app.py

# from transformers import AutoTokenizer, AutoModelForCausalLM
# from peft import PeftModel
# import accelerate
import streamlit as st

st.set_page_config(page_title = "Q&A Demo")
st.header("Langchain Application")

# model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0").cuda()
# model = PeftModel.from_pretrained(model, "shionhonda/tiny-llama-reviewer2-1.1B-dpo-lora")
# tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")

title = st.text_input("Title : ")
# prompt = tokenizer.apply_chat_template([
#         {"role": "system", "content": "You are an experienced researcher and a reviewer of scientific papers. Given a title of the paper, write a review about it in one sentence."},
#         {"role": "user", "content": title}
#     ], tokenize=False, add_generation_prompt=True)
# inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
# generate_ids = model.generate(inputs.input_ids, max_new_tokens=50, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)

submit = st.button("Submit")

if submit:
    st.subheader("Reviewer #2:")
    # st.write(tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0])
    st.write("test")