import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

@st.cache_resource
def load_model():
    model_name = "Salesforce/codet5-small"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
    return tokenizer, model

# Load the model and tokenizer (cached)
with st.spinner("Loading model..."):
    tokenizer, model = load_model()

# Streamlit UI
st.title("Code Generator with Hugging Face")
st.write("Generate code snippets from natural language prompts!")

prompt = st.text_area("Enter your coding task:", placeholder="Write a Python function to calculate factorial.")
max_length = st.slider("Select maximum length of generated code:", min_value=20, max_value=200, value=50, step=10)

if st.button("Generate Code"):
    if prompt.strip():
        with st.spinner("Generating code..."):
            inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
            outputs = model.generate(inputs.input_ids, max_length=max_length, num_beams=4, early_stopping=True)
            generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
            st.text_area("Generated Code:", generated_code, height=200)
    else:
        st.warning("Please enter a prompt!")