app.py

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) 
        if model == "InCoder":
            st.image("https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/incoder.png", 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", 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)