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" ) HUMANEVAL_IMG = ( "https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/humaneval_scores.png" ) MODELS = ["CodeParrot", "InCoder", "CodeGen", "PolyCoder"] GENERATION_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 def read_markdown(path): with open(path, "r") as f: output = f.read() st.markdown(output) st.set_page_config(page_icon=":laptop:", layout="wide") with open("utils/table_contents.txt", "r") as f: contents = f.read() st.sidebar.markdown(contents) # Introduction st.title("Code generation with 🤗") with open("utils/intro.txt", "r") as f: intro = f.read() st.markdown(intro) # Pretraining datasets st.subheader("1 - Pretraining datasets") read_markdown("datasets/intro.txt") read_markdown("datasets/github_code.txt") #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) col1, col2= st.columns([1,2]) with col1: selected_model = st.selectbox("", MODELS, key=1) read_markdown(f"datasets/{selected_model.lower()}.txt") # Model architecture st.subheader("2 - Model architecture") read_markdown("architectures/intro.txt") col1, col2= st.columns([1,2]) with col1: selected_model = st.selectbox("", MODELS, key=2) read_markdown(f"architectures/{selected_model.lower()}.txt") if selected_model == "InCoder": st.image(INCODER_IMG, caption="Figure 1: InCoder training", width=700) # Model evaluation st.subheader("3 - Code models evaluation") read_markdown("evaluation/intro.txt") st.image(INCODER_IMG, caption="Table 1: HumanEval scores", width=700) read_markdown("evaluation/demo_humaneval.txt") # Code generation st.subheader("4 - Code generation ✨") col1, col2, col3 = st.columns([7,1,6]) with col1: st.markdown("**Models**") selected_models = st.multiselect( "Select code generation models to compare:", GENERATION_MODELS, default=["CodeParrot"], key=3 ) st.markdown(" ") st.markdown("**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"] with col3: st.markdown("**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 ) gen_prompt = st.text_area( "Generate code with prompt:", value=example_text, height=200, ).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])