app.py

import streamlit as st
from transformers import pipeline

# Load the code explainer pipeline
@st.cache_resource
def load_model():
    return pipeline("text2text-generation", model="philschmid/code-explainer", device=-1)

explainer = load_model()

# Streamlit UI
st.title("🧠 Code Explainer (Hugging Face)")

st.markdown("Paste any code snippet below (Python, JavaScript, etc.) and get a plain-English explanation using a Hugging Face model.")

code_input = st.text_area("📝 Paste your code here:", height=200)

if st.button("Explain Code"):
    if code_input.strip() == "":
        st.warning("Please paste some code to explain.")
    else:
        with st.spinner("Explaining your code..."):
            result = explainer(f"Explain this code: {code_input}")
            explanation = result[0]['generated_text']
            st.success("✅ Explanation:")
            st.write(explanation)