_prompt):
# Prepare messages
messages = [
{"role": "system", "content": system_prompt},
]
# Add history to messages
for human, assistant in history:
messages.append({"role": "user", "content": human})
messages.append({"role": "assistant", "content": assistant})
# Add current message
messages.append({"role": "user", "content": message})
# Generate response
output = pipe(messages, **generation_args)
response = output[0]['generated_text']
return response
# Streamlit App Layout
st.title("Testing new Phi 3.5 model")
st.sidebar.title("Settings")
system_prompt = st.sidebar.text_input("System Prompt", value="You are a helpful AI assistant.")
# Initialize chat history
if "chat_history" not in st.session_state:
st.session_state["chat_history"] = []
# Input text box
user_input = st.text_input("You:", key="input")
# Chat history display
if st.session_state["chat_history"]:
for user_msg, bot_msg in st.session_state["chat_history"]:
st.write(f"**You:** {user_msg}")
st.write(f"**Bot:** {bot_msg}")
# On submit
if user_input:
bot_response = chat(user_input, st.session_state["chat_history"], system_prompt)
st.session_state["chat_history"].append((user_input, bot_response))
#st.experimental_rerun()
# Clear chat history button
if st.button("Clear Chat"):
st.session_state["chat_history"] = []
#st.experimental_rerun()
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