chmawia/CodeMagic

1import os2import streamlit as st3from transformers import AutoModelForSeq2SeqLM, AutoTokenizer4import torch5import subprocess6 7# Force CPU usage & prevent model download issues8os.environ["HF_HOME"] = "./cache"  # Store model locally9MODEL_NAME = "Salesforce/codegen-350M-mono"  # Updated model10 11tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)12model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)13 14def generate_code(description, language):15    prompt = f"Generate {language} code: {description}"16    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)17    outputs = model.generate(**inputs, max_length=400)18    response = tokenizer.decode(outputs[0], skip_special_tokens=True)19    return response.strip()20 21def execute_code(code, language):22    if language == "Python":23        try:24            result = subprocess.run(['python3', '-c', code], capture_output=True, text=True, timeout=5)25            return result.stdout if result.stdout else result.stderr26        except Exception as e:27            return str(e)28    return "Code execution only supported for Python."29 30# Streamlit UI31st.title("Multi-Language Text-to-Code AI")32st.write("Convert natural language descriptions into code in different programming languages! Run Python code directly in the app.")33 34description = st.text_area("Describe your coding task...")35language = st.selectbox("Select Programming Language", ["Python", "JavaScript", "Java"])36 37if st.button("Generate Code"):38    if description:39        code = generate_code(description, language)40        st.code(code, language=language.lower())41        42        if language == "Python":43            output = execute_code(code, language)44            st.text_area("Execution Output", output, height=150)45    else:46        st.warning("Please enter a description to generate code.")47