import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# device = "cpu"
# if torch.cuda.is_available():
#     device = "cuda"
# if torch.backends.mps.is_available():
#     device = "mps"

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

model = AutoModelForCausalLM.from_pretrained(
    "microsoft/phi-2",
    device_map="cpu",
    trust_remote_code=True,
    # offload_folder="offload",
    torch_dtype=torch.float32
    # torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
)

# Streamlit UI
st.title("Microsoft Phi 2 Streamlit App")

# User input prompt
prompt = st.text_area("Enter your prompt:", """Write a short summary about how to create a healthy lifestyle.""")

# Generate output based on user input
if st.button("Generate Output"):
    with torch.no_grad():
        token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt",  
                                     # return_attention_mask=False
                                    )
        output_ids = model.generate(
            token_ids.to(model.device),
            max_new_tokens=512,
            do_sample=True,
            temperature=0.3,
            # max_length=200
        )

    output = tokenizer.decode(output_ids[0][token_ids.size(1):])
    st.text("Generated Output:")
    st.write(output)