import json import pandas as pd import requests import threading import streamlit as st MODELS = ["CodeParrot", "InCoder", "CodeGen", "PolyCoder"] GENERATION_MODELS = ["CodeParrot", "InCoder", "CodeGen"] @st.cache() def load_examples(): with open("utils/examples.json", "r") as f: examples = json.load(f) return examples def read_markdown(path): with open(path, "r") as f: output = f.read() st.markdown(output, unsafe_allow_html=True) def generate_code( generations, model_name, gen_prompt, max_new_tokens, temperature, seed ): # call space using its API endpoint 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] generations.append(generated_text) def generate_code_threads( generations, models, gen_prompt, max_new_tokens, temperature, seed ): threads = [] for model_name in models: # create the thread threads.append( threading.Thread( target=generate_code, args=( generations, model_name, gen_prompt, max_new_tokens, temperature, seed, ), ) ) threads[-1].start() for t in threads: t.join() 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 🤗") read_markdown("utils/intro.txt") # Pretraining datasets st.subheader("1 - Code datasets") read_markdown("datasets/intro.txt") read_markdown("datasets/github_code.txt") 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") # Model evaluation st.subheader("3 - Code models evaluation") read_markdown("evaluation/intro.txt") 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=4, 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..."): # use threading generations = [] generate_code_threads( generations, selected_models, gen_prompt=gen_prompt, max_new_tokens=max_new_tokens, temperature=temperature, seed=seed, ) for i in range(len(generations)): print(generations[i]) for i in range(len(generations)): st.markdown(f"**{selected_models[i]}**") st.code(generations[i]) # Resources st.subheader("Resources") read_markdown("utils/resources.txt")