import streamlit as st
from transformers import pipeline

# Load the Hugging Face model for text generation
model = pipeline("text-generation")

# Streamlit app
def main():
    st.title("Cherry Bot")
    st.markdown("*Your Sweetest Companion*")

    # Maintain a list to store text inputs for each chat
    chat_inputs = []

    # Add a sidebar to the app
    with st.sidebar:
        st.markdown("# Text Generation Settings")
        # Slider for adjusting the maximum length of generated text
        max_length = st.slider("Max Length of Generated Text:", min_value=10, max_value=200, value=50, step=10)

        # Add a "New Chat" button
        if st.button("New Chat"):
            chat_inputs.append(st.text_area(f"Enter starting text for Chat {len(chat_inputs) + 1}:", height=100))

    # Perform text generation for each chat
    for idx, starting_text in enumerate(chat_inputs):
        st.header(f"Chat {idx + 1}")
        if st.button(f"Generate Text for Chat {idx + 1}"):
            if starting_text:
                # Generate text using the loaded model
                generated_text = model(starting_text, max_length=max_length)[0]['generated_text']

                # Display the generated text
                st.write("Generated Text:")
                st.write(generated_text)

if __name__ == "__main__":
    main()