app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the Phi 2 model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    "microsoft/phi-2",
    trust_remote_code=True
)

model = AutoModelForCausalLM.from_pretrained(
    "arieridwans/phi_2-finetuned-lyrics",
    device_map="auto",
    trust_remote_code=True
)

# Streamlit UI
st.title("Eleanor Rigby")

# User input prompt
user_prompt = st.text_area("Enter your prompt that can be song lyrics:", """Yesterday, I saw you in my dream""")

# Generate output based on user input
if st.button("Generate Output"):
    instruct_prompt = "Instruct:You are a song writer and your main reference is The Beatles. Write a song lyrics by completing these words:"
    output_prompt = "Output:"
    input = inference_tokenizer(""" {0}{1}\n{2} """.format(instruct_prompt, user_prompt, output_prompt),
                     return_tensors="pt",
                     return_attention_mask=False,
                     padding=True,
                     truncation=True)
    result = inference_model.generate(**input, repetition_penalty=1.2, max_length=1024)
    output = inference_tokenizer.batch_decode(result, skip_special_tokens=True)[0]
    st.text("Generated Result:")
    st.write(output)