|
import streamlit as st |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
from transformers import pipeline |
|
import torch |
|
import json |
|
import pandas as pd |
|
import requests |
|
|
|
@st.cache(allow_output_mutation=True) |
|
def load_tokenizer(model_ckpt): |
|
return AutoTokenizer.from_pretrained(model_ckpt) |
|
|
|
@st.cache(allow_output_mutation=True) |
|
def load_model(model_ckpt): |
|
model = AutoModelForCausalLM.from_pretrained(model_ckpt, low_cpu_mem_usage=True) |
|
return model |
|
|
|
@st.cache() |
|
def load_examples(): |
|
with open("utils/examples.json", "r") as f: |
|
examples = json.load(f) |
|
return examples |
|
|
|
st.set_page_config(page_icon=":laptop:", layout="wide") |
|
|
|
st.sidebar.header("Models") |
|
models = ["CodeParrot", "InCoder"] |
|
selected_models = st.sidebar.multiselect("Select code generation models to compare", models, default=["CodeParrot"]) |
|
|
|
st.sidebar.header("Tasks") |
|
tasks = [" ", "Pretraining datasets", "Model architecture", "Model evaluation", "Code generation"] |
|
selected_task = st.sidebar.selectbox("Select a task", tasks) |
|
|
|
|
|
if selected_task == " ": |
|
st.title("Code Generation Models") |
|
with open("utils/intro.txt", "r") as f: |
|
intro = f.read() |
|
st.markdown(intro) |
|
|
|
elif selected_task == "Pretraining datasets": |
|
st.title("Pretraining datasets 📚") |
|
st.markdown("Preview of some code files from Github repositories") |
|
df = pd.read_csv("utils/data_preview.csv") |
|
st.dataframe(df) |
|
for model in selected_models: |
|
with open(f"datasets/{model.lower()}.txt", "r") as f: |
|
text = f.read() |
|
st.markdown(f"### {model}") |
|
st.markdown(text) |
|
|
|
elif selected_task == "Model architecture": |
|
st.title("Model architecture 🔨") |
|
for model in selected_models: |
|
with open(f"architectures/{model.lower()}.txt", "r") as f: |
|
text = f.read() |
|
st.markdown(f"## {model}") |
|
st.markdown(text) |
|
if model == "InCoder": |
|
st.image("https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/incoder.png", caption="Figure 1: InCoder training", width=700) |
|
|
|
elif selected_task == "Model evaluation": |
|
st.title("Code models evaluation 📊") |
|
with open("evaluation/intro.txt", "r") as f: |
|
intro = f.read() |
|
st.markdown(intro) |
|
|
|
elif selected_task == "Code generation": |
|
st.title("Code generation 💻") |
|
st.sidebar.header("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.sidebar.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.sidebar.header("Generation settings") |
|
temperature = st.sidebar.slider("Temperature:", value=0.2, min_value=0.0, step=0.1, max_value=2.0) |
|
max_new_tokens = st.sidebar.slider("Number of tokens to generate:", value=default_length, min_value=8, step=8, max_value=256) |
|
seed = st.sidebar.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=220,).strip() |
|
if st.button("Generate code!"): |
|
with st.spinner("Generating code..."): |
|
for model in selected_models: |
|
url = f'https://hf.space/embed/loubnabnl/{model.lower()}-subspace/+/api/predict/' |
|
r = requests.post(url=url, json={"data": [gen_prompt, max_new_tokens, temperature, seed]}) |
|
generated_text = r.json()['data'][0] |
|
st.markdown(f"{model}") |
|
st.code(generated_text) |
|
|
