| | 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("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("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("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) |
| | |
| | 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", 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) |
| |
|
