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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
@st.cache_resource
def load_model():
model_name = "Salesforce/codet5-base" # Switch to 'codet5-base' for better results
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
return tokenizer, model
# Load the model and tokenizer
tokenizer, model = load_model()
st.title("Code Generator")
st.write("Generate code snippets from natural language prompts using CodeT5!")
prompt = st.text_area("Enter your coding task:", placeholder="Write a Python function to calculate factorial.")
max_length = st.slider("Maximum length of generated code:", 20, 300, 100)
if st.button("Generate Code"):
if prompt.strip():
with st.spinner("Generating code..."):
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
# Use sampling-based generation for better quality
outputs = model.generate(
inputs.input_ids,
max_length=max_length,
temperature=0.7,
top_p=0.95,
do_sample=True,
)
# Debugging: Show raw token output
st.write("### Debugging: Raw Model Output")
st.json(outputs.tolist())
# Decode tokens properly
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
st.write("### Generated Code:")
st.code(generated_code, language="python")
else:
st.warning("Please enter a prompt!")
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