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import json |
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import pandas as pd |
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import requests |
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from multiprocessing import Pool |
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from functools import partial |
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import streamlit as st |
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GITHUB_CODE = "https://huggingface.co/datasets/lvwerra/github-code" |
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INCODER_IMG = ( |
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"https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/incoder.png" |
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) |
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@st.cache() |
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def load_examples(): |
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with open("utils/examples.json", "r") as f: |
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examples = json.load(f) |
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return examples |
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def generate_code(model_name, gen_prompt, max_new_tokens, temperature, seed): |
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url = ( |
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f"https://hf.space/embed/loubnabnl/{model_name.lower()}-subspace/+/api/predict/" |
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) |
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r = requests.post( |
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url=url, json={"data": [gen_prompt, max_new_tokens, temperature, seed]} |
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) |
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generated_text = r.json()["data"][0] |
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return generated_text |
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st.set_page_config(page_icon=":laptop:", layout="wide") |
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st.sidebar.header("Models") |
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models = ["CodeParrot", "InCoder"] |
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selected_models = st.sidebar.multiselect( |
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"Select code generation models to compare", models, default=["CodeParrot"] |
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) |
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st.sidebar.header("Tasks") |
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tasks = [ |
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" ", |
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"Pretraining datasets", |
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"Model architecture", |
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"Model evaluation", |
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"Code generation", |
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] |
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selected_task = st.sidebar.selectbox("Select a task", tasks) |
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if selected_task == " ": |
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st.title("Code Generation Models") |
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with open("utils/intro.txt", "r") as f: |
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intro = f.read() |
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st.markdown(intro) |
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elif selected_task == "Pretraining datasets": |
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st.title("Pretraining datasets 📚") |
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st.markdown( |
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f"Preview of some code files from Github repositories in [Github-code dataset]({GITHUB_CODE}):" |
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) |
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df = pd.read_csv("utils/data_preview.csv") |
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st.dataframe(df) |
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for model in selected_models: |
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with open(f"datasets/{model.lower()}.txt", "r") as f: |
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text = f.read() |
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st.markdown(f"### {model}") |
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st.markdown(text) |
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elif selected_task == "Model architecture": |
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st.title("Model architecture") |
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for model in selected_models: |
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with open(f"architectures/{model.lower()}.txt", "r") as f: |
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text = f.read() |
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st.markdown(f"## {model}") |
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st.markdown(text) |
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if model == "InCoder": |
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st.image(INCODER_IMG, caption="Figure 1: InCoder training", width=700) |
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elif selected_task == "Model evaluation": |
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st.title("Code models evaluation 📊") |
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with open("evaluation/intro.txt", "r") as f: |
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intro = f.read() |
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st.markdown(intro) |
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elif selected_task == "Code generation": |
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st.title("Code generation 💻") |
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st.sidebar.header("Examples") |
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examples = load_examples() |
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example_names = [example["name"] for example in examples] |
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name2id = dict([(name, i) for i, name in enumerate(example_names)]) |
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selected_example = st.sidebar.selectbox( |
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"Select one of the following examples or implement yours", example_names |
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) |
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example_text = examples[name2id[selected_example]]["value"] |
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default_length = examples[name2id[selected_example]]["length"] |
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st.sidebar.header("Generation settings") |
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temperature = st.sidebar.slider( |
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"Temperature:", value=0.2, min_value=0.0, step=0.1, max_value=2.0 |
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) |
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max_new_tokens = st.sidebar.slider( |
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"Number of tokens to generate:", |
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value=default_length, |
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min_value=8, |
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step=8, |
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max_value=256, |
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) |
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seed = st.sidebar.slider( |
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"Random seed:", value=42, min_value=0, step=1, max_value=1000 |
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) |
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gen_prompt = st.text_area( |
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"Generate code with prompt:", |
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value=example_text, |
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height=220, |
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).strip() |
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if st.button("Generate code!"): |
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with st.spinner("Generating code..."): |
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pool = Pool() |
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generate_parallel = partial( |
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generate_code, |
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gen_prompt=gen_prompt, |
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max_new_tokens=max_new_tokens, |
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temperature=temperature, |
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seed=seed, |
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) |
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output = pool.map(generate_parallel, selected_models) |
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for i in range(len(output)): |
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st.markdown(f"**{selected_models[i]}**") |
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st.code(output[i]) |
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