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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
from transformers import pipeline
import torch
import json
@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.title("Code Generation Models comparison 💻")
with open("intro.txt", "r") as f:
intro = f.read()
st.markdown(intro)
st.sidebar.header("Models:")
models = ["CodeParrot", "OPT", "InCoder"]
selected_models = st.sidebar.multiselect('Select code generation models to compare',
models,
default=["CodeParrot"])
st.sidebar.header("Tasks:")
tasks = ["Model architecture", "Model evaluation", "Pretraining dataset", "Prompting"]
selected_task = st.sidebar.selectbox("Select a task:", tasks)
architectures = {}
datasets = {}
pipelines = {}
if selected_task == "Pretraining dataset":
st.title("Pretraining datasets 📚")
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 == "Prompting":
for model in selected_models:
if model == "CodeParrot":
tokenizer = load_tokenizer("lvwerra/codeparrot")
model = load_model("lvwerra/codeparrot")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
pipelines[model] = pipe
elif model == "InCoder":
tokenizer = load_tokenizer("facebook/incoder-1B")
model = load_model("facebook/incoder-1B")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
pipelines[model] = pipe
else:
tokenizer = load_tokenizer("facebook/opt-1.3b")
model = load_model("facebook/opt-1.3b")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
pipelines[model] = pipe
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