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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!")
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