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| import json | |
| import pandas as pd | |
| import requests | |
| from multiprocessing import Pool | |
| from functools import partial | |
| import streamlit as st | |
| GITHUB_CODE = "https://huggingface.co./datasets/lvwerra/github-code" | |
| INCODER_IMG = "https://huggingface.co./datasets/loubnabnl/repo-images/raw/main/incoder.png" | |
| def load_examples(): | |
| with open("utils/examples.json", "r") as f: | |
| examples = json.load(f) | |
| return examples | |
| def generate_code(model_name, gen_prompt, max_new_tokens, temperature, seed): | |
| 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] | |
| return generated_text | |
| 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(f"Preview of some code files from Github repositories in [Github-code dataset]({GITHUB_CODE}):") | |
| 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(INCODER_IMG, 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..."): | |
| # Create a multiprocessing Pool | |
| pool = Pool() | |
| generate_parallel=partial(generate_code, | |
| gen_prompt=gen_prompt, | |
| max_new_tokens=max_new_tokens, | |
| temperature=temperature, | |
| seed=seed) | |
| output = pool.map(generate_parallel, selected_models) | |
| for i in range(len(output)): | |
| st.markdown(selected_models[i]) | |
| st.code(output[i]) |