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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 online🤗")
# Code generation
col1, col2, col3 = st.columns([7, 1, 6])
with col1:
st.markdown("**Modelli disponibli**")
selected_models = st.multiselect(
"Seleziona uno o più 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("<span style='color:red'>Avviso: alcuni modelli vanno in timeout, prova un'altra volta o riduci il numero di token da generare.", unsafe_allow_html=True)
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