import streamlit as st # Don't forget to include `streamlit` in your `requirements.txt`
from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration

# Set up authentication
if "hf_token" not in st.session_state:
    st.title("Authentication Required")
    st.write("Please authenticate with Hugging Face using the following token:")
    hf_token = st.text_input("Enter your token", type="password")

    if hf_token == "":
        st.stop()
else:
    hf_token = st.session_state.hf_token

# Load Token from Storage
if "hf_token_local" not in st.session_state:
    st.title("Load Token from Storage")
    st.write("Please load your token from storage (e.g., environment variable, file)")
    hf_token_local = st.text_input("Enter your token", type="password")

    if hf_token_local == "":
        st.stop()
else:
    hf_token_local = st.session_state.hf_token_local

# Load Processor and Model
if hf_token or hf_token_local:
    processor = PaliGemmaProcessor.from_pretrained(
        "google/paligemma2",
        token=hf_token,
        local_file_dir="/tmp/",
    )
    model = PaliGemmaForConditionalGeneration.from_pretrained(
        "google/paligemma2",
        token=hf_token,
        local_file_dir="/tmp/",
    )

    # Rest of your code
else:
    st.title("No Token Found")
    st.write("Please authenticate with Hugging Face or load token from storage")

# Use the model
def main():
    if "output" not in st.session_state:
        st.write("Model output")
    else:
        st.write(st.session_state.output)

    # Add a button to generate text using the model
    if st.button("Generate Text"):
        input_text = st.text_input("Input text")
        if input_text:
            output = model.generate(input_text, max_length=50)
            st.session_state.output = output

if __name__ == "__main__":
    main()