import json import pandas as pd import requests from multiprocessing import Pool from functools import partial import streamlit as st GITHUB_CODE = "https://huggingface.co/datasets/lvwerra/github-code" INCODER_IMG = ( "https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/incoder.png" ) MODELS = ["CodeParrot", "InCoder"] @st.cache() def load_examples(): with open("utils/examples.json", "r") as f: examples = json.load(f) return examples def generate_code(model_name, gen_prompt, max_new_tokens, temperature, seed): url = ( f"https://hf.space/embed/loubnabnl/{model_name.lower()}-subspace/+/api/predict/" ) r = requests.post( url=url, json={"data": [gen_prompt, max_new_tokens, temperature, seed]} ) generated_text = r.json()["data"][0] return generated_text #st.set_page_config(page_icon=":laptop:", layout="wide") # Introduction st.title("Code generation with 🤗") with open("utils/intro.txt", "r") as f: intro = f.read() st.markdown(intro) # Pretraining datasets st.title("1 - Pretraining datasets 📚") st.markdown( f"Preview of some code files from Github repositories in [Github-code dataset]({GITHUB_CODE}):" ) df = pd.read_csv("utils/data_preview.csv") st.dataframe(df) st.header("Model") col1, col2= st.columns([1,2]) with col1: selected_model = st.selectbox( "Select a code generation model", MODELS, key=1 ) with open(f"datasets/{selected_model.lower()}.txt", "r") as f: text = f.read() st.markdown(text) # Model architecture st.title("2 - Model architecture") st.markdown("Most code generation models use GPT style architectures trained on code. Some use encoder-decoder architectures such as AlphaCode.") st.header("Model") col1, col2= st.columns([1,2]) with col1: selected_model = st.selectbox( "Select a code generation model", MODELS, key=2 ) with open(f"architectures/{selected_model.lower()}.txt", "r") as f: text = f.read() st.markdown(text) if selected_model == "InCoder": st.image(INCODER_IMG, caption="Figure 1: InCoder training", width=700) # Model evaluation st.title("3 - Code models evaluation 📊") with open("evaluation/intro.txt", "r") as f: intro = f.read() st.markdown(intro) # Code generation st.title("4 - Code generation 💻") col1, col2 = st.columns(2) with col1: st.subheader("Models") selected_models = st.multiselect( "Select code generation models to compare", MODELS, default=["CodeParrot"], key=3 ) st.subheader("Examples") examples = load_examples() example_names = [example["name"] for example in examples] name2id = dict([(name, i) for i, name in enumerate(example_names)]) selected_example = st.selectbox( "Select one of the following examples or implement yours", example_names ) example_text = examples[name2id[selected_example]]["value"] default_length = examples[name2id[selected_example]]["length"] st.subheader("Generation settings") temperature = st.slider( "Temperature:", value=0.2, min_value=0.0, step=0.1, max_value=2.0 ) max_new_tokens = st.slider( "Number of tokens to generate:", value=default_length, min_value=8, step=8, max_value=256, ) seed = st.slider( "Random seed:", value=42, min_value=0, step=1, max_value=1000 ) with col2: gen_prompt = st.text_area( "Generate code with prompt:", value=example_text, height=220, ).strip() if st.button("Generate code!"): with st.spinner("Generating code..."): # Create a multiprocessing Pool pool = Pool() generate_parallel = partial( generate_code, gen_prompt=gen_prompt, max_new_tokens=max_new_tokens, temperature=temperature, seed=seed, ) output = pool.map(generate_parallel, selected_models) for i in range(len(output)): st.markdown(f"**{selected_models[i]}**") st.code(output[i])