import streamlit as st import torch from transformers import AutoTokenizer, AutoModelForCausalLM # ============================== # 🔐 HIDE STREAMLIT MENU # ============================== st.markdown(""" """, unsafe_allow_html=True) # ============================== # PAGE CONFIG # ============================== st.set_page_config(page_title="💻 AI Code Generator", layout="wide") # ============================== # LOAD MODEL (OPTIMIZED) # ============================== @st.cache_resource def load_model(): model_name = "google/codegemma-2b" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float32, # CPU safe device_map="cpu" # force CPU (faster on Spaces) ) return tokenizer, model tokenizer, model = load_model() # ============================== # CODE GENERATION FUNCTION # ============================== def generate_code(prompt, language): full_prompt = f""" You are an expert {language} developer. Write clean, optimized, production-ready code. Task: {prompt} Rules: - Only return code - No explanation """ inputs = tokenizer(full_prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=150, # reduced for speed temperature=0.1, top_p=0.85, do_sample=True ) result = tokenizer.decode(outputs[0], skip_special_tokens=True) return result.replace(full_prompt, "").strip() # ============================== # UI # ============================== st.title("💻 AI Code Generator") col1, col2 = st.columns(2) with col1: user_prompt = st.text_area("Describe your task", height=200) with col2: language = st.selectbox( "Select Programming Language", ["Python", "JavaScript", "SQL", "Java", "C++", "HTML", "CSS"] ) if st.button("Generate Code"): if not user_prompt.strip(): st.warning("Please enter a task") else: with st.spinner("⚡ Generating fast code..."): code = generate_code(user_prompt, language) st.success("✅ Generated Code") st.code(code, language=language.lower())