import os
from transformers import pipeline, AutoTokenizer, AutoModelForTextGeneration

# Retrieve the API token from environment variables
api_token = os.getenv('hugging_face_api_token')

if not api_token:
    raise ValueError("Hugging Face API token not found. Please set the HUGGING_FACE_API_TOKEN environment variable.")

# Configure the use of the token for Hugging Face operations
from huggingface_hub import HfFolder
HfFolder.save_token(api_token)

# Now you can securely use the model
model_name = "google/gemma-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTextGeneration.from_pretrained(model_name)

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)





# Streamlit app title and description
st.title("Gemma Text Generation App")
st.write("This app generates text based on the input prompt using the Gemma model.")

# Text input for user prompt
prompt = st.text_input("Enter your prompt:", "Once upon a time,")

# User controls for output length and creativity
max_length = st.slider("Select the maximum output length:", min_value=50, max_value=500, value=100)
temperature = st.slider("Adjust the creativity level (temperature):", min_value=0.1, max_value=1.0, value=0.7)

# Generate button to trigger text generation
if st.button("Generate Text"):
    with st.spinner('Generating text...'):
        try:
            generated_text = generator(prompt, max_length=max_length, temperature=temperature)[0]['generated_text']
        except Exception as e:
            st.error(f"Error generating text: {str(e)}")
        else:
            st.success('Text generation complete!')
            st.markdown("### Generated Text:")
            st.markdown(generated_text)

# About section
with st.expander("About"):
    st.write("""
        The Gemma Text Generation app uses the powerful Gemma-7b model from Google to generate text. 
        Adjust the sliders to change the length and creativity of the output.
    """)