import streamlit as st from transformers import AutoModelForCausalLM, AutoTokenizer # Load the model and tokenizer @st.cache_resource def load_model(): model_name = "replit/replit-code-v1-3b" # Replace with your fine-tuned model if applicable tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True) return model, tokenizer model, tokenizer = load_model() # App title and description st.title("Replit-code-v1-3b Code Assistant 📊") st.markdown(""" This application allows you to interact with the **Replit-code-v1-3b** large language model. You can use it to generate, debug, or optimize code snippets. Simply provide a prompt, and the model will respond with suggestions! """) # User input st.header("Enter Your Prompt") prompt = st.text_area("Describe your coding task or paste your code for debugging:") # Temperature and max length controls st.sidebar.header("Model Settings") temperature = st.sidebar.slider("Temperature (Creativity)", 0.0, 1.0, 0.7) max_length = st.sidebar.slider("Max Response Length", 50, 500, 200) # Submit button if st.button("Generate Response"): if prompt.strip(): with st.spinner("Generating response..."): # Tokenize and generate response inputs = tokenizer(prompt, return_tensors="pt", truncation=True) outputs = model.generate( inputs.input_ids, max_length=max_length, temperature=temperature, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) # Display response st.subheader("Generated Code/Response") st.code(response, language="python") else: st.warning("Please enter a prompt to generate a response.") # Footer st.markdown("---") st.markdown(""" **Replit-code-v1-3b Code Assistant** Built with [Streamlit](https://streamlit.io/) and the [Hugging Face Transformers Library](https://huggingface.co/docs/transformers). """)