File size: 3,553 Bytes
c9e8e4a a16fa71 c9e8e4a 3bce3fb a16fa71 c9e8e4a 1e8c169 dc737c0 7982bc6 a16fa71 7982bc6 9be3f4c fc158ea c9e8e4a 7cf1a13 33c3beb 3bce3fb 7982bc6 a16fa71 9be3f4c f25abd8 3e9df94 3bce3fb c9e8e4a 3bce3fb a16fa71 f25abd8 5b9eb09 f25abd8 7cf1a13 a16fa71 22fef42 a16fa71 7cf1a13 a16fa71 7cf1a13 3283b93 a16fa71 7cf1a13 a16fa71 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
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 = [" ", "Model evaluation", "Pretraining datasets", "Model architecture", "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)
|