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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 comparison")
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("preview-github-data.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)
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