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import streamlit as st
from typing import Generator
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
st.set_page_config(
page_icon="💬",
page_title="Chat App",
layout="wide",
)
model_name = "JuliaTsk/SuccinctLabs-chat-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cpu", low_cpu_mem_usage=True)
st.title("ChatGPT-like clone 🎈")
def generate_chat_responses(chat_completion) -> Generator[str, None, None]:
for chunk in chat_completion:
if chunk.choices[0].delta.content:
yield chunk.choices[0].delta.content
left, right = st.columns([2, 6], vertical_alignment="top")
max_tokens_range = 32768
max_tokens = left.slider(
label="Max Tokens:",
min_value=128,
max_value=max_tokens_range,
# Default value or max allowed if less
value=min(1024, max_tokens_range),
step=128,
help=f"Adjust the maximum number of tokens (words) for the model's response."
)
temperature = left.slider(
label="Temperature:",
min_value=0.0,
max_value=1.0,
value=0.7,
step=0.01,
help=f"Controls randomness: a low value means less random responses."
)
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
avatar = '🤖' if message["role"] == "assistant" else '👨💻'
with right.chat_message(message["role"], avatar=avatar):
right.markdown(message["content"])
prompt = st.chat_input("Say something")
if prompt:
with right.chat_message("user", avatar='👨💻'):
right.markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
with right.chat_message("assistant"):
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=100, num_return_sequences=1)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.session_state.messages.append({"role": "assistant", "content": generated_text})
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