import json import os import pandas as pd import requests import threading import streamlit as st from datasets import load_dataset, load_metric 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 load_evaluation(): # load task 2 of HumanEval and code_eval_metric os.environ["HF_ALLOW_CODE_EVAL"] = "1" human_eval = load_dataset("openai_humaneval") entry_point = f"check({human_eval['test'][2]['entry_point']})" test_func = "\n" + human_eval["test"][2]["test"] + "\n" + entry_point code_eval = load_metric("code_eval") return code_eval, test_func 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/codeparrot/{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({model_name: 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.cache(show_spinner=False) def generate_teaser(gen_prompt): generations = [] generate_code(generations, "CodeParrot", gen_prompt, 8, 0.2, 42) return generations[0]["CodeParrot"] st.set_page_config(page_icon=":laptop:", layout="wide") # Introduction st.title("Genera codice onlinešŸ¤—") # Code generation col1, col2, col3 = st.columns([7, 1, 6]) with col1: st.markdown("**Modelli disponibli**") selected_models = st.multiselect( "Seleziona uno o piĆ¹ modelli pe generare del codice:", GENERATION_MODELS, default=GENERATION_MODELS, key=3, ) st.markdown(" ") st.markdown("**Esempi**") 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( "Seleziona un esempio per prendere spunto:", example_names ) example_text = examples[name2id[selected_example]]["value"] default_length = examples[name2id[selected_example]]["length"] with col3: st.markdown("**Impostazioni**") temperature = st.slider( "Temperature:", value=0.2, min_value=0.1, step=0.1, max_value=2.0 ) max_new_tokens = st.slider( "Token da generare:", 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( "Istruzioni per generare il codice:", value=example_text, height=200, ).strip() if st.button("Genera il codice e risparmi tempo", key=4): with st.spinner("Dammi un minuto, sto rubando un programmatore..."): # 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)): st.markdown(f"**{selected_models[i]}**") for j in range(len(generations)): if selected_models[i] in generations[j].keys(): st.code(generations[j][selected_models[i]]) if len(generations) < len(selected_models): st.markdown("Avviso: alcuni modelli vanno in timeout, prova un'altra volta o riduci il numero di token da generare.", unsafe_allow_html=True)